{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview \n", "\n", "In the 10x series of notebooks, we will look at Time Series modeling in pycaret using univariate data and no exogenous variables. We will use the famous airline dataset for illustration. Our plan of action is as follows:\n", "\n", "1. Perform EDA on the dataset to extract valuable insight about the process generating the time series. **(COMPLETED)**\n", "2. Model the dataset based on exploratory analysis (univariable model without exogenous variables). **(COMPLETED)**\n", "3. Use an automated approach (AutoML) to improve the performance. **(COMPLETED)**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Only enable critical logging (Optional)\n", "import os\n", "os.environ[\"PYCARET_CUSTOM_LOGGING_LEVEL\"] = \"CRITICAL\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "System:\n", " python: 3.9.16 (main, Jan 11 2023, 16:16:36) [MSC v.1916 64 bit (AMD64)]\n", "executable: C:\\Users\\Nikhil\\.conda\\envs\\pycaret_dev_sktime_16p1\\python.exe\n", " machine: Windows-10-10.0.19044-SP0\n", "\n", "PyCaret required dependencies:\n", " pip: 22.3.1\n", " setuptools: 65.6.3\n", " pycaret: 3.0.0rc9\n", " IPython: 8.10.0\n", " ipywidgets: 8.0.4\n", " tqdm: 4.64.1\n", " numpy: 1.23.5\n", " pandas: 1.5.3\n", " jinja2: 3.1.2\n", " scipy: 1.10.0\n", " joblib: 1.2.0\n", " sklearn: 1.2.1\n", " pyod: 1.0.8\n", " imblearn: 0.10.1\n", " category_encoders: 2.6.0\n", " lightgbm: 3.3.5\n", " numba: 0.56.4\n", " requests: 2.28.2\n", " matplotlib: 3.7.0\n", " scikitplot: 0.3.7\n", " yellowbrick: 1.5\n", " plotly: 5.13.0\n", " kaleido: 0.2.1\n", " statsmodels: 0.13.5\n", " sktime: 0.16.1\n", " tbats: 1.1.2\n", " pmdarima: 2.0.2\n", " psutil: 5.9.4\n", "\n", "PyCaret optional dependencies:\n", " shap: 0.41.0\n", " interpret: Not installed\n", " umap: Not installed\n", " pandas_profiling: Not installed\n", " explainerdashboard: Not installed\n", " autoviz: Not installed\n", " fairlearn: Not installed\n", " xgboost: Not installed\n", " catboost: Not installed\n", " kmodes: Not installed\n", " mlxtend: Not installed\n", " statsforecast: Not installed\n", " tune_sklearn: Not installed\n", " ray: Not installed\n", " hyperopt: Not installed\n", " optuna: Not installed\n", " skopt: Not installed\n", " mlflow: 2.1.1\n", " gradio: Not installed\n", " fastapi: Not installed\n", " uvicorn: Not installed\n", " m2cgen: Not installed\n", " evidently: Not installed\n", " fugue: 0.8.0\n", " streamlit: Not installed\n", " prophet: 1.1.2\n" ] } ], "source": [ "def what_is_installed():\n", " from pycaret import show_versions\n", " show_versions()\n", "\n", "try:\n", " what_is_installed()\n", "except ModuleNotFoundError:\n", " !pip install pycaret\n", " what_is_installed()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pycaret.datasets import get_data\n", "from pycaret.time_series import TSForecastingExperiment" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "y = get_data('airline', verbose=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# We want to forecast the next 12 months of data and we will use 3 fold cross-validation to test the models.\n", "fh = 12 # or alternately fh = np.arange(1,13)\n", "fold = 3" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Global Figure Settings for notebook ----\n", "# Depending on whether you are using jupyter notebook, jupyter lab, Google Colab, you may have to set the renderer appropriately\n", "# NOTE: Setting to a static renderer here so that the notebook saved size is reduced.\n", "fig_kwargs = {\n", " # \"renderer\": \"notebook\",\n", " \"renderer\": \"png\",\n", " \"width\": 1000,\n", " \"height\": 400,\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# User Customizations\n", "\n", "Let's look at how users can customize various steps in the modeling process" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prediction Customization\n", "\n", "### Forecast Horizon\n", "Sometimes users may wish to customize the forecast horizon after the model has been created. This can be done as follows." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, session_id=42, verbose=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.44620.493313.028616.14850.03270.03340.9151
11957-120.59830.599318.292020.34420.05060.04910.8916
21958-121.00440.928028.699930.16690.06710.06970.7964
MeanNaT0.68300.673520.006922.21990.05010.05070.8677
SDNaT0.23560.18516.51175.87460.01410.01480.0513
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = exp.create_model(\"arima\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2
0ARIMA0.49550.539515.086718.63800.03120.03120.9373
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_pred
1960-01420.8767
1960-02397.1069
1960-03456.4335
1960-04442.6482
1960-05463.5822
1960-06513.0988
1960-07587.0872
1960-08596.4580
1960-09499.1383
1960-10442.0694
1960-11396.2036
1960-12438.5023
\n", "
" ], "text/plain": [ " y_pred\n", "1960-01 420.8767\n", "1960-02 397.1069\n", "1960-03 456.4335\n", "1960-04 442.6482\n", "1960-05 463.5822\n", "1960-06 513.0988\n", "1960-07 587.0872\n", "1960-08 596.4580\n", "1960-09 499.1383\n", "1960-10 442.0694\n", "1960-11 396.2036\n", "1960-12 438.5023" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Default prediction\n", "exp.predict_model(model)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_pred
1960-01420.8767
1960-02397.1069
1960-03456.4335
1960-04442.6482
1960-05463.5822
1960-06513.0988
1960-07587.0872
1960-08596.4580
1960-09499.1383
1960-10442.0694
1960-11396.2036
1960-12438.5023
1961-01453.8109
1961-02429.5811
1961-03488.5351
1961-04474.4479
1961-05495.1374
1961-06544.4560
1961-07618.2840
1961-08627.5248
1961-09530.0999
1961-10472.9458
1961-11427.0110
1961-12469.2538
\n", "
" ], "text/plain": [ " y_pred\n", "1960-01 420.8767\n", "1960-02 397.1069\n", "1960-03 456.4335\n", "1960-04 442.6482\n", "1960-05 463.5822\n", "1960-06 513.0988\n", "1960-07 587.0872\n", "1960-08 596.4580\n", "1960-09 499.1383\n", "1960-10 442.0694\n", "1960-11 396.2036\n", "1960-12 438.5023\n", "1961-01 453.8109\n", "1961-02 429.5811\n", "1961-03 488.5351\n", "1961-04 474.4479\n", "1961-05 495.1374\n", "1961-06 544.4560\n", "1961-07 618.2840\n", "1961-08 627.5248\n", "1961-09 530.0999\n", "1961-10 472.9458\n", "1961-11 427.0110\n", "1961-12 469.2538" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Increased forecast horizon to 2 years instead of the original 1 year\n", "exp.predict_model(model, fh=24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prediction Interval\n", "\n", "#### NOTES: \n", "1. **When prediction intervals are requested, the default coverage = 0.9 corresponding to 90% coverage.** \n", "2. **Coverage is symmetrical around the median (alpha = 0.5). Hence a coverage of 0.9 corresponds to lower interval = 0.05 and an upper interval of 0.95 to give a total coverage between lower and upper interval = 0.9.**" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2
0ARIMA0.49550.539515.086718.63800.03120.03120.9373
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_predlowerupper
1960-01420.8767403.9466437.8067
1960-02397.1069375.3199418.8939
1960-03456.4335431.9786480.8884
1960-04442.6482416.5909468.7055
1960-05463.5822436.5252490.6392
1960-06513.0988485.4054540.7921
1960-07587.0872558.9843615.1902
1960-08596.4580568.0895624.8264
1960-09499.1383470.5969527.6796
1960-10442.0694413.4152470.7236
1960-11396.2036367.4756424.9316
1960-12438.5023409.7260467.2786
\n", "
" ], "text/plain": [ " y_pred lower upper\n", "1960-01 420.8767 403.9466 437.8067\n", "1960-02 397.1069 375.3199 418.8939\n", "1960-03 456.4335 431.9786 480.8884\n", "1960-04 442.6482 416.5909 468.7055\n", "1960-05 463.5822 436.5252 490.6392\n", "1960-06 513.0988 485.4054 540.7921\n", "1960-07 587.0872 558.9843 615.1902\n", "1960-08 596.4580 568.0895 624.8264\n", "1960-09 499.1383 470.5969 527.6796\n", "1960-10 442.0694 413.4152 470.7236\n", "1960-11 396.2036 367.4756 424.9316\n", "1960-12 438.5023 409.7260 467.2786" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# With Prediction Interval (default coverage = 0.9)\n", "exp.predict_model(model, return_pred_int=True)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2
0ARIMA0.49550.539515.086718.63800.03120.03120.9373
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_predlowerupper
1960-01420.8767407.6860434.0673
1960-02397.1069380.1320414.0818
1960-03456.4335437.3800475.4870
1960-04442.6482422.3463462.9502
1960-05463.5822442.5013484.6631
1960-06513.0988491.5221534.6754
1960-07587.0872565.1915608.9830
1960-08596.4580574.3553618.5606
1960-09499.1383476.9009521.3756
1960-10442.0694419.7441464.3946
1960-11396.2036373.8208418.5863
1960-12438.5023416.0819460.9227
\n", "
" ], "text/plain": [ " y_pred lower upper\n", "1960-01 420.8767 407.6860 434.0673\n", "1960-02 397.1069 380.1320 414.0818\n", "1960-03 456.4335 437.3800 475.4870\n", "1960-04 442.6482 422.3463 462.9502\n", "1960-05 463.5822 442.5013 484.6631\n", "1960-06 513.0988 491.5221 534.6754\n", "1960-07 587.0872 565.1915 608.9830\n", "1960-08 596.4580 574.3553 618.5606\n", "1960-09 499.1383 476.9009 521.3756\n", "1960-10 442.0694 419.7441 464.3946\n", "1960-11 396.2036 373.8208 418.5863\n", "1960-12 438.5023 416.0819 460.9227" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# With Prediction Interval (custom coverage = 0.8, corresponding to lower and upper quantiles of 0.1 and 0.9 respectively)\n", "# The point estimate remains the same as before.\n", "# But the lower and upper intervals are now narrower since we are OK with a lower coverage.\n", "exp.predict_model(model, return_pred_int=True, coverage=0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Sometimes, users may wish to get the point estimates at values other than the mean/median. In such cases, they can specify the alpha (quantile) value for the point estimate directly.**\n", "\n", "**NOTE: Not all models support this feature. If this is used with models that do not support it, an error is raised. If you want to only use models that support this feature, you must set `point_alpha` to a floating point value in the `setup` stage (see below).**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2
0ARIMA0.43350.516813.200417.85490.02920.02870.9425
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_pred
1960-01426.2742
1960-02404.0529
1960-03464.2301
1960-04450.9556
1960-05472.2083
1960-06521.9277
1960-07596.0468
1960-08605.5022
1960-09508.2376
1960-10451.2047
1960-11405.3624
1960-12447.6766
\n", "
" ], "text/plain": [ " y_pred\n", "1960-01 426.2742\n", "1960-02 404.0529\n", "1960-03 464.2301\n", "1960-04 450.9556\n", "1960-05 472.2083\n", "1960-06 521.9277\n", "1960-07 596.0468\n", "1960-08 605.5022\n", "1960-09 508.2376\n", "1960-10 451.2047\n", "1960-11 405.3624\n", "1960-12 447.6766" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# With Custom Point Estimate (alpha = 0.7)\n", "# The point estimate is now higher than before since we are asking for the\n", "# 70% percentile as the point estimate), vs. mean/median before.\n", "exp.predict_model(model, alpha=0.7)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.71370.844020.841227.62620.05130.05330.7516
11957-120.66780.703820.417223.89180.05570.05390.8505
21958-120.71980.763020.566924.80240.04570.04710.8624
MeanNaT0.70040.770220.608425.44010.05090.05140.8215
SDNaT0.02320.05750.17561.58980.00410.00310.0497
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2
0LinearRegression0.79930.927524.337632.04180.04750.04930.8147
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
y_predlowerupper
1960-01399.5740NaNNaN
1960-02384.6911NaNNaN
1960-03420.8922NaNNaN
1960-04412.8696NaNNaN
1960-05438.3520NaNNaN
1960-06494.9357NaNNaN
1960-07556.8907NaNNaN
1960-08558.1492NaNNaN
1960-09503.6881NaNNaN
1960-10449.0433NaNNaN
1960-11405.1229NaNNaN
1960-12431.7701NaNNaN
\n", "
" ], "text/plain": [ " y_pred lower upper\n", "1960-01 399.5740 NaN NaN\n", "1960-02 384.6911 NaN NaN\n", "1960-03 420.8922 NaN NaN\n", "1960-04 412.8696 NaN NaN\n", "1960-05 438.3520 NaN NaN\n", "1960-06 494.9357 NaN NaN\n", "1960-07 556.8907 NaN NaN\n", "1960-08 558.1492 NaN NaN\n", "1960-09 503.6881 NaN NaN\n", "1960-10 449.0433 NaN NaN\n", "1960-11 405.1229 NaN NaN\n", "1960-12 431.7701 NaN NaN" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For models that do not produce a prediction interval --> returns NA values\n", "model_no_pred_int = exp.create_model(\"lr_cds_dt\")\n", "exp.predict_model(model_no_pred_int, return_pred_int=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Forecast Plotting Customization\n", "\n", "Similar to the prediction customization, we can customize the forecast plots as well." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Regular Plot\n", "exp.plot_model(model)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Modified Plot (zoom into the plot to see differences between the 2 plots)\n", "exp.plot_model(model, data_kwargs={\"alpha\": 0.7, \"coverage\": 0.8})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Enforce Prediction Intervals\n", "\n", "In some use cases, it is important to have prediction intervals. Users may wish to restrict the modeling to only those models that support prediction intervals.\n", "\n", "* Specifying `point_alpha` to any floating point value restricts the models to only those that provide a prediction interval. The value that is specified corresponds to the quantile of the point prediction that is returned.\n", "* This also adds an extra metric called `COVERAGE`.\n", "* `COVERAGE` gives the percentage of actuals that are within the prediction interval." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id3833
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalTrue
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoauto
16Max Period to Consider60
17Seasonal Period(s) Tested[12, 24, 36, 11, 48]
18Significant Seasonal Period(s)[12, 24, 36, 11, 48]
19Significant Seasonal Period(s) without Harmonics[48, 36, 11]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[12]
24Primary Seasonality12
25Seasonality PresentTrue
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D1
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USI95ff
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NameReferenceTurbo
ID
naiveNaive Forecastersktime.forecasting.naive.NaiveForecasterTrue
grand_meansGrand Means Forecastersktime.forecasting.naive.NaiveForecasterTrue
snaiveSeasonal Naive Forecastersktime.forecasting.naive.NaiveForecasterTrue
arimaARIMAsktime.forecasting.arima.ARIMATrue
auto_arimaAuto ARIMAsktime.forecasting.arima.AutoARIMATrue
etsETSsktime.forecasting.ets.AutoETSTrue
thetaTheta Forecastersktime.forecasting.theta.ThetaForecasterTrue
tbatsTBATSsktime.forecasting.tbats.TBATSFalse
batsBATSsktime.forecasting.bats.BATSFalse
prophetProphetpycaret.containers.models.time_series.ProphetP...False
\n", "
" ], "text/plain": [ " Name \\\n", "ID \n", "naive Naive Forecaster \n", "grand_means Grand Means Forecaster \n", "snaive Seasonal Naive Forecaster \n", "arima ARIMA \n", "auto_arima Auto ARIMA \n", "ets ETS \n", "theta Theta Forecaster \n", "tbats TBATS \n", "bats BATS \n", "prophet Prophet \n", "\n", " Reference Turbo \n", "ID \n", "naive sktime.forecasting.naive.NaiveForecaster True \n", "grand_means sktime.forecasting.naive.NaiveForecaster True \n", "snaive sktime.forecasting.naive.NaiveForecaster True \n", "arima sktime.forecasting.arima.ARIMA True \n", "auto_arima sktime.forecasting.arima.AutoARIMA True \n", "ets sktime.forecasting.ets.AutoETS True \n", "theta sktime.forecasting.theta.ThetaForecaster True \n", "tbats sktime.forecasting.tbats.TBATS False \n", "bats sktime.forecasting.bats.BATS False \n", "prophet pycaret.containers.models.time_series.ProphetP... False " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "\n", "# We can see that specifying a value for point_alpha enables `Enforce Prediction Interval` in the grid (and limits the models).\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, point_alpha=0.5)\n", "exp.models()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 ModelMASERMSSEMAERMSEMAPESMAPER2COVERAGETT (Sec)
etsETS0.58650.614517.218420.28570.04350.04400.89090.66670.1500
arimaARIMA0.68300.673520.006922.21990.05010.05070.86770.63890.0733
auto_arimaAuto ARIMA0.71810.711421.029723.46610.05250.05310.85090.69442.9400
thetaTheta Forecaster0.97291.030628.319233.86390.06700.07000.67100.63890.0400
snaiveSeasonal Naive Forecaster1.14791.094533.361135.91390.08320.08790.60720.83330.0200
naiveNaive Forecaster2.35992.761269.027891.03220.15690.1792-1.22160.77781.3900
grand_meansGrand Means Forecaster5.53065.2596162.4117173.64920.40000.5075-7.04620.41670.0233
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "best_model = exp.compare_models()\n", "\n", "# # To enable slower models such as prophet, BATS and TBATS, add turbo=False\n", "# best_model = exp.compare_models(turbo=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types of Window Splitters\n", "\n", "Various window splitters are available for performing the cross validation.\n", "\n", "### Sliding Window Splitter" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, fold_strategy='sliding', verbose=False)\n", "exp.plot_model(plot=\"cv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Expanding/Rolling Window\n", "\n", "* They are identical" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, fold_strategy='expanding', verbose=False)\n", "exp.plot_model(plot=\"cv\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, fold_strategy='rolling', verbose=False)\n", "exp.plot_model(plot=\"cv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Error Handling due to lack of data\n", "\n", "Sometimes, there are not enough data points available to perform the experiment. In such cases, pycaret will warn you accordingly." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Not Enough Data Points, set a lower number of folds or fh\n" ] } ], "source": [ "try:\n", " exp = TSForecastingExperiment()\n", " exp.setup(data=y[:30], fh=12, fold=3, fig_kwargs=fig_kwargs)\n", "except ValueError as error:\n", " print(error)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id5965
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(30, 1)
5Transformed data shape(30, 1)
6Transformed train set shape(24, 1)
7Transformed test set shape(6, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoauto
16Max Period to Consider60
17Seasonal Period(s) Tested[]
18Significant Seasonal Period(s)[1]
19Significant Seasonal Period(s) without Harmonics[1]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[1]
24Primary Seasonality1
25Seasonality PresentFalse
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D0
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USIa170
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " exp = TSForecastingExperiment()\n", " exp.setup(data=y[:30], fh=6, fold=3, fig_kwargs=fig_kwargs)\n", "except ValueError as error:\n", " print(error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuning Customization" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id42
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoauto
16Max Period to Consider60
17Seasonal Period(s) Tested[12, 24, 36, 11, 48]
18Significant Seasonal Period(s)[12, 24, 36, 11, 48]
19Significant Seasonal Period(s) without Harmonics[48, 36, 11]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[12]
24Primary Seasonality12
25Seasonality PresentTrue
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D1
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USIa819
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs, session_id=42)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.71370.844020.841227.62620.05130.05330.7516
11957-120.66780.703820.417223.89180.05570.05390.8505
21958-120.71980.763020.566924.80240.04570.04710.8624
MeanNaT0.70040.770220.608425.44010.05090.05140.8215
SDNaT0.02320.05750.17561.58980.00410.00310.0497
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = exp.create_model(\"lr_cds_dt\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.43730.502112.770016.43410.03270.03360.9121
11957-120.54970.641016.806121.76220.04570.04410.8759
21958-120.66890.684619.112822.25420.04620.04760.8892
MeanNaT0.55200.609216.229620.15020.04150.04180.8924
SDNaT0.09460.07782.62132.63530.00620.00590.0149
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 4.8s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(n_jobs=-1), sp=12,\n", " window_length=12)\n", "BaseCdsDtForecaster(deseasonal_model='multiplicative',\n", " fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(fit_intercept=False, n_jobs=-1),\n", " sp=24, window_length=23)\n" ] } ], "source": [ "# Random Grid Search (default)\n", "tuned_model = exp.tune_model(model)\n", "print(model)\n", "print(tuned_model)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp.plot_model([model, tuned_model], data_kwargs={\"labels\": [\"Original\", \"Tuned\"]})" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.70530.834520.595427.31460.05070.05270.7571
11957-120.67930.711320.767924.14710.05680.05490.8472
21958-120.66990.719719.142423.39330.04250.04360.8776
MeanNaT0.68480.755120.168624.95170.05000.05040.8273
SDNaT0.01490.05620.72901.69900.00590.00490.0511
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.8s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(n_jobs=-1), sp=12,\n", " window_length=12)\n", "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(fit_intercept=False, n_jobs=-1),\n", " sp=12)\n" ] } ], "source": [ "# Fixed Grid Search\n", "tuned_model = exp.tune_model(model, search_algorithm=\"grid\")\n", "print(model)\n", "print(tuned_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations:**\n", "* In this case, the tuning resulted in worse metrics than the original model (this is possible).\n", "* Hence, pycaret returned the original model as the best one since `choose_better=True` by default.\n", "* If the user does not want this behavior, they can set `choose_better=False`" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.70530.834520.595427.31460.05070.05270.7571
11957-120.67930.711320.767924.14710.05680.05490.8472
21958-120.66990.719719.142423.39330.04250.04360.8776
MeanNaT0.68480.755120.168624.95170.05000.05040.8273
SDNaT0.01490.05620.72901.69900.00590.00490.0511
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.8s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(n_jobs=-1), sp=12,\n", " window_length=12)\n", "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(fit_intercept=False, n_jobs=-1),\n", " sp=12)\n" ] } ], "source": [ "tuned_model = exp.tune_model(model, search_algorithm=\"grid\", choose_better=False)\n", "print(model)\n", "print(tuned_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes, there are time constraints on the tuning so users may wish to adjust the number of hyperparameters that are tried using the `n_iter` argument." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 cutoffMASERMSSEMAERMSEMAPESMAPER2
01956-120.54470.566715.907718.55150.04470.04370.8880
11957-121.25551.259038.384742.74140.10720.10040.5214
21958-120.68780.715519.653023.25730.04420.04540.8790
MeanNaT0.82930.847124.648428.18340.06540.06320.7628
SDNaT0.30700.29759.832610.47180.02960.02630.1707
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", "[Parallel(n_jobs=-1)]: Done 15 out of 15 | elapsed: 1.9s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(n_jobs=-1), sp=12,\n", " window_length=12)\n", "BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=LinearRegression(n_jobs=-1), sp=12,\n", " window_length=12)\n" ] } ], "source": [ "tuned_model = exp.tune_model(model, n_iter=5)\n", "print(model)\n", "print(tuned_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More information about tunuing in pycaret time series can be found here:\n", " \n", "1. **[Basic Tuning](https://github.com/pycaret/pycaret/discussions/1791)**\n", "2. **[Advanced Tuning](https://github.com/pycaret/pycaret/discussions/1795)**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Renderer\n", "\n", "Sometimes the plotly renderer if not detected correctly for the environment. In such cases, the users can manually specify the render in pycaret" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-12-01T00:00:00", "1957-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-12-01T00:00:00", "1958-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-01-01T00:00:00", "1958-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-02-01T00:00:00", "1958-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-03-01T00:00:00", "1958-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-04-01T00:00:00", "1958-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-05-01T00:00:00", "1958-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-06-01T00:00:00", "1958-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-07-01T00:00:00", "1958-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-08-01T00:00:00", "1958-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-09-01T00:00:00", "1958-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-10-01T00:00:00", "1958-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-11-01T00:00:00", "1958-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-12-01T00:00:00", "1959-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-01-01T00:00:00", "1959-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-02-01T00:00:00", "1959-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-03-01T00:00:00", "1959-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-04-01T00:00:00", "1959-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-05-01T00:00:00", "1959-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-06-01T00:00:00", "1959-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-07-01T00:00:00", "1959-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-08-01T00:00:00", "1959-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-09-01T00:00:00", "1959-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-10-01T00:00:00", "1959-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-11-01T00:00:00", "1959-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-12-01T00:00:00", "1957-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-12-01T00:00:00", "1958-01-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-01-01T00:00:00", "1958-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-02-01T00:00:00", "1958-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-03-01T00:00:00", "1958-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-04-01T00:00:00", "1958-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-05-01T00:00:00", "1958-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-06-01T00:00:00", "1958-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-07-01T00:00:00", "1958-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-08-01T00:00:00", "1958-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-09-01T00:00:00", "1958-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-10-01T00:00:00", "1958-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1958-11-01T00:00:00", "1958-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-01-01T00:00:00", "1959-02-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-02-01T00:00:00", "1959-03-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-03-01T00:00:00", "1959-04-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-04-01T00:00:00", "1959-05-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-05-01T00:00:00", "1959-06-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-06-01T00:00:00", "1959-07-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-07-01T00:00:00", "1959-08-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-08-01T00:00:00", "1959-09-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-09-01T00:00:00", "1959-10-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-10-01T00:00:00", "1959-11-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1959-11-01T00:00:00", "1959-12-01T00:00:00" ], "y": [ "2", "2" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-12-01T00:00:00", "1957-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-12-01T00:00:00", "1958-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-01-01T00:00:00", "1958-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-02-01T00:00:00", "1958-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-03-01T00:00:00", "1958-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-04-01T00:00:00", "1958-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-05-01T00:00:00", "1958-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-06-01T00:00:00", "1958-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-07-01T00:00:00", "1958-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-08-01T00:00:00", "1958-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-09-01T00:00:00", "1958-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-10-01T00:00:00", "1958-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-11-01T00:00:00", "1958-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-12-01T00:00:00", "1959-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-01-01T00:00:00", "1959-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-02-01T00:00:00", "1959-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-03-01T00:00:00", "1959-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-04-01T00:00:00", "1959-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-05-01T00:00:00", "1959-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-06-01T00:00:00", "1959-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-07-01T00:00:00", "1959-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-08-01T00:00:00", "1959-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-09-01T00:00:00", "1959-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-10-01T00:00:00", "1959-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-11-01T00:00:00", "1959-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-12-01T00:00:00", "1957-01-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-01-01T00:00:00", "1958-02-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-02-01T00:00:00", "1958-03-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-03-01T00:00:00", "1958-04-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-04-01T00:00:00", "1958-05-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-05-01T00:00:00", "1958-06-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-06-01T00:00:00", "1958-07-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-07-01T00:00:00", "1958-08-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-08-01T00:00:00", "1958-09-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-09-01T00:00:00", "1958-10-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-10-01T00:00:00", "1958-11-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1958-11-01T00:00:00", "1958-12-01T00:00:00" ], "y": [ "1", "1" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1956-12-01T00:00:00", "1957-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1957-12-01T00:00:00", "1958-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-01-01T00:00:00", "1958-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-02-01T00:00:00", "1958-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-03-01T00:00:00", "1958-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-04-01T00:00:00", "1958-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-05-01T00:00:00", "1958-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-06-01T00:00:00", "1958-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-07-01T00:00:00", "1958-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-08-01T00:00:00", "1958-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-09-01T00:00:00", "1958-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-10-01T00:00:00", "1958-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-11-01T00:00:00", "1958-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1958-12-01T00:00:00", "1959-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-01-01T00:00:00", "1959-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-02-01T00:00:00", "1959-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-03-01T00:00:00", "1959-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-04-01T00:00:00", "1959-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-05-01T00:00:00", "1959-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-06-01T00:00:00", "1959-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-07-01T00:00:00", "1959-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-08-01T00:00:00", "1959-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-09-01T00:00:00", "1959-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-10-01T00:00:00", "1959-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#C0C0C0" }, "mode": "lines+markers", "name": "Unchanged", "showlegend": false, "type": "scattergl", "x": [ "1959-11-01T00:00:00", "1959-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-01-01T00:00:00", "1949-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-02-01T00:00:00", "1949-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-03-01T00:00:00", "1949-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-04-01T00:00:00", "1949-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-05-01T00:00:00", "1949-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-06-01T00:00:00", "1949-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-07-01T00:00:00", "1949-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-08-01T00:00:00", "1949-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-09-01T00:00:00", "1949-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-10-01T00:00:00", "1949-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-11-01T00:00:00", "1949-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1949-12-01T00:00:00", "1950-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-01-01T00:00:00", "1950-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-02-01T00:00:00", "1950-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-03-01T00:00:00", "1950-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-04-01T00:00:00", "1950-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-05-01T00:00:00", "1950-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-06-01T00:00:00", "1950-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-07-01T00:00:00", "1950-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-08-01T00:00:00", "1950-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-09-01T00:00:00", "1950-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-10-01T00:00:00", "1950-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-11-01T00:00:00", "1950-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1950-12-01T00:00:00", "1951-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-01-01T00:00:00", "1951-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-02-01T00:00:00", "1951-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-03-01T00:00:00", "1951-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-04-01T00:00:00", "1951-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-05-01T00:00:00", "1951-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-06-01T00:00:00", "1951-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-07-01T00:00:00", "1951-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-08-01T00:00:00", "1951-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-09-01T00:00:00", "1951-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-10-01T00:00:00", "1951-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-11-01T00:00:00", "1951-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1951-12-01T00:00:00", "1952-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-01-01T00:00:00", "1952-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-02-01T00:00:00", "1952-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-03-01T00:00:00", "1952-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-04-01T00:00:00", "1952-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-05-01T00:00:00", "1952-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-06-01T00:00:00", "1952-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-07-01T00:00:00", "1952-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-08-01T00:00:00", "1952-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-09-01T00:00:00", "1952-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-10-01T00:00:00", "1952-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-11-01T00:00:00", "1952-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1952-12-01T00:00:00", "1953-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-01-01T00:00:00", "1953-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-02-01T00:00:00", "1953-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-03-01T00:00:00", "1953-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-04-01T00:00:00", "1953-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-05-01T00:00:00", "1953-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-06-01T00:00:00", "1953-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-07-01T00:00:00", "1953-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-08-01T00:00:00", "1953-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-09-01T00:00:00", "1953-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-10-01T00:00:00", "1953-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-11-01T00:00:00", "1953-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1953-12-01T00:00:00", "1954-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-01-01T00:00:00", "1954-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-02-01T00:00:00", "1954-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-03-01T00:00:00", "1954-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-04-01T00:00:00", "1954-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-05-01T00:00:00", "1954-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-06-01T00:00:00", "1954-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-07-01T00:00:00", "1954-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-08-01T00:00:00", "1954-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-09-01T00:00:00", "1954-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-10-01T00:00:00", "1954-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-11-01T00:00:00", "1954-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1954-12-01T00:00:00", "1955-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-01-01T00:00:00", "1955-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-02-01T00:00:00", "1955-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-03-01T00:00:00", "1955-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-04-01T00:00:00", "1955-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-05-01T00:00:00", "1955-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-06-01T00:00:00", "1955-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-07-01T00:00:00", "1955-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-08-01T00:00:00", "1955-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-09-01T00:00:00", "1955-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-10-01T00:00:00", "1955-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-11-01T00:00:00", "1955-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1955-12-01T00:00:00", "1956-01-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-01-01T00:00:00", "1956-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-02-01T00:00:00", "1956-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-03-01T00:00:00", "1956-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-04-01T00:00:00", "1956-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-05-01T00:00:00", "1956-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-06-01T00:00:00", "1956-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-07-01T00:00:00", "1956-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-08-01T00:00:00", "1956-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-09-01T00:00:00", "1956-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-10-01T00:00:00", "1956-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#1f77b4" }, "mode": "lines+markers", "name": "Train", "showlegend": false, "type": "scattergl", "x": [ "1956-11-01T00:00:00", "1956-12-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-01-01T00:00:00", "1957-02-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-02-01T00:00:00", "1957-03-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-03-01T00:00:00", "1957-04-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-04-01T00:00:00", "1957-05-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-05-01T00:00:00", "1957-06-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-06-01T00:00:00", "1957-07-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-07-01T00:00:00", "1957-08-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-08-01T00:00:00", "1957-09-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-09-01T00:00:00", "1957-10-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-10-01T00:00:00", "1957-11-01T00:00:00" ], "y": [ "0", "0" ] }, { "hoverinfo": "skip", "line": { "color": "#DE970B" }, "mode": "lines+markers", "name": "ForecastHorizon", "showlegend": false, "type": "scattergl", "x": [ "1957-11-01T00:00:00", "1957-12-01T00:00:00" ], "y": [ "0", "0" ] } ], "layout": { "autosize": true, "showlegend": true, "template": { "data": { "bar": [ { "error_x": { "color": "rgb(51,51,51)" }, "error_y": { "color": "rgb(51,51,51)" }, "marker": { "line": { "color": "rgb(237,237,237)", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "rgb(237,237,237)", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "rgb(51,51,51)", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "rgb(51,51,51)" }, "baxis": { "endlinecolor": "rgb(51,51,51)", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "rgb(51,51,51)" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" }, "colorscale": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "rgb(237,237,237)" }, "line": { "color": "white" } }, "header": { "fill": { "color": "rgb(217,217,217)" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "tickcolor": "rgb(237,237,237)", "ticklen": 6, "ticks": "inside" } }, "colorscale": { "sequential": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ], "sequentialminus": [ [ 0, "rgb(20,44,66)" ], [ 1, "rgb(90,179,244)" ] ] }, "colorway": [ "#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3" ], "font": { "color": "rgb(51,51,51)" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "rgb(237,237,237)", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "paper_bgcolor": "white", "plot_bgcolor": "rgb(237,237,237)", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside" }, "bgcolor": "rgb(237,237,237)", "radialaxis": { "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside" } }, "scene": { "xaxis": { "backgroundcolor": "rgb(237,237,237)", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "rgb(237,237,237)", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "rgb(237,237,237)", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside", "zerolinecolor": "white" } }, "shapedefaults": { "fillcolor": "black", "line": { "width": 0 }, "opacity": 0.3 }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside" }, "baxis": { "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside" }, "bgcolor": "rgb(237,237,237)", "caxis": { "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside" } }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside", "title": { "standoff": 15 }, "zerolinecolor": "white" }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "showgrid": true, "tickcolor": "rgb(51,51,51)", "ticks": "outside", "title": { "standoff": 15 }, "zerolinecolor": "white" } } }, "title": { "text": "Train Cross-Validation Splits" }, "xaxis": { "autorange": true, "range": [ "1948-05-07 08:26:50.9385", "1960-07-26 15:33:09.0615" ], "title": { "text": "Time" }, "type": "date", "zeroline": false }, "yaxis": { "autorange": true, "range": [ -0.1650485436893204, 2.1650485436893203 ], "title": { "text": "Windows" }, "type": "category" } } }, "image/png": "iVBORw0KGgoAAAANSUhEUgAABNkAAAFoCAYAAABqhi2QAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQd4FVX6h3/pvZKQkAAhtNCLgBVUxIIVUWyrKzZkLSjrWlbXsqzs/m27Yll3WXtZuwLqKij2Tu8dAoFAKAmk9+T/fGfu3Nx7c28KE8g9md95Hh9JZs7MOe/35czc9845E1BfX18PFhIgARIgARIgARIgARIgARIgARIgARIgARIggcMmEEDJdtjsWJEESIAESIAESIAESIAESIAESIAESIAESIAEFAFKNiYCCZAACZAACZAACZAACZAACZAACZAACZAACVgkQMlmESCrkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAlG3OABEiABEiABEiABEiABEiABEiABEiABEiABCwSoGSzCJDVSYAESIAESIAESIAESIAESIAESIAESIAESICSjTlAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhYJULJZBMjqJEACJEACJEACJEACJEACJEACJEACJEACJEDJxhwgARIgARIgARIgARIgARIgARIgARIgARIgAYsEKNksAmR1EiABEiABEiABEiABEiABEiABEiABEiABEqBkYw6QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgEUClGwWAbI6CZAACZAACZAACZAACZAACZAACZAACZAACVCyMQdIgARIgARIgARIgARIgARIgARIgARIgARIwCIBSjaLAFmdBEiABEiABEiABEiABEiABEiABEiABEiABCjZmAMkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYJEAJZtFgKxOAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAApRszAESIAESIAESIAESIAESIAESIAESIAESIAESsEiAks0iQFYnARIgARIgARIgARIgARIgARIgARIgARIgAUo25gAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJWCRAyWYRIKuTAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQACUbc4AESIAESIAESIAESIAESIAESIAESIAESIAELBKgZLMIkNVJgARIgARIgARIgARIgARIgARIgARIgARIgJKNOUACJEACJEACJEACJEACJEACJEACJEACJEACFglQslkEyOokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQMnGHCABEiABEiABEiABEiABEiABEiABEiABEiABiwQo2SwCZHUSIAESIAESIAESIAESIAESIAESIAESIAESoGRjDpAACZAACZAACZAACZAACZAACZAACZAACZCARQKUbBYBsjoJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJULIxB0iABEiABEiABEiABEiABEiABEiABEiABEjAIgFKNosAWZ0ESIAESIAESIAESIAESIAESIAESIAESIAEKNmYAyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgkQAlm0WArE4CJEACJEACJEACJEACJEACJEACJEACJEAClGzMARIgARIgARIgARIgARIgARIgARIgARIgARKwSICSzSJAVicBEiABEiABEiABEiABEiABEiABEiABEiABSjbmAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAlYJEDJZhEgq5MACZAACZAACZAACZAACZAACZAACZAACZAAJRtzgARIgARIgARIgARIgARIgARIgARIgARIgAQsEqBkswiQ1UmABEiABEiABEigPQjMmzcPzz//PJ555hlkZma2qgmHDh3C9OnTMXHiREyYMKFVde24szBeunQpZs2ahfj4eDsiYJ9JgARIgARIgARaQICSrQWQuAsJkAAJkAAJ6EBARMCrr77abFPHjx+P+++/H+Hh4c3u62uH7OxsTJs2DWeddZb6f1sXUwKtWbPG7dCTJ08+Iudr6/a7Hk/ismDBgiZl2OEIM291WnIuaZsVyWbGfsqUKY0EnbTp4YcfxuzZszFixIgjidXnsX3ljpW895Rs5jm6du3q/Ftqiku7gOBJSYAESIAESIAEjjoBSrajjpwnJAESIAESIIGjQ8D80D9jxow2Fx5HUrKZosZTqJliQ+jp9ERRc/KloqICM2fOVEnRGvlJydb470ieNps6dSo8c8eMQVJS0mHlDiXb0RmzeBYSIAESIAES0J0AJZvuEWT7SYAESIAESMAHgSMp2Y4UdFOSPPDAAz6nMb799tuQp5J0mbbXnERrTsL5Yn04T7+ZxzpST7IdqbxoyXGb4yzHONzcacl00cONY0v6xn1IgARIgARIgAT0IEDJpkec2EoSIAESIAESaDUBX5LNVc588sknzimmMsVPijwJ5Fk8p/95EwrmcR977DG8+eabmD9/vjrMoEGDWvT0kClJdu3a1aL9TVEk0xLPO+88NY00Ly9PCTjziTCznfJ7KampqV6nbXpOtfXW5pbs05QQ8zWNUrjNmTPH2WdTNDYXA19PsnlbO8x8OtA85sCBA5Gfnw/XKZ8tOa8nT/N45pNjvsSf5/m9Td20mj+u+dCSKcyuOSz9kPiYxTPffT3JJrkn52qOixzXSv60+o+fFUiABEiABEiABNqFACVbu2DnSUmABEiABEjgyBNoSrKZQsFTJoho+emnn9zWPTPli+u+viSbHNdVZLXk6SKTRGunoLquveVN2nhrt7c1wzwFirc2t2SfpiLqSwB5+31LY9BSyeZtnTZv8WvpeZtbk83zZQy+2HnKVDM2h5s/ZtxE7rZkTThXMeb65KS3vGlOsknsm+JiNX+O/GjBM5AACZAACZAACbQFAUq2tqDIY5AACZAACZCAHxJoyZNsLXkrpSkvUlJSnPKtqSfZPN92KdLioYceavYtmK2dbtfUk0tNbXMVHhI2ecum+USSaxgXLlyI0aNHQ/rf3D4teYmEtymH3oSOt1TyFoOWSDZfOdDS6aItjb3ZZs82+eqft/j4egqupfkjbWjNCzN85VtLJKu39vs6XlO5aOZYS/LHD4cYNokESIAESIAESMCDACUbU4IESIAESIAEOiiBw5VsLREVrZVsMgW1uaeL2lKyNbUenavM6dKli3rpgDz95GsdONcnpHzt44uZa59b8oSUmYotiUFLJJsvceVLsrXkvK15ks1zKqzZP28iqynJ1pL88fwz9va2XdcXIrTmyTMrT7K1JH866BDEbpEACZAACZCA7QhQstku5OwwCZAACZCAXQgcjmTz9mbPlj7NZFWSHO50UW9PoTX1hJjnNlcJYuaG59ptLdmnubzyfKLJl+hqaQxaItl8Ldjv7dwtPW9rJFtTLwzw3GY1f5rjb0o3U3w21w/XdfKsSDZpV1vkT3P943YSIAESIAESIIH2J0DJ1v4xYAtIgARIgARI4IgQaK1k8zWt7WhJNisvPvBc6L6lT7J5my5r1k1KSvL5AoaW7OMtqK7roxUUFDSaRtuaGLREsvl6ksxTsrXmvM3JKdc12drzSTZP/p7tPlpPsnnLg8PNnyMyUPCgJEACJEACJEACbUaAkq3NUPJAJEACJEACJOBfBFor2Zpbo8rKmmwtne5nPmXma1qmEP7hhx/UG0ul+ForrTVrsq1Zs0atveZaXAVWQkICmtunJWvbyfFNxldddZU6phTzTaiu213f+im/P9w12ZpbE23ixImYMGGCz0X7vZ23qfXcjtSabC3JH2mXTPu9/PLLvf4herJoTb635Em2pqbgtlX++NcIw9aQAAmQAAmQAAl4EqBkY06QAAmQAAmQQAcl0FrJ5m2dLNdpbs2tZ9VW0/28TVuUEJkSQ/49a9asJiWbbPQm7DzfLupNxnly8Pbig9a8NdU1vTynDXrKxNbEoCVPsjV3PPP8ze3nGvum+u6rTQsWLHC++MLXE4tW88d1PTlvb80VUddcDkusvL2NtSWSzReXluQYX3zQQQdhdosESIAESMB2BCjZbBdydpgESIAESMAuBFor2YSLpwSStckee+wxvPnmmzgaT7KZsWnJAvxNPa1mHsdkkJeXp37ludaa/M5zH/mdq4xp6T4tzStT9HlrS2ti0BLJ5ut4M2bMUDLJfJKtNed1FZ7m03gmL1+izOyzyWj8+PFuT/DJ761KNm99MM/X0rjL/vKUpEjc+Ph4Z0hbItma4tKSHGtp/nA/EiABEiABEiAB/yVAyea/sWHLSIAESIAESIAESIAEjhCB1r7N9gg1g4clARIgARIgARLoQAQo2TpQMNkVEiABEiABEiABEiCBlhGgZGsZJ+5FAiRAAiRAAiTQcgKUbC1nxT1JgARIgARIgARIgAQ6CAFKtg4SSHaDBEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbnnFjq0mABEiABEiABEiABEiABEiABEiABEiABPyIACWbHwWDTSEBEiABEiABEiABEiABEiABEiABEiABEtCTACWbxbgVFBRYPAKr251ASEgIwsPDUVxcbHcU7L+mBCIiIlTLy8vLNe0Bm213AjIGBwYGoqyszO4o2H9NCURHR6Oqqkr9x0ICOhKIiopCTU0NKisrdWw+20wCiIuLQ0lJCWpra7WnkZiYqH0f2rMDlGwW6VOyWQTI6qBkYxLoToCSTfcIsv2UbMwB3QlQsukeQbafko05oDsBSjbdI9h27adks8iSks0iQFanZGMOaE+Akk37ENq+A5Rstk8B7QFQsmkfQtt3gJLN9imgPQBKNu1D2GYdoGSziJKSzSJAVqdkYw5oT4CSTfsQ2r4DlGy2TwHtAVCyaR9C23eAks32KaA9AEo27UPYZh2gZLOIkpLNIkBWp2RjDmhPgJJN+xDavgOUbLZPAe0BULJpH0Lbd4CSzfYpoD0ASjbtQ9hmHaBks4iSks0iQFanZGMOaE+Akk37ENq+A5Rstk8B7QFQsmkfQtt3gJLN9imgPQBKNu1D2GYdoGSziJKSzSJAVqdkYw5oT4CSTfsQ2r4DlGy2TwHtAVCyaR9C23eAks32KaA9AEo27UPYZh2gZLOIkpLNIkBWp2RjDmhPgJJN+xDavgOUbLZPAe0BULJpH0Lbd4CSzfYpoD0ASjbtQ9hmHaBkA1BRUYGZM2di/vz5CuwDDzyACRMmtAgyJVuLMHGnJgiEhIRAPuAVFxeTEwloSYCSTcuwsdEuBCjZmA66E6Bk0z2CbD8lG3NAdwKUbLpHsO3aT8kG4JlnnlFEp02bhkOHDmH69Onq3yNGjGiWtB0kW3VtHYICAxAYENCIR11dPSqqaxEZFtxoW1VNLWR7eGjjbbJzaWU1IkND4OWwqKyuVecMDgpsfM76epRX1SLKyznN40aEBnttb01tHWrr6hEWEuQ1tmVVNQgPCfJat6KqBoGBgQgN9t6miirvHOREZZWO4wY2ZhgQGITA4BDUVlV4bVN5VQ3CgoMQ6FG3vh6QbdJXXwzlgN76WltXh6qaOlXXWymvrkWID/4S1/p6Ydi4rvxeYhMe6p2hnNOo25i/xEXi7i2XpI2SZ5ITIV5yQuVafT3CvbTJyIkaRPrgJPkt55a4e5amzin8y6qqERUW4jOXwoIDERTYOF+aOqf0o6n8FkYS79Dgxu2VupKHEaFBCPCSFJIvkr/e2iQMqmrrEOGFg9lXX3+vQSFhKq51NVWNWDSVp2a+RIQFo/FfBmDkmvccbmrsMfNFxo9gL39zVsYmI3Z1XnOtudipfAoAQrzEzsrY1FQ+GfkS0OS41VS+eBt7hK+0t7qu3me+HO7Y1Ny41tTY1NS41tzYJGNwQEAgaqsrvf89yxjuZVxTf3MybvkYS5u6llXX1KFWjVu+rke1CA8J9H49amI8bG5sMq5lkhONz9tULpnXMhm/ZSz2LFbGpsrqGkeeNm5Ts+NWjYx53q5HTV8j1fgi10hvY6lcj3wcV/otbZJrkdd7lCbui0yGvv7mmhqbmsu14NBw1NXWqP88izE21Xi9XslYKn9Xvu6pKqpr1DXD27W32XxR9y/erzktGZt83Q80lWvG2CTXMh/3N01cB6WuFG9xlTGkvrYKAUGhXq+vsk32CQwOa8y/usyoF+gtT2tRV12BoNAor2NPXXUpAoIj1PjUKK61VUB9HQKDwxvXra9DXU05AkO8H7e+tgIICEJAYON7mHqpW12GoNBor22qrSpBYEhkq9uk7hNqqxDkhZGcSL6wq62tRVVV43sJ2S7bgoIajxEqNsLey/2W1Kurq1Mx83ZfJPWkeNtm1m3quL62NVdX+iJ1fbWpuf4cbl+bO64vTr7YSz+b499cXV/8rR63ub76YtjceWW7r3yhZPM6ZNjyl7aXbCLVHnroISXWMjMzVRK4SrfmsqIjS7ac/FLc9dFmbN5frjBcNiwZ95zZ24nkiYVb8eayfern2PAg/GNiPxzTLVYJi2nvrcMvO4rUtqzOEXhyYj+kxhk3AYtyCnHbe+tRVWtc2P5wajdceWxX9e/sA6W4a94mbMs3hNPonnF46uIBTon0wo/b8dyPe9S2iOAAzLq4P0ZlxKmfl+QU4fb316G8xjju707oghvH9FD/lgFx+gfr8f22QvVzZkIYHr8wCz2TjZuPJTmFmP7BBpRVGzdYNxyXiptPMfIhr7Acf5i7Cev3lqmfj+8Rh2cm9Xd+yHh/2W78beEOtS0kEHhqUn8c3yNe/bxsZxHumLMBRRW16ucrhifjrjMaGN49Zz0Wbj6ktqXFhuCxCVkY0CXGWffOORtwyFH3kqFJuPesPmrblxv24+6PtqgPCEEBAXhsQm+M7Zuktom8vO39DVieW6J+Hp4ejacn9XPeXM/43ybMW5uvtnWKDMZjF2ZheNdY9fPq3CLcPW8j9pYYN+nj+yXibxdkqX/Lzf+09zdgcY4R1yFp0Zh1URbiI0PVzz9uO4jff7gRNXUG/xnjM3H+kFT1b7kRn/b+eixy5MTA1ChVt1O0cTP65Jdb8fpSI5eiQwPx5MX9MaKb0aZNe0tUm3IOGTddp/aOxz8u6q/+Lbl22/vr8fN2I65ZyRH4+8QspMVHqJ8XbS/E7R+sR6Uj1+44pSuuOq6b2ib3VVL3x2yDf9/kCDxxYV90TYjEroNluHPuZmzab8T8pMx4PHVxP6fo/O+iXfj7NzvVttCgADx9SX8c293Iw6U7i/D7D9ajpMrIpWtGpeC2sT3Vv6Xc8cF6fLPVOGf3hDA8ekEfZKUYMZ+7Yg/+8vl29W9xuU9d1A8n9ExQPxeUVGL6hxuxJq9U/XxsRhyeubgfQhzS939r9uLBT7cZOREYgCcn9sXoXolq3+U7i3DX3I0oKDfiOnFwJzxwdl9nm/708UZ8tr5A/dw5ysiJIekGf9dcE9EuuXaaI9dESk97b71LrsXg6YuzEBVu3LR/szkfd83brOIkH8kfuaA3zuiXrLZ9vyUfd8xt2PaXszNx7mAjX1SufbARi3cYcR3cxci1hCgj1/6+cCv+6xh7YsIC8eRF/dXYI2WjypdN2HnIkCVje8fj7458kQ+at77bMDb16xyJv1/YF13MfMkpwm3vrXOOTXeO7YrfjDLyRT6I3vbhBvy0zYhdVudIlS/pjrov/LAdz/3UMDY9NWkARnY32rRDxtJ5m7HlgDGWnpQZp8YJ4Sl5OP2DdQ1jU2I4Hp/QBz2TjQ83nmPTlONScZNjbJLtMq5958injIQwPDahL/p0jsaB4grFd80eYxw4NiPWkS/GB5SPVuXhz/Oz1b9FRD55UV+c1LMhX+6cuwEHy41xa9KQJNw33hh7pNz30UbM32DkS2p0CB6d0BeDHfmycMMB3PPRZufY9PiE3jjVkS8lFdW47YMNWGGOTV0d+eIQ1V9tPKDGNYmT5Muj5/fC6f07q/Osyi3CPS5j09n9E/HX842xScrMzzbjw9UH1L8TIoLwxIX9MNyREz9sLcAdczY5xybJtfMcuVbtyLVFjlwb1CVa/e2YY9PP2w7i9g83oMb4c8aDZ/bAhcO6qH//85tteHHRXvXvqJBAzJrUMG5t229cy7ILjGvZmJ5x6nolN+ZyPZIx2hx7+iRFqJh372RcjxZtP6T+1iscJ711dDquO7G72razoEzlkq+x6Y1fd+If3+5S+4YFBag8k7FCyp7Cctzx4UZsdFzTT+gRh6ddrmVuY1O8kUt9U4w8lHHtjg/Xo7jSAHHViM64Y1wv9e/8kkr8fs4ml1yTsSnLKZM/WZ2HBz9ryLV/uIxNh8qqVF9X7TbydGS3GDwzqZ/6EmfFrkLcPXcTDpQZ49b5AzthxrkN49af/7cJHzmuZUnqWtYXw7oaff1uS4G6bptjz/+d3xtn9jfGHpGicj1autN4enxoegxmTeyLOMe17JEFm/HuSiOX4sOD8ITj3kZ+XrenGPd8tAm5hcb1aFyfeDw+0bgeSfF1XyTbft0h9z7r4LjNwJ/OyMDFw9NUPRHEMpaa9039U4yxKTXOuJb969tsPP9rnvp3pOTaxf0w0nHN2ba/ROWEe6413Dc9/8N2/MvH2PTaLzmY9V2uOq7ky9OTBmBUhjFu5R4qx51zN2HjPuM6eGLPeDx9UcN18PcfrMO3W40xunt8qLqnkrFHiud18OoRnTHdkS/7i2Rs2oS1zmtZLJ6d1N8ptuat3IMZCxqug7Mu6ocTHdfBg6VGvqx2jGty/ye5Zgrje+dtwIKNB1UbUqMlJ/phUJpxfTWugxtQ4BjXXK+Dcs2R6/bPO4ycOLFHLP5xUT/ncSt3f4Xyn25GQF0Z6gMjEXHCcwhLP03tKzKq7PvJqD3wq/o5sNMoRI5+BUFhMag+sBKlP9+EgHLjbzIw/TzEnPSckSwAipc8iLptr6h/14fEI3r08whOPk79XJ33HUq/vxYB9dXq55BjHkVk7yuMfetq1Dlr9n6vfg6IH4KIk15ASJRxDS1e8RjqNj1r7BsUjegxLyG48/HGcQ9tQtlPU4GSrernoC6nI2r0i05pULLqH6jdMMtRNxLRY15GcOcT1M81e39EibSpzhjXggb8EdGDbnb0ph4l312L2ryvjDbFZiH8hH+OEmpGAAAgAElEQVQjNM4YJ0q3vIfqZXcjALWoC0lE9En/QojjuNXV1dixYwfKy41rZExMDDIyMpxtys/Px+7du9U2ESPdu3dX+0jJyclBYaGRh2FhYejWrZuSdeqcpaXYuXMn5PhSkpKS0KWLMX6L+JFzyj5S5EnQHj16OM8px5Rjq74EBKj2mOcsKytTdWtqjLEpMTER6enpDg7Arl27cPCgkYcyW0XaGxkZqX6WPkpds03x8fGqzVJEBsq2khJjPJQn++S8pliUGS+y3RSDclyROuZxpa+Vlca9j+tx5ee8vDzs379fbZPjyXHl+N7qyjHl2FKkLXJOaZuUtLQ0dOrUydnX7du3O2fiCHepFxpq3KvJ+eS8UiRuck7hLEVEqvA1Yx4bG6vqmvLKM65SV+LrrU0pKSno3Nm4V5Ai7S0qMj6rSB05rjytLkXiIvHx1iaJiZxX4ivFMyckByUXpXj21Tw3JZszDLb/h+0lW3Z2Nv785z+r/0zJNm/ePCxevBj333+/84/SV6Z0ZMl25SsrsH6fccEzy5QT0jCyezxW7y7Cs98bg5RZ5MbvyYv64eM1+/HJWmMgN8uIbjG48cRu6oPXTe+sU/93LQ+elak+rD71zXasc8gsc/uEQck4Z2AytheU4f++MG68zCIy4Z+X9EMAAnDLe+ucH4TM7X88vQcyO0Xis3X7MXe1e5vkJnb6qYaEu9Xlxtese+dpGeiTHIXnf9qJJY6bcXPb+P6dMHFICvYWV+LBT40bFdciHxJCgwLx+w8bxJ25fdqYrhiUFqsExFvLjA9oZpEb1T+dZUg4+ZBrfqAxt990Ujp6JEbgno+3NDrnExP6ICY8BG8u2Y1vHR+6zZ1O6RWP34xMw+IdB/HCL4YIMEtiRDD+7wLjg8ufP9uMPUXGzYhZfjsyVcmaOav2Yf5644OHWU7qGY+rR6WpD6+3vLe+UZv+em4vJEWHYe6qffjMo+5xGbG47viuWJ9XjFnfGsLKLOHBgXhqUj9Iojzx1XZsdsguc/ukoZ1xRr8klWeSb65FpOLvRndHPepx0zvrG+XaA2dmomtCBP63bj8+8siJYekxuGl0N/zrhxynCDCPfe6AJFwwuLP64PGXBcYHRrOIEPjnJfIhIUB9UDKlnrn9jrHdkdU5Ggs35uO9Fe4x750UgbvGZWJ/SRXu/1/juIq0kictXvk11ykTzeOe3jcRlwxPRX5pFe77pHHdpy7OQnhwkJITpqw16954QhpGdI/Hj9sK8Npi4ybILCJOZpzbB0UV1erDm2cRuRQTFowPVu7F5xuMGw6ziEy4alS6Equ3vr+hUd3HLuiDqNAg3OJl29/O7aXkxtzV+/DZOvdcEyFwzXHpWLunGE9/554vpnCXkz36xTZsc4gN8+SXDOuM07OSvI9NXWNw40nNj01N5Vp2fhkeWeh7bHr2+xysdkgEs00XDErGuQOTvY5NA1KicPupGWpXb2PTXadloHdyFBas348PV7nnv4jD34/tgZd+2YVfHVLbPOcZWYmYNCwVB0qq8CcvuTbror7q6Y+75m10fjFg1v3diekY3i0O328twBtL3PMlLTYUD53dG4XlVUqSeRaRBdFhwfjvkt1OIWjuY45N8hSlSHzPIvIpNjwED326GXnF7mPT1aO64KSeCVi2sxCzfzJEgVniwgLVB2xfY9PfzuuNTlGhTeaaCJqbvYxrM8/tjYNlVfj718YHMLOIcJexHwjAk19vxwaHnDC3TxycjPEDkr2OPfKlxS1jjHHrlvc2KDnkWu47IxMZiRFex6bzBibh/EFNj01yrZz9404s2+W+LME5A5IwYXBnfLHhAN5faXzRYRZzbBLjOf399c4vsMztt53cDQO7xBze2HRRlnqC79VFufgp2/iAbJbTsxJxybBU/OmTTThQ6v5U1nXHdcFxPRLwS3YBXl7knoci2v56fl+fY8+j5/dBfGQI5qzai/nr3cet0T3j8dtRaUrm/vMH93sb+eLn7xMlrsBfF2xxfuFjtvfyY1Iwtk+nJu+LJJq/e2edZ3rjL+f0QkpMmNexSYTjlBO7Ycv+Ujz+lfFFnlnkC71nLxmgfnzqmx1Yt9cQBWa5cHAyzh6QrL64fORL97oi1Z+9pB92HazAzM/dr2Ui/uVaJg8q/vuHHOcXKOZxzevg5xsO4AOPfOmbHIk/nNZD0l99kep5Hfz9Kd3QLzUGL/2Sq4SjazmzXyIuHpqKA6VV+JO3a5kjX7xdB81xzdvYlB4XigfHG/dUf/xoEw46vmgyz21eB99dtgdfbjakiFkuHdoJt56UhvqqQ6hbeGKj2IWf9BICgyNRve1NVO/8yG17UOo4hGVNQfmSe1Ff6s44uPf1CE0/A9UHlqF67WNu9eqDYxB10vMIQB1Kv/1N43MeOwuBEamo3v4uqnd86H7O5BMRNuA21Bxch6pVf3GvGxiGqJNfU3KmfMVM1Beucc+nzN8gpPsFqC3chMoVD7q3KSAEUSe/LroOZd9dBdQbX76YJfyY/0NgTCaqdsxBzfZ33LYFJg5H+OB7UFO2F1WLb3c/bmAEgscvFv2Cffv2OWWXuVNCQoISRSI+TClibhMRI0JMRIopPZztCQ93ijQRJiLTXEtqaqoSJAcOHGi0TIuIHhFIpuzyDICIHpFF0h5Tkpn7iOQRaSVSypRZ5jYRbV27Gg8T5ObmNnpSLzk5WQkdEUDy8IdrMdskv5PPrJ5FBF1wcLCSkKZgM/cRqShiUESWKbvMbab0kp/37Nmjlk5yLSIO5dwi0TyL9EX65K29wkBYSFtMMeoaN1OeNhVzEZyen68lZhI7Ka7Szzy2yD8Rat5ywqzrLZekvvn5X+JmCk7zuJKDkosiY6XNrkXEaa9ehkQ2CyVbo3Sx7S8o2bKzMWvWLMyYMUMN5lK8SbapU6c2SpLZs2c3GmQ7SibJDWHfBz7vKN1hP0iABEiABEiABEiABEigRQS6xQRi5uhIRBd8hMS9s1tUhzu1jsCezGdRHW7MGmEhAR0JnHiiu4AX4Sli13zaUMc+mW0Wkcpy+AQo2Vr4JNuSJUsaUR45cmSHXqx+2N++V+vFuJb0+HCkxYVjd2EFcg81XkNsVEY8tueXYn+J+xMHsg7RMd3j1TSwlbmNF/jvnhCBlNgwNTXKc2CS88l59xRWYJeXc5pTJqSuZzHbm1tYgd0+2itdXJLj/s2RHMesuyznEByzDZ2HT40NQ7eECOwvrsT2Aven/WQnmX4pa3os3uH7uE0xlGP4qitPDHo+2SX7D0mLUWudrd9T5JyqaDZYvonv3yW2ybg1dU6JweZ9JTjk8S1weHAABqfHqTXn1uYZj7e7lsxOEepJtm0HSpFf6p4T8tTH0K5xPtskcZVvK71ziEBaXBi255epJ8Bci5lrMg1sRRO5tvNgOfKK3Ndfkm/v5emupTmH4PEgCcyY7y2qRM7BxjEf0U3aa0zv85WHuwsr1ZNwnkX+bprLpZW7Cp3TGM36naNDkdEpUk3X2pbf+LgyDUqm0RxuHhaWVTmnpbm2WZ5YkKlVG/KKUVzp/i1xVGggBnSJRVF5lXOakWtdeTpG2rTOS7707BShnmTbsq+k0RMHzeWLMPSdw83nS3Nj0478MuzzkWtNjU2Sw0tzCtUUSNdijmtNjQNSY0kTY4iMwVLfWz6t2HnIOS3N3J4SE4ruiZHqSTZ5+s6zyJOcsl7c4ebLwdJK55RY12MPTY9Va8Kt31OMkir3fOkUGaKm7fvKlz7JEYiPDDvsNsmU5rWOqWXexqat+0tRUOZ9bJI1rMzptq515WliWY/O2zWwqXErLT4c6XHhyCkow95i93FLniAa0T1OTRuUZQY8S9f4cHSJC/c6NsnvZbuvsemYbnFqCrm3a1mXuDB0jY9AU2NTc9eGpsamAyWVyG5ibFqdW4gKxxIPZp+To0PRo1PkYce8uKIaGzye7JJj906KVFPOZQmCwgr3J+QiQgLVE+aHe11u7r5I1ndb5eV6lJEQjs6x4U1ey3y1aWRGvJpW3dTfa1Njk1z/vN1TSR5KPjZ179PU2CP3cE1dB1fsLFR/P67FHJt8XQfNsclrrsWEIiMx0lLsvF1zRqWF497RCQguXIyYjX9o9DcZmDBUraNWU7AW9dUe1/3gaIR0GoLqvT81qhcU3Q2BUd1QV7oTtSXuT2XLziEpJ6q11GrzlzeuG9sLgREpqCncgvoK96dqEBiCkORRqCvdhdoS96ds1XFlamZAAKr2/tRoDdSGNnmvq6aa1tWh5sCixhyiMxAUlY66khzUlro/BYqAQIR0Ph61ZftRV9z4qfhDw+ehPiRBPT3kee8vUw7lySSZVuj5hJY0Qp78kieTvG0zp3V6e6GYHFOOLVMCPZ9yM19EJtNAzWmMrh2Wp7TkCbCmjuurvYfbJpnaKU9MydN15tRW1zbJU1oidrwxNPsqjLytc9dcm+S43s5pxsbbceWex3wJi6+4yT6tba95XMkTz6fNhIfZ16b4S754PrFn5pIcX/pqTos1GTeVh1Jn+PDhbn8TkiOSO57HafSHo8EvzPzQoKl+2UTbSzauyeY7L1//ZSee/K7hgilTst6+Zgi6yYe04gpMfGEFSqsbbpROljVnJg1Q6yhN9ZgWIdNBLxxqPOZ749tr1fppZpGpRnNuGK7Wlnrt152Y5VhPxtz+4hUD1do6sjDvhNlLcaCs4UOarDXz78uMKRM3y1pLjrW55OekqBDMuWGYWsxXbuque8t9qsZtY9JxzQnGmgO3vLsWP29vaFNCRDDmThmmpl+6rl1ktmn25QMxqrux/tzFLyxzmz4yvGsMXvzNILWrrPHhOnVT1u358PqhSI4NV4LiytfWuE1ndF3n5q456/GlY702OZbw/+D6oUiJDceVr67EBpepvANSo/DG1UPUOb/fUoDbP9zoFtinLsrCmN6J2FVQhkkvr3KTNa7rrv11/mZ8sKphmp58MHt78mD0So7Cz9mHGk0Jlal/p/cz1oK79o3VWOkyJa5np3C8e+0wtY7ZL9sP4eZ33aeT/vXcnjh7YAoKSisx8YWVbrJmTM94tZaQlH9/vwP/+dlYi8Msr145UK0B5S3X7j8jAxc51rmZ8tZatTaMWbrEhODDG4YrGSm/l+2u5Y/juuPSEel4b1ku/m+h+03qfy6XNbbiIIuVT3xhOXYXNXxIlrW3/nP5QHUoWeftB8e6XfJzXHgQ5t4wTEmpNbuLcfUb7tM0zPX/JJcueXE5th9sEH/yweKlK41cWrBuH+79xH1q8nOX9MfxmfFqXa/LXl7hJjhk+tkrVw1Wde+ZuwFfbGqYCiNr70guydp1IkAvf2W1c70q2X/ioE544BxjCrHkmrkeofzcXK7Jelan9DHW67j69dVukkLWMXtz8lAlI695Y7VzHSbZV+Tb29cOVR/ufsk+1Gia3iPn9caZA5KRL2PPiyvdRLI59shxnv0mGy+5TCOTD6GvXjVQfYBenFOIqW+7jwMPndUDE4Yaa7R45ousk2iMTUHqQ+ONHnXvO707Jh2Trl5UMeE/y5xrR8mxjusRj39dauTwm4t34Ymv3T9MPX/5ACVVlu8sxPVNjE03v7sWv7iNTUGYN2U4osND1NS2a/7rnsM3nZSGKSdl4LO1+/Cn/7nni7RH2iXry13y0nJkFzTk2tD0aLx8pZEvd8/dgIUu+SLTtz+8fohaH0o+jF7x2hq36Yzmmm1y8/ubV1Y61/ySY8n6i687xiZZJ0vWU3ItMqV5jGPtwKteW4V1jnWaZB+Z+vrmNUPV7jM/24QPVzdM8ZMpb29NHoReydFqrbGLXljpNj3t9L7xeOxCg7/n2NRLxqbrhqs8/CX7IG5+z32aqkxbHj/QWN/luv+udps63iMhDO9dP1ytfXnh8yvcpmGfmBnnnML30k85ePYH9ymsL17RH8O7xWPpzkJM8Yj53ad1x+UjjTV9pr691rn2pfycHBWMeTceo6ZXvrs0F4986X1sElF84fPL3abVytqWz19hjE1zVuzBw441H80YzL5M1uCK8zo2TTm+C2462VhSQdY7Ndc0lZ/lixsZ1xKjwzB/7T7c55Frz13SD8dnJqix6dKXlmOrY51VqTs0LRovO8amL9bvb7T8gSwBcUJmAh74eCP+51grUurJFMl3rxmCjKQotc7hpS+vchPJ5w1IxF/OM9bpa2rskfVDPacmy7TkcVlJag3Wi19c6TY11nVdx398uRVvONYPlfPI+PLfqwepaZBN3RfJvte/uQbLXabrdo8Pw/vXDzO+kPMyNv15fE9cMCQFspbhhOeXO9dIlGMd3yMWz11qxPWVn3Pw9PeeuTZATe8WySw54TrtVtaMfe7S/upFQxc+v8y5Bqsca1T3OMy+3Linen9ZLv7m4zrobeyZekIapo4xprl7Xgdl7UwZt2QN10/X7sP9vsam+npc8sJyZLtcB13z5fN1+/FHj+mk5nVw095iXPn6Wrex6dJhyfijYy3he+dtxIKNxlqSUuQ6+P51Q5CeEIkftxaodUBdy7OTsnCiY53K4oUTUVew1Lk5MPEYxJw+V/1cvfsrlP1wjVvd8BOeR1i3s1Cy9M+o3fpSw7aAIESO+x9CEgegujQPZQvGAjUNU30DUs9E7MkvqP1LvroYtQdkOqWjRPdC7NkLERAQpNZGK/3WWJ/NLKEjn0REz4tRW1GI4vmnIKCqoa9BKaci+pTX1K6la55FzTr3aaoRYz9EaPJI1FYVo/hTqdtwHxiccjKiTnnDaNPXl6N2f4M4rA9LQdx53yMgKBzV+xeh7OtJbm0KHvIgovrdoF7GUfz5eKB4k3N7QMppiD3FWI/OdZ0sc4eePXuq6ZdyXdm8ebObTJPfy3aRGVu3bnUTdDL10pxW6LqulxxXhFWfPn3UVEfXNdfMc8q0QXPNsC1btriJNpFZvXsbU49d11yTn0W8yXFFyIhYkva6SkOZbug6XdR1KqTUleOKJBJ55zk9U6ZXyrRNKdJXc70w+VnWGZO6Intc11yTbfI72Sb7iDTctGmTm1SUY8qxpezdu7fRVEiZBilyT6aoukotaaccV9otUmrbtm1uMZe16WSqqUgmOafrtFpha07NdF1nzzPm0kfpq2uRtfRk+qsUzzZJPKVNIgUlJyR2rsVcs62pXJL9xQnIunauxcwJkXcSV1d55rk+ndTjdFE3fLb+wfaSTaLPt4v6/huQhajlA7bIlgsHJzkX4ZUa8sFm7uoDahHhlOgQ/GZUw6KfsmDxj9sK1bfy8jSN+aFb6omWe+WXXBwqr1Jv6LtyRApiI4xFMqXIwtnyVJTceJ7aKx4D04yLixRZ9Pad5XvVh9rEqGBMPs5Y48Asry3KRX5JlXrT2KXDOqsPAGaRBYu/3nJQvRWvf0qUcxFkc/sbi3erp4nkg4ys4ZQU0/C2JhFX8lSUXFRGZ8ZhqONFAVJXnuJ6bcke1abY8GC1zpjrix3fXJyLvSXV6rgTPRjK02Ei0uoDAtE1OtD5ogCzTW8v3Y09RZVqTa4Jg5LUzaAUET0v/Zqr3popIvDa49Lc3oa1aMch/OoQjsf1iMOxjqd8pK680OKTdfnqTZJpMaG4dISx8LJZ5q7Mw/aCCvXkyfh+nZwvh5Dt8kSOvGRALjIjusc6F0qXbSKJZB2o4gp5g1kwJh+b5vbWPFk3SdbekSd6juka41yUX+ruLSrHh6sOqLefpcaE4Tej3NskwmDT/nK1QPu4vgnqA41Z5Gb/+62HUOPItVMdgke2ywe8V37dpZ7AE/6euWbWlbYPTI3EOMei/FL36035ah0tyf0xveKdLwKQbfLUzX+X7oW8dS0+IhTXHJ/u9s3wG4tzsa+4Sr15cdKwzkh2ySVZmH/hxgK1bpC8bOGcQSnOvkgOvbZ4t4prXEQIrj0+3e3NgrKAu6ypJCJK1igTSWMW+Tt8dZFRNyY8GNef0NWt7rtLdyPXkUvnD0xSstwssnC2LGQvb3/NSAzHRIcQN3KtFi/9ahxXxM51HrkmT1EszS1TN5THpEcpiWMW+Vt7+dfdkCdLokKDce3xac6FpGVskLV5fOWLM9e85It8EJ7jY+yRc3+6RvKlTL0NT9Z3ynIs3i7bzLFJ8nBwWjTc8sUxNskTWRFhIbhqZKr6+/KWa0O6RGNslnHDJ0XqvLN8n8+xaeH6/Vi3zxhLT+4Z73xRgNR1HZsGpEar9QZdy+uLctXTZ5LDlw5PcS7K76y7+aCSpPLiD1MOybYfth7Esl1FKg9kIW95mtgs8rf22uI9znyRccv1jZFvL8nFnuIqNfZcMChJvRCkIV9KMX9DvsqXHonhzi9QZLuInpd/2Y3SKu9j06/bD6oXoMgHgOMy4pwvrjFz7eVFe5RUkDFEcs31TazuY1Oi8+UQUldeCPDx2gPqjZDpsWFu41pzY5Pk2qKdJeraNCQ1wm1sEiH58q+5KCyvVm8+njwqTb29V4pIlfdW7FNfAHWOCcNVHuPWgnX7sWFfqRqbx/ZOcL7URuqu3l2Mb7ccVOPhoNQonOaSS/K39Nqi3SgorVbnkjW/zBfMSF25Lq/ZU9qCsSkE18j1yCWZZC1QeaJK3hbpOa7JFz9fbjJySZ4gPMchGs3qTV3LWjM2eebaT9sOqi89JCc8x7X3l+/BrsJK9bZrWXsuw/FyCGmTiLZP1uarN0l2iwvDxcMNWS7FGF+8jz2yXaS5fHEkZWS3WOcLZuTn3Yfk3ma/yqUusWG43OMa+fGqPLXuo1yPzuzXybnYv9Rt6r5IrkdyjVS5FBqE347q4va2T9exScTSyS7XMnla+d3lcs2pRVJ0KH57bMP9lpxX1tTbnF+JANTjlJ5xbrnWMDbVIDEqxO2+SZ5WlrVhJYfly0W5p3K9f/l64wGs2lOC4GbyRV5wNX5Aw8Lj0iZZH1aelpPr4MVDO6sn9szS5NhUXavy/3Cug1v3l2DBBuP6KmPTBMfLl8zzvrdst8onGdfOH5SEbi7j2qpdhfjBsT6grC0qT+mbpb6uFhWbXkB95UEgNB7hfW9AYFDDm0LlibXavd+rNRWDO49GSOpoZ92K7PdRX7QJAYGhCOp6HkISjPX9pNSU5KI6+y3U11YiICINEVnXNpyzvg6V659DXeVB9YbQ0Kypbm8Krd63GLX7voG0LShpFELTxjUct3QvarLfQF11OQIjUxCeNcVlFAAqd3yMukNr1NtOg9LOUk/dOdtUfgDVW16BvH00MLwzwvvd2FBX1nRb/2/UV+Wr9eiCe1+L4HDj5UxSqg+sQO2eL1BXV43A+KEIzzjXua22ugzVm55HXW05ENYFkVmT3doka2mJDJL7S5E75osCZCd54kzWUJNtIlJEtpgL5MuTSeY6ZiKUzKV/zIOLzBHRI/uL/HGdAifyyBRI8tSO+SIAqSvnMs8p9/4i71zfKCmizPXlBeai/FJXRJvZJjmfnNe1SF2RNnI8aa9rXRFX5pNyIqVM6Sf15dogbTLfTOrZJjmn8PB2XGmrnFeOYTJ0bZNZV34nbTJfFCA/y1plEhvhIOxd3/AqUst88YSISPNFDCq/a2rUmnlyTqkj7XUtUk/aK9tF+rnGXI5rvrxA2uJ6XDmGcJDje4ur2SbZ5lnXzCXzzaOuL0yQ47rmhGebXBl69tXsFyWbW4ht/QMlm7xpqqICM2fOxPz581UyPPDAA5gwYUKLEqMjv/igRQC4k2UC5uPp3h4/t3xwHoAEjgIB801e3qZXHIXT8xQkYJmA3IjLBwjXpwQsH5QHIIGjSMCcouVtWthRbAZPRQKHTUAkl4gTb9MMD/ugrEgCR5EAJdtRhO3np6JksxggSjaLAFldfasmH/Ao2ZgMuhKgZNM1cmy3SYCSjbmgOwFKNt0jyPZTsjEHdCdAyaZ7BNuu/ZRsFllSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusAyjNSRMAACAASURBVJRsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA5RsFlFSslkEyOqUbMwB7QlQsmkfQtt3gJLN9imgPQBKNu1DaPsOULLZPgW0B0DJpn0I26wDlGwWUVKyWQTI6pRszAHtCVCyaR9C23eAks32KaA9AEo27UNo+w5Qstk+BbQHQMmmfQjbrAOUbBZRUrJZBMjqlGzMAe0JULJpH0Lbd4CSzfYpoD0ASjbtQ2j7DlCy2T4FtAdAyaZ9CNusA+0u2Z555hns3bsX999/v+rUzJkzMX/+fKSmpkK2ZWZmtllnj8SBKNmOBFV7HTMkJATyAa+4uNheHWdvOwwBSrYOE0rbdoSSzbah7zAdp2TrMKG0bUco2Wwb+g7TcUq2DhNKyx1pV8l26NAhTJ8+HdOmTcOIESOwdOlSzJkzRwm3tWvXOv8tN7/+WijZ/DUy+rSLkk2fWLGl3glQsjEzdCdAyaZ7BNl+SjbmgO4EKNl0jyDbT8nGHDAJtLtke+ihh5RokyfW5Mk1KSLdsrOzMWvWLMyYMQPx8fF+GzFKNr8NjTYNo2TTJlRsqA8ClGxMDd0JULLpHkG2n5KNOaA7AUo23SPI9lOyMQf8QrJVVFSo6aETJ05Er169Gj3VJtJNRBslGxO2IxOgZOvI0bVH3yjZ7BHnjtxLSraOHF179I2SzR5x7si9pGTryNG1R98o2ewR55b0sl2fZJMGyhNr8uRaXl4eJk+erP5tTiOVKaTysz8XPsnmz9HRo22UbHrEia30TYCSjdmhOwFKNt0jyPZTsjEHdCdAyaZ7BNl+SjbmgF88ydYRwkDJ1hGi2L59oGRrX/48u3UClGzWGfII7UuAkq19+fPs1glQsllnyCO0LwFKtvblz7NbJ0DJZp1hRzlCuz/JpjtISjbdI9j+7adka/8YsAXWCFCyWePH2u1PgJKt/WPAFlgjQMlmjR9rtz8BSrb2jwFbYI0AJZs1fh2pNiWbxWhSslkEyOqgZGMS6E6Akk33CLL9lGzMAd0JULLpHkG2n5KNOaA7AUo23SPYdu1vV8lmrr22Zs0azJ49G7IGm26Fkk23iPlfeynZ/C8mbFHrCFCytY4X9/Y/ApRs/hcTtqh1BCjZWseLe/sfAUo2/4sJW9Q6ApRsrePVkfduV8lmgpW3iL766qtunAcNGuT3bxaVBlOydeQ/j6PTN0q2o8OZZzlyBCjZjhxbHvnoEKBkOzqceZYjR4CS7cix5ZGPDgFKtqPDmWc5cgQo2Y4cW92O7BeSzRPavHnz8PDDD0MH0UbJplvK+197Kdn8LyZsUesIULK1jhf39j8ClGz+FxO2qHUEKNlax4t7+x8BSjb/iwlb1DoClGyt49WR9/YLyZadnY1p06YhLy9PsX7ggQcwYcIELbhTsmkRJr9uJCWbX4eHjWsBAUq2FkDiLn5NgJLNr8PDxrWAACVbCyBxF78mQMnm1+Fh41pAgJKtBZBssku7SjZzTTZhPWvWLMTHx2uHnZJNu5D5XYMp2fwuJGxQKwlQsrUSGHf3OwKUbH4XEjaolQQo2VoJjLv7HQFKNr8LCRvUSgKUbK0E1oF3b1fJZnJ1fQGC/G7y5MnqyTYdCiWbDlHy7zZSsvl3fNi65glQsjXPiHv4NwFKNv+OD1vXPAFKtuYZcQ//JkDJ5t/xYeuaJ0DJ1jwju+zhF5LNE/bSpUsxdepUrslmlyy0eT8p2WyeAB2g+5RsHSCINu8CJZvNE6ADdJ+SrQME0eZdoGSzeQJ0gO5TsnWAILZRF9pdsnmux2b2a/z48bj//vshN77+XPgkmz9HR4+2UbLpESe20jcBSjZmh+4EKNl0jyDbT8nGHNCdACWb7hFk+ynZmAMmgXaVbK7TRGfPno0RI0ZoFxlKNu1C5ncNpmTzu5CwQa0kQMnWSmDc3e8IULL5XUjYoFYSoGRrJTDu7ncEKNn8LiRsUCsJULK1ElgH3r1dJVtH4ErJ1hGi2L59oGRrX/48u3UClGzWGfII7UuAkq19+fPs1glQsllnyCO0LwFKtvblz7NbJ0DJZp1hRzmCX0g2cw02V6i6PNlGydZR/hTarx+UbO3HnmduGwKUbG3DkUdpPwKUbO3HnmduGwKUbG3DkUdpPwKUbO3HnmduGwKUbG3DsSMcpd0lmwi2Z555BrNmzUJ8fLxiaq7TNmXKFEyYMMGvOVOy+XV4tGgcJZsWYWIjmyBAycb00J0AJZvuEWT7KdmYA7oToGTTPYJsPyUbc8Ak0K6SraKiAjNnzsTEiRMbrccm8m3OnDl+//IDSjb+MVklQMlmlSDrtzcBSrb2jgDPb5UAJZtVgqzf3gQo2do7Ajy/VQKUbFYJsn57E6Bka+8I+M/521WyyYsPHnroIUyfPh2ZmZluVORpNnm6bcaMGc4n3PwHW0NLKNn8MSp6tYmSTa94sbWNCVCyMSt0J0DJpnsE2X5KNuaA7gQo2XSPINtPycYcMAm0q2Tjk2xMRBIAKNmYBboToGTTPYJsPyUbc0B3ApRsukeQ7adkYw7oToCSTfcItl3721WySTfmzZunpoVyTba2CyqPpBcBSja94sXW8kk25kDHI0DJ1vFiarceUbLZLeIdr7+UbB0vpnbrESWb3SLuu7/tLtmkaXy7KBPSzgQo2ewc/Y7Rdz7J1jHiaOdeULLZOfodo++UbB0jjnbuBSWbnaPfMfpOydYx4tgWvfALydYWHWmLY8hTdTk5OZg2bVqLD8c12VqMijv6IEDJxtTQnQAlm+4RZPsp2ZgDuhOgZNM9gmw/JRtzQHcClGy6R7Dt2k/J5vEk3eTJkynZ2i6/eKQWEKBkawEk7uLXBCjZ/Do8bFwLCFCytQASd/FrApRsfh0eNq4FBCjZWgCJu/g1AUo2vw7PUW3cUZds8kZReZvomjVrmu3ooEGD3NZqa7aCxR34JJs7wNq6ejz5dTY+Xr0PXWLDcMspGRjTK9G508/ZB/HoF9uw81AVxvWJxz1n9ESn6DC1/e0lu/GvH3aioqYOlwxPwR9O64mAAKNq9v4SPPnNDvyYXYSszhH4w7ieGNEtVm2rq6vHowu3Yc6q/YgODcC0k7tj4rAuznMu3lGIJ77chi0HKnByr3j84bQMdE2IVNt3HyrH419m47uthejVKRx3nd4TozLinHU/Wp2Hp77JQXFlHS4cnKzaGxRoNCqvsELV/WbLIWQmhuOeM3tiVHejbn19Pf7xVTbeXb4XYcGB+N1JXfGbUenO4y7fWYQnv96ONXmlOLFHLH4/tgd6JUep7QWllXj0i2ws3HQQ3eJD8cczeuH4zHhn3YUbDqjzFpTX4twBnXDfmT0RGhyktheWV+GRz7Px+cYCpMeF4I9n9MSJPQ3+q3OL8NQ3O7A8twQjusXgtlO6Y1CawVDKv77fgdcX71H//u2oLrhpTIZz2/dbChTjPUXVOKtfIu49MxMx4aFqe0V1Lf62YCs+W1+ATlFBuOf0TIztm+Ss+/LPO/HiL7morQOuOCYFt41teCvwxr0lmPXNdvy6oxhDukRh+tgMDOvawP/VX3bihZ93o6q2DleMSMV0l7q/bj+kcmnHwUqM65uAu8b1QHJMuDpvbV0d/u/zbZi35gDiwgJxx2k9cM7Azs42vbMkF8/9sEvl2qRhKbhzXEOubT8guZaDH7YVom9yOO48vZcz1+QA7y3NxT9/2IWy6jpcNCQZd5/RC4EBAagH8NgXW/HByn2IDAnCzWO64tJj0pznXLqzCH//chs27ivHSZmx+P2pGchMjlbb9xVV4PGvtuOrTQeRkRCGP57ZC8e65OFna/fhH1/vwKGKWlwwMAn3ntkTwUGBqu7yXUV48qtsrMkrwwk9YnH7KRnom2IcV8qsr7PxzrK9CA4KwHXHdcG1J3R3bluxqxCzvtqOVXllOK57DG4/NQP9UmPU9iLJpS+ysWBDAdLiQlVcR7v8LX+96YDK0/yyWozvl4D7xvdGRIiRhyrXvt2B5btKcEy3GNWmQWnGcaX8W3JtSZ76O/ntyC646eSGXFu7uwhPf5eDJTnFGJYehdtP7YEh6UaebsgrxqxvdmBRTjGGdonC7R758ork2q+7UVNbr/LltlN7OM/5i+TL51uRI2NP33jcMy4TnRz5UlNbp3L443X5iA8Pwh/GZmC8S768tTgX//7RyJdLhqXgD+MyEeAYnLIPlOLJr2VsKkS/zhG4w2VskpO/szQX//ohF2XVtZg0NAV3ndETjmENTY1N9fXAI19sxZxV+xAVGoRbx3TDxcMbxrV5q/Lw9Lfm2JSEe87o5TI2ybi23TE2hakcdc2nj1flYZaMa1V1uGCQkU9BgUY+Sb68tTQPocGBmHJ8Gq4+vptbvjz51XasduSL/L1mpRhxLSyvxiOfb3OMPTJuydiT4Kz71cZ8PLZwK/LL6nB2/wTcd1ZvhDvyZVVuEZ52GZtuP6U7BnoZm4T51aO6YOrohhxes7sYT32zHct2lWB412iVa4Md+eI6NiVFBeGucZk4LathbPpx20E88vlW7HaMa5LjcZHGuNaisemX3ah25JrkqVk27S1R+f/z9iIMSo3E78dmYrjjerU4p1Cdc3tBJU7tHa/GrdS4CFVVrp8S83mrDyAmLBDTT83A+YNTnMd9f/kePPtdjmPs6Yy7z+ipxh4pOwvK8I+vt6trWe9OYbjrjN4Y2d1xjayvx+MLtznGpkDcPLorLh3RcD1yHZtG94xTY1OPJON6JHko18/3V+xFeHAgbhrdDZePbBjXPl2zT12bjbGpE+47S/LQyKUDxRV47Mvt+NIxrsn187geDdcyiZvkWkhQAG44Pg2TPXJt1tc7sGpPKY7LiMH0U3sgy2Vce1rydPleyOX4huPTce0JRp6WVFSrcWv++nykxoaoa7brPch3WwoU433FNRjfv5O6lkWFhai66/ZILu3Akp3FGJom16OGsUe2v/hTDl76dTfq6oErR6Ti1lMaYv7TtoPqGrnrUBXO7Jeoxst4Ry5V1dTib59vw//W5SMhPAh3nd4DZ/RLdsa1qfuirfvlGpmDn7ILMSA1CneM7eHMJTmA69h06fBUda0zx6ZFOwrVNSnbR649+sVWzHXkmtwPTBiS6myTjE2SM1vz5b4pDneOy0R6vJGnS3IK8cTCbdh8oAJjJF/GZiCjkyNfADz+xTa8v3Kvug7eNDodl7nk2ierjbGnqNIcexrGLdfrYI/EMNxzek8c65Ivcp17e1keQoMCMeXENFx9XMPYZFwHt2FNXjmOd+SL63XwaUeuyXXw+uPScI0jX6Q/X6p7qm1qbDpngNzf9HKOTcUyri3chvnrC9Al1rincr0O/us7uZbtUcx/OzIVv3O5b1Jj07c7sGxncaOxSc47+4ccvLZ4j7oOXjUyFTefbORTZXWNypdP1xcgMcIYt07v1zBuSb7IWLursApnZCXi7tN7IDHKuI/evK8Es77ejp93FGNgSiTuOK1h7JHtbyzahf/8tAuVNfW47BjJl4b7scU5RXhkwWZsP1jV6rFJck3yZVt+hap757ge6OIY13IK5BqZg++2HkKfJLmn6omRjntladOcFXvw9Hc7UVpVh4mDjWtZoOM+W+4l/vltNlbnlePU3nFqHEiONfJQ/b2v+geqt74OBMcgbMDtiOx1sXNbzb5fUbbiYdQdWo2glLEIH/onhMT3UdsrdnyMihUzEFCVj+CMSYgY8VcEBhljf015PiqWP4iaXZ8AkRmIGjkTIaknO49buuIRVG95CQgIRGi/aYgceItzW/WBZShf8VfUFyxBQNIJiBh6H0I6DVHba6tKUL7sftTkzAUi0hB5zMMITR/nrFu1+0uULbkfqNiD4G4TVJuCQo17udrqUpQt+RPqds1DXVgKokZI3TOcdcvWPI2qjc8B9XUI6XM9oobe03Dc/NWoWPU31O//CQGJI4w2JY9U2+tqa1C2/GFU7/gA9RHpiBxyF8K7nt7At6QEe/bsQWVlJUQEdenSBcHBwWq75O3u3btx8OBB9TvZJvuoHK6sRF5eHoqLiyFfRsk2kaFmkRlde/fuRW1tLTp16qS2O2NaUqK2lZWVOc8pDxaYxTxnYGAgUlNTkZDQcJ8hbZHzynHl92lpac7xsLq6WvWlsLAQ8iWv1JUvGcwidc02mXXNbdIWqSv/j42NRUpKiuqXWeSc+fn56lydO3dGUlLD32tRURH27dsHOb8cV87rWSjZGiGx7S+OumTzJP3MM8+ge/fumDBhgnNTU28dPZKRomRzp3v33A1KDrmW2Zf1x6iMeKzcVYhr31zntk0+lL55zTB8uGIPZn6+3W2bfPj78znGBfHs5xZjb0mN2/aPpgxVsuyuORvw5Wb3c/713F44e2BnbNtfikkvr3KrJ+Jq3o0j1O8u/M9S9aHbtbx37RAlvBas2497P9nitu20Pgl4YmI/9btJLy5XNxSu5a3Jg9SHzr98uhlz1xxw23bv6Rm45Jg07C0sxzmzVygpY5ZOkcFYcMtI9YHpyldXYv3eMre6r1w5AEPS4/D91gLc/sFGt23HZ8TiucsGqt/99rVVWJtX6rb9xSsGICslCuOeWYLK2oazRgQH4OvbRilBJ4Lt+Z93u9WbckKaEm1Lcwox5W33uA1Nj8bLVw5W+099ex3kg6NreXZSlpJ7ry/ahSe/2em27aoRKUpE1NXXY9zTi1BYWefcLgJzwU3D1Q3jW0ty8fhXOW51Lx2WjD+e2Rtrdhfh6jfWum3rmxyBt68dpn53+/vr8f22Q27bH5/QB+OykjBnZR4eXpDttu28AZ3wl/P6qt+d868lyCuudts+94Yh6J4YBZETD813r3tO/06YeX5fPPDJJvUhyrU8ND5TfXDZdbAMFzy/0m1bSnQwPrt5lPrdZS+vwOb95W7b37hqEAakxeCrjQdw57zNbtvG9IrHUxf3R35JJcb/e7n6cG6WhIhgfHGrkUuPf7EVby3f51b3D2O748pR6ThUVoUzn1uGGpe68sH+q2nHKlkz+fXVWL2nxK3uC1cMwDHd4vBL9kHc/N4Gt20ibp+/YhDKqmoa5Zp8OP/y1hGICA3GCz/tVILTtdxwfBf1AaO6pg6nPr0I5TUN/QkNCsCXt45UH3pOe2aREt5mCQ4EFt4yErERIfjv4lz8/Wv3fLlieGfcdUYvJf0m/9c9X0TWv3WNI18+WI/vt7rny98n9MHYrCR8sHwP/vqF+9g0YVASHmpibPp4ylCkJ0Ri7so8/MUj187tn4iHz8/yOjZ1TwjD3CnHqO7dO28jFmwscOP0l7Mzcd7gVB9jUzyemNhf7T/pheXYVuA+Nr09ebCSr19s2I97PnIf107uGYdZkwYo+fPuCvd8ufu0DCVVCkoqcZZHronAXujIl6tfX4U1e9zHnhcu749jusfjx60FmOYxbo3qHovZlw9EaWU1Tn92qe+x6bsdeP4X97FJJNENJ3ZDZU0tTnt6sVu+hAUF4KtpoxARGoQb31qLJTuL3Bj+c1I/nNAzActyCnGDx7g2OC0ar141WEn6059dgsKKWpdck7HpGCREheLNxbl4wiPXLhuWjHvO7K3GtTOeXYyD5Q115e/ps6nDkF9WgyteXe3WHvly573rh6vf/eHD9fh6i3sePnJ+b5zZPxmfrN6LBz/b5lZ3fL9E/O2CLPW7C2YvVR+6Xcv71w5Gz+Ro3P/xRvWB3bX8eXxPXDAkRcm5CS+4j02p0cH41DE2PfTJRny8zr3uA2f2UF9keR2besbjqUlGHl7xykps3Od+LXvtqoHqy51HP9+Cd1bsd2vTnWO7qy+jDpZW4ax/uY9NceFBWHjrSCXw5AusN5bkudWdfko3XH1cV1z3xmqs2O0+bpn3IIt3HMLUd9a71RuWHo2XrhwMEe2nPr1YCUyzhARCjT3R4SF49ZddeOo792vZ5FGpuH1sJlbsLMR1b7lfI0WIvXG18eH6lnfX4eft7tfIpy7KwpjeiU3eF0kujX9uCQ6UNtz7iFL99HfDkBIbgfeX78bfvtjh1p8LByfhwbP7QETv5R651jMxHO/fYOTanXPW46vN7rn2f+f1xlkDkiFi75KX3fO0e3wo5t44AjvySzHxRfd7qrTYUHzyO+Oe6oGPN+J/Hrn24FmZuHBoKhZu2I+7PcaeMZmxeOoS4/7l8pdXYJPndfC3gzCgS4zXsemu07rjipHpXq+D8mXJF7eOUteypq6DP2wtwG0eY9OxGbH4t+Oe6po3VmOVRz49f8VA9cXbc99txwu/GF9MmsUcm8rlOvjsUvXFjFlkbPpy2khEhgbjxZ92qi/rXMv1x3fBLSf3wM3vrsMvHvny9MX9MLpXQpO5Jl84n/nPJSgob8iXoIAAlS/yBeS7S3fjkS/d8+XiIcn40/jeWL+nCFe+7n6NbG5sevSCPjijX5ISe5e94p4vPRLC8KHjWuZtbPrgusHITIqGfIH4p/9tdeNwZlYiHpmQhfySCpw7ewWqXO5beyaG4f0bjGtk8dKHUbf1ebe6ocf+ExE9zkdNSS5KPj0RxtefRqkPS0H8BYsgMqv8x+vc6gWnnoqok18zjvvFeag76J7jEWM/RGjySJQu/ytqNs92qxsy+D5E9v8daiuLUPzRcATUN9w/1gdFI27iKgQEBqP4y4tQl7/ErW7kKW8jJOVEVO/9EWXfXuG2LbDTKMSM+0D9ruSrS1B74Fe37RFj/ovQLmNQuvY51Kx9xL1NfW9C5LB7UVdbjaJ5wxBQU9zAISAEMRcsQ1BYHIp+mIr63Z+51Q0f8ybCuoxGeXk5tmxxv1cQOdW7d2+1//bt25VEcy09evRATEwMNm7ciKqqhuuRyKe+ffsiNDRUSbldu9xzX+RT165dIZ/lN292v98NCwtTdaVIPanvWrp164b4+HiIzNqxwz2/RV6JL5Aix5Xju5Y+ffooWeatrsi0jIwMJcekPyIVzSLSLysrS0m13NxceC4DJXJP5GFJSQmys90/M0hbpc2uhZLNDYetf2hXySZPtT300EPqybbMzIZvYCQi8jIEeevo/fff72aYj2S0mpJs//nPfxqd+sYbb1QDV0ct/Wd81VG7xn6RAAmQAAmQAAmQAAmQAAm0E4HHT45E56hAdNswAQH17l++t1OTOtRpCztdgsLO13SoPvlbZ4499li3JonoEylZV9cg5f2tzS1tj7kUTEv3537uBPxWsoktnjVrFmbMmKGs9tEoTUm22bPdv+2Q9kydOpWS7WgEhucgARIgARIgARIgARIgARLoMAQo2Y5sKCnZjixfObqnZJOn9USyEnArOAAAIABJREFUuT4td+RbcWTOQMlmjWu7SjZzWuioUaPcpotKl+RJNplKKqLNHySbL8wd+e2inC7K6aKS95wuavz1c7qowYHTRRuuBpwuarDgdFGDQ3NTsjhd1ODE6aIGB04XbRhLOV3UYGFOTeZ0UYDTRY2cCOF0UeO6wemi1ozPYdROTGxYh/0wqtu+SrtKNqEvMk2mjIpQM6eMmi9HmDhxYiP5diQjxjXZ3OnyxQd88QFffMAXH/DFBw0vFOGLD4xrBF98YOQEX3zAFx/wxQd88YGMBXzxgXFt4IsPDA588YHBQdYx44sPjqS5OLLHpmSzxrfdJZs039sbR2V65ogRxuKrR7qI6JOpn66lpefvyE+yHWnuPL5BQBbdlDn8nouOkg8J6ELAfKS8I69RqUss2M7DIyBjsLzhTN44xkICOhKQt+vJNCXXRcp17AfbbF8C8tbKmpoa9UZLFhLQkQBffKBj1I5Mm/1Csh2Zrh2do1KyHR3OHfkslGwdObr26Bslmz3i3JF7ScnWkaNrj75Rstkjzh25l5RsHTm69ugbJZs94tySXlKytYRSE/tQslkEyOp8ko05oD0BSjbtQ2j7DlCy2T4FtAdAyaZ9CG3fAUo226eA9gAo2bQPYZt1oN0lm7epombvBg0adFRffHA4VCnZDoca67gS4JNszAfdCVCy6R5Btp+SjTmgOwFKNt0jyPZTsjEHdCdAyaZ7BNuu/e0u2eSFB1KmTZvWdr06ikeiZPv/9s4DTqrq+uNn+y5b6b33DiIqdmNUrIDYG0ajpPwxxGgSjRqNJjFGI5aY2EUxNkSwYqyxK1KkFynSe2eBrf/PebNv5s3s7CzszDB7537f55NPZGfue/d8z5lz7/u9e887hLCT9FKIbEnqWIvMQmSzyNlJaioiW5I61iKzENkscnaSmorIlqSOtcgsRDaLnF2LqQkV2XQVm75ZdOzYsf43i5rmGkQ20zxW//qLyFb/fEKPDo4AItvB8eLb9Y8AIlv98wk9OjgCiGwHx4tv1z8CiGz1zyf06OAIILIdHK9k/jYiW5TeRWSLEiDNqclGDBhPAJHNeBdabwAim/UhYDwARDbjXWi9AYhs1oeA8QAQ2Yx3YcwMSKjIplbodtF27drJsGHDYmbUoTwRItuhpJ2c12IlW3L61SarENls8nZy2orIlpx+tckqRDabvJ2ctiKyJadfbbIKkc0mb0e2NeEi2/Lly+Wll15ytozqJNe0A5HNNI/Vv/4istU/n9CjgyOAyHZwvPh2/SOAyFb/fEKPDo4AItvB8eLb9Y8AIlv98wk9OjgCiGwHxyuZv51QkS3Sm0UVOm8XTebQwzaXACIbsWA6AUQ20z1I/xHZiAHTCSCyme5B+o/IRgyYTgCRzXQPxq7/CRXZYmdG4s7ESrbEsU+WKyOyJYsn7bUDkc1e3yeL5YhsyeJJe+1AZLPX98liOSJbsnjSXjsQ2ez1fajliGxRxgIiW5QAac6LD4gB4wkgshnvQusNQGSzPgSMB4DIZrwLrTcAkc36EDAeACKb8S6MmQGIbFGiRGSLEiDNEdmIAeMJILIZ70LrDUBksz4EjAeAyGa8C603AJHN+hAwHgAim/EujJkBCRHZ3Fpsl112mUyYMEHmzp0b1iBqssXMz5yoHhNgu2g9dg5dOyACiGwHhIkv1WMCiGz12Dl07YAIILIdECa+VI8JILLVY+fQtQMigMh2QJis+FJCRLZkIstKtmTyZmJsQWRLDHeuGjsCiGyxY8mZEkMAkS0x3Llq7AggssWOJWdKDAFEtsRw56qxI4DIFjuWpp8pISKbu5JN4Y0bN06KioqM5YjIZqzr6k3HEdnqjSvoSB0JILLVERzN6g0BRLZ64wo6UkcCiGx1BEezekMAka3euIKO1JEAIlsdwSVhs4SIbMpx+vTpMnr06CCkJmwPDY0BRLYk/FUcYpMQ2Q4xcC4XcwKIbDFHygkPMQFEtkMMnMvFnAAiW8yRcsJDTACR7RAD53IxJ4DIFnOkxp4wYSJbKDF3dZu3PpsJohsim7GxX286jshWb1xBR+pIAJGtjuBoVm8IILLVG1fQkToSQGSrIzia1RsCiGz1xhV0pI4EENnqCC4Jm9Ubkc3LdsqUKXLnnXcKIlsSRhwmVSOAyEZQmE4Akc10D9J/RDZiwHQCiGyme5D+I7IRA6YTQGQz3YOx63+9ENkeeughGT9+vN+qoUOHyi233CI66a3vByvZ6ruH6n//ENnqv4/oYWQCiGxEiOkEENlM9yD9R2QjBkwngMhmugfpPyIbMeASSIjIFro11CRRLTR0ENn4MUVLAJEtWoK0TzQBRLZEe4DrR0sAkS1agrRPNAFEtkR7gOtHSwCRLVqCtE80AUS2RHug/ly/Xohso0aNkjFjxtQfKgfRE0S2g4DFV8MSQGQjMEwngMhmugfpPyIbMWA6AUQ20z1I/xHZiAHTCSCyme7B2PU/ISJbaPdD3zRq0so2RLbYBaOtZ0Jks9XzyWM3Ilvy+NJWSxDZbPV88tiNyJY8vrTVEkQ2Wz2fPHYjsiWPL6O1pF6IbDWJbrz4IFr30t4EAohsJniJPkYigMhGfJhOAJHNdA/Sf0Q2YsB0AohspnuQ/iOyEQMugXojsoWuZtMOmrCijZVs/JiiJYDIFi1B2ieaACJboj3A9aMlgMgWLUHaJ5oAIluiPcD1oyWAyBYtQdonmgAiW6I9UH+unzCRLfSNoqaIaqGuQ2SrP8Fsak8Q2Uz1HP12CSCyEQumE0BkM92D9B+RjRgwnQAim+kepP+IbMSASyAhIpv7dlHtxLhx46SoqMhYjyCyGeu6etNxRLZ64wo6UkcCiGx1BEezekMAka3euIKO1JEAIlsdwdGs3hBAZKs3rqAjdSSAyFZHcEnYLCEiWzJxRGRLJm8mxhZEtsRw56qxI4DIFjuWnCkxBBDZEsOdq8aOACJb7FhypsQQQGRLDHeuGjsCiGyxY2n6mRDZovQgIluUAGkuiGwEgekEENlM9yD9R2QjBkwngMhmugfpPyIbMWA6AUQ20z0Yu/4jskXJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILJFiRKRLUqANEdkIwaMJ4DIZrwLrTcAkc36EDAeACKb8S603gBENutDwHgAiGzGuzBmBiCyRYkSkS1KgDRHZCMGjCeAyGa8C603AJHN+hAwHgAim/EutN4ARDbrQ8B4AIhsxrswZgYgskWJEpEtSoA0R2QjBowngMhmvAutNwCRzfoQMB4AIpvxLrTeAEQ260PAeACIbMa7MGYGILKJyEMPPSTjx4/3Q7311ltl2LBhBwQZke2AMPGlCAQyMjJEb/B27doFJwgYSQCRzUi30WkPAUQ2wsF0AohspnuQ/iOyEQOmE0BkM92Dseu/9SLbvn375PHHH5fLL79cioqKZPny5TJmzBi54447ZNCgQbWSRmSrFRFfqIUAIhshYjoBRDbTPUj/EdmIAdMJILKZ7kH6j8hGDJhOAJHNdA/Grv/Wi2yhKFV0u+uuu2Tw4MEHtJot2UW2DxdtlgUbiiUtNUWG920iLQpz/MjW7dgrk+dsln2l5dI8L0MuGdza/9ms1Tvk82U7pLyiQvq3zpcTujb2f1YpIs98tUa27y2R7Ix0uXRQcynIyfR//v7CzTJv/W5JT0uVEzsXSe9WBf7Ptu0pkZdmbpC9JeXSKDddRh3ZJsiFz36zRrbsLpGczHS5YEAzaZSX5f98/rpd8tH326SsvEJ6Ns+VU3s2DWo7Ydpa2bRrv2RnpMn5A5pJk/xs/+effr9VZq3ZJampqXJsx0Lp3ybQp+L9ZfLst+ucPhVkp8tVR7WRlJTAqf8zbY1s2F3qnHdECMMlG3fLB0u2S2VKqrTJS5Wz+7UI6tOL09fKup37JSs9TYb1aSKtGzZwPi8tq5Cnvl4je/aXSX52hvzkyFYOL/f45oft8vWKHc4/j+xQKEe0L/J/tnLLHnlz/hYpKa+QVvmZcsGgVkHXnPzdelmxdZ9kpqfK0B6NpVPTXP/n01fukC9X7JCKigoZ1K5AjunUyP9ZeUWlPPXVatm1r0xys9Jl1BGtHJvdY8aqHfLF8h1SUVkph7XJl2M7B9pu2LlXJs3eLHtLyqRFfpZcMji4T+/M2yiLN+2V9NQUOblbQ+nRIt9/3tlrdsqnS7dLWVWsneiNtUqRZ75eLdv3ljl9CY01t632vXeLBnJyj0BMfLR4i8xZu9uJ/eM6F0m/1gGf79xbIs9P3yD7SsukKCdTrjyqtXhcLhOmrZGNu0okJyNNzhvQTJp6YmnRht3y/qKtUlpeId2a5sgZfZr7bdEYenbaWsevhTkZ8pOjWkuqJ5g+W7pVZqze5fxtSIdCGdSu0N9Wf4fjv/G1zc9Ol6uHtAlq+/L0tbKmKpbO7t1E2jbyxZIeyzbtlqkLt0pJWYW0b5QtI/oH4rC0rFye+tp33rzsDLkqJNam/bBdpq8plkr1a+tcObJDINb0t/b012tl175Syc1Ml58c1Uoy030xobnhqa/W1Bgv/lgLEy/rd+yV12rIPXrut+dqvBRLRlqq/Lh7I+nePM9vq5ubNA77tsqToHipyk3b9uyXnKwMuezwFs7vyz28sdavZZ6c1L2J/zNt89LMjTXmpvcXbJL5G3259PhORdLXE0/e3NSrRZ6c0iNwXr3Ac9+skc27NV+myQUDm0vj0Ly2ZJuUVVRK96Y5MrR3M3+fPlu6TWas3unEwdEdCuSwdgHf6G/t2Wnr/PGieUv75h4vfrtG1u0qcXLPOX2aSJuq3OOLlz0ydeEWJ146NMqW4Z54KSkrl6e/Wit7SsLnpq9XbJNvftgpKSkpcmT7QhncPhDDGmtPf7NOdmu8ZKU7sZZRFS963eDc1Eg6NQ34ddXWYnlj3mbZX1YurQuygvJabblJY+2bVbtFx6Z+LXKCclNFRaU8/fUa2bG3VBpoXhvcSnIyfTG8edc+eWXWRtlbWi7N8rPkspC89e78TbJw4x4nN5/UpaH0ahnIW3PW7pL/fb/NyYd9WuTKjzyxpL+lZ79ZK1v3lDrXuuiw5lLUIDBG6rg8d92eA8hNGXKljkeeDP/xki0ye80uSUtNrZbXFq7fJR8s9sVSV81NnljSU0Qayw4mN4XG2hfLtsn0Vb6YCM1rE2euk9U79ktGaoqc1buJtG8cGI9+0LFs3hYpraiQtoVZMnJgS894pPklfO7RL32rY9ny7c73D29bIEM6NfS3Xbtd5zabnFhqWZAlF4WMkW/MXi/Ltu5zxqNTezSWrs0CcRhpXlRZKc4Y6cRSZppcPril5GYF8os3N/VvlSfHe8ayLbv3y8szdcwplyZ5mXL5EYH5lnb8vYWbZcmW/ZIilXJCp8KgWAvkpjJplJsRNG/aUVwiL8zY4MRwwxzfnMo7f/lo0WaZvW63pNcSL92b5cjQXoHc48TLt2tl/c79zjg4sn8zaVYQmFNFzE2l5U7812UcXLppt7y70De+am4aFjKnemXGWieeNK+d3aeJtPXkta9XbJdvfthRlZsKZLBn3qRjmY6DNeUmHQe//mGnMw4e0b4waBx8Y84GWbZlrzMendq9kXQJiZfXZmusVUiL/Ay5+PCAX0NzzxWDW0qDzHR/nM7UOdWKnc5YOrBNvhznmVNt2rVPJvpzU6Zc5pmf6wl8ualY0tNSquWmSLGm9o3/eo1sK9bclF4tN81du1P+p/Ox8grp1TxXTvHMswPzpnIpykmXK0NiTXPT/I37RGeyR3fID5pzKffnvl0n+0oqpFDbeuZGizfslvcWbXHyVqfG2XJ23+B59AvfrpH1u0olKz1VRvRrKi099zFfLNsq01ftqso9Rc681j32l5bJM1+vqxrLdP7S2vkduMdEjaWdJZKZlipn9moclJtWbN4tb8/f6syz2xZlysiBwXPaV2euk1Xb9zvz7NN7NJKOnrHMyU0rtosOSIPbFchRHQO5qdpYNrilZHti4v2Fm5x7Ns1N5/RpLK0bBvJl6d5tUr70aamsKJXKBh2lQZcLPCODyN5lr0rlrkWSkpoh6a3PlIxGvao+r5R9C/4l5fu2SFpGA0nv8lNJz/aM25tnSPm696WyolxSGx0mWW1P85+3bO8WKVv6jFSU7ZWUrCaS0/NnQdfcv/Idqdg+S0TSJL3VKZLRZKD/89Its6V87btOf1OL+kpW+7OD+7vwManct1FS03MkrePlkpEbyD8lW+ZIxdqpUllRJimFvSS7Q2B3WnnJbilb8rjo/6dmNZTsnr8Ub9IrXfuhlG3+RiQlVdKanyiZzY4I2LN7rZSteEEqK8sltaifZLUdGtQn/QciWzUk1v4BkS3E9du3b5exY8c6q9lsX8n23Fer5P5PVvsJ5aSnyItX9nNuzvXmYsQTs2RPqd6W+I7jOxXKuPN6ybQfdsjol+YHkb3ttI7+G7FrX5wn367c6f+8VUGmvPbTgZKRnirPfr1Kxv0vcE390pMX95aBbQucSeCwR6fL5uJyf1udzPz7Qt9A8IuX58tXVcKS/rtJboa89tMBzs3arFU75KoXgvt03XGt5coh7Zy2v3x5nny5ItAnnWxOvmaAc4P9+uz1cvvU5UH2PHpRb2fw0wFv5BMzZOX2Ev/nOtl58pI+zr9//eoCZ8LhHrkZqTLp6v7StCBb9Ibm0mfnOjd27nHZoGZy/cmdnX/e+NoCR4BzD+X/6tX9pXlBtlw6/jtZuHGv/7NeLXJlwhX9nH+rIPirSYuC+vvAud3luC6NZPXWYjnv6dlSUh646tAejeQv53R3vv/nqUvk1dmb/W31pvvFUX2lc9Nc54bkl68sCDrvPed0lR9XCQI/mTBHvlu72/+5TnZe/skASU1Nka9WbJdfvBzc9s9ndpLTezeXrXv2y4gnvpNd+wN+Pa5TkTxwXk/nXP/+9Ad57Mu1Qdcdf2lvR6QIF2u3nNJezq2a0Fzzwjzn5s09WuZnyKSfDpSsjDTn7/q59/j9ye3kgkGt5ZUZa+Sv768M+uyxi3rJ4e0KHYFzxBMzZe3OgM8Pb1cgj13U2/n+dRMXyGfLAn4rzE6TyT8dIIUNMmXu2l1yxYS5Qef96ZEt5BcndHRi6fwnZ8qKbfv9nw9onS9PXeqLpXfnb5Sb3lwa1PaR83vKUR2LRG/eLnx6lny/ORAT/VrlyTOX9XW+/7vJC+W9xdv8bbPSfLHUqihHlm3eIxc9M8eZoLrHiD6N5dYzujn/1FjTSZt71BZr94/o5hfVr3hujsxdF4iJ7s0ayH9G9XfmM1dOmCOzPfHSpUmOvPiT/o4g9NXy7fKLkFi7+6wucmqvprJFc8+T38nukgp/n9zco394+OPl8tQ36/2fqcAw/rLe0qdVgUxbuUNGvxicB/54WgcZ1t93cx4aL60KNIdobkpzbsqvDWl784/byXmHtXaEtWGPzZDNxWX+66rY+K8LfDH8n2mr5d6PVgX57vGLejkiqd4oXR0hN/3i5XnyVVBuSpMp1wx0BE8V/a58PjiGf35MK7nmmPaiwvQf3gqOF+2P9ktv3s5/aqYs3xqItf6t8+TpS33x8tvJC+V9T7xkp2ve6uc8ZPl+4265+Nm5Try6x3n9msjNQ7s6N5iXPPOdLNoUiMPeLXLluarc9Mn3W2VsaG4a2d1/c3jZs7Nl/vo9/vP20Hi5sr/z77veWSyT5mzxf6Y3ES+M6iOdm+aJChvnPvGd7PfktR93K5J7hvv4h+amzpqbrhroxOFXy7fJL15ZGOSbv5zZ2S9WXvX8HJm1JhDDHRpmyStXD5Q9+0tl+OOzZPu+QN46umOhPHy+bzx66ouV8vBna4LO++TFPWVg2yKZvmqHXBPi89/+qJ1cVHWDPfrFeTLNM0Y2zU2XKdce5oisL09fI3d/ED43qcA5/PGZzg2lewxqWyCPX+zLTa/NWid3/ndFUJ8evbCXI3SGy03XHNVSfn58B+f7v5o4Xz5d5ntwo0deZqqT1/RB1tR5G+XmkFh75Pwezs2h5qYLnpopS7fs87dVAenpqtz03oJN8rs3vg/q0z/P7yFDOjaUW99YJG8t2Or/LCNV5OUr+0n7JrmiAtsFT8+W0kAakLN6NZI/neUbyyLlns+XbZMxE4N9/vdhXeXk7k1EBfyRT34ne8sC8X1SlyK571xfLP3jg6UyYfpGf580vzx/RR/nwU+keZE2uPo/c2Xm6kBZiHZFWTLx6gGOCBsuN90+tJOc06+5I+wMe3ymbNsbiLWjOhTIIxf4/PrMlyvlwU9DY62XDGxbKMUlZU5MbN4TyE1HdSiSRy7oKftLNV5myIbdgc8GtyuURy/yxfDEGWvkLzWMg+Fyz+ghrWT0ce2dtqHjYH5WqpO3VCx+e95GuaWm3FRZKec/MVOWe8ZBb7z8d/4m+f2bwfHijoMFInUcAAAgAElEQVSLN+ySS5+bF5SbLhjQVH5/ahenTzdNWSTvLgrEk46DE6/q5zy8/N+SLfLr1xYHxaE7b9I/huamns0byPOjfLlJBcPrXg2Op/uGd5WTujWRv7+3VF6YGYiXtJQUmXBFH+fBz8ade+XcJ2dLsSeIT+xcKP8Y6eN/9fNzZeaaQLy0b6jxMtAR18PFy59O7yRn9W0uKmZFyk1Pfv6D/PPz4DnV05f0kv5tCp1Y07Zb93rjpVAeucDXp3C56fXRhzmi5cxVO+XqkDnV9Se2lcuOaOPE2rlPzJR1NeSmSTPXyl3v/RDE381N+vBl5JOzZPWOwJzrsLYF8sTFvUUfUF3+XPA8+opBzWXsyZ2cc13/6nz5eGkgbzVw5uD9pFlBjry3YKP87o3gMfLh83rI0Z0aOvPyi5+e5TzcdQ99KDe+Km+Fy02v/KSftGucKyqwXfjMnBpz0+1vLZbX53nHMpGXruwnHZvkij6suO7V4Pn7vcO6+h/CjJowx3n46x7dmuoY2c+ZNz3zxQp58LN1/s+ce7ZRfaRt4zwp279Tdk39saTuD8yNpN3FUnjU35zv7555t5QvecTftlJSJPfkNySjcT/Z/dEFUr7pq4BvGrSV/NM/ktS0TCnd+KUUf3xhkN/S+/9ZcrtfLhWlxbLz7eMlZX8g/tObHye5JzzvfL944VNSOvv2oLY5J74imc2OlNKN06T445FBn2X0uUka9Pq587ddH18mFRs/CfQ3s7Hkn/4/ScsqkNLNM6T4w+FBbdN6/kby+v5KKsrLZNfUk0X2BO7p0pocIXk/muh8f+/ySVIybWxQ29wTXpD05sdI6c4fZM97p0lKeWA+nN7nFsntdW3Q9xHZgnBY/Q9EthD3a302PVRk8x7Tp0+vFigqwiVzHa0Bf/lUynWG7DlaF2VLq8JsWbtjn6zZHpg0u1/RJ3+rtu11nl56j7QUcVZR6M2d3mCEHu0a5kjzgiznRlZv1LyHXk+vu27HPlkd5poqfOihbUMPt79rduyTtTX0Vy/37cqAKOKew207Y+V28dy7OR+rgKcDoq58W7E1MAi7bfu1yneEHH26WVOfIjHUNjW11UnCkk2BJO+eXwUMXRG4YN3OIAFCP9cbop4tCyL6LdI11QcrthTLpt2BSY5+Pzs9Rfq2LnSeGs9aHRCz3D51bJwjTfKyZOXWvbJhV3BMZKalOJO6mjioX3VlQ3gOOdKqMEtWb9vrrPQLF2v6dHeGR2Bzv+PG2urte2XdjuC2upBnULsimb5yu3g0BKdp8/wsadcoRzbs3C8rt1X3ua7Y1CfVkeJw7Y79smZ79bb6u6kplvq0zHOeGH+3ekeQOKp9atQgwxFA9cmzPikPPVQQ05USdY1DXeWgK8JCD53YqWi4fHOxbN4THBO5manSq2WB6A3/d54bBPccKqap8B3us06Nc5xVWj9sLXZWAnqP2uLFXXUQKV6iyU2R2kbKTRrDulJKVyx5jxYFWdK2YU7E36S2+DZCDtEcrL+f0ENZzFq1PWiS740XXRW3fEt1v7qxVtd40RUzXqE3kJtypSAnQxas2yW7SwJCgX6en5XmCBR6EzZ7bfW6lLqiqqhBVp1juKY4dHPTD1uKZWNIXnNjTVdjzAyT1zo0ypHSisqwY2CkvNWqKFtaF2ZL+NyT4oiuukrY+2DAZdimKFtaFmbXkJsypV2jBjXmpgFtCpzcFG4sa5aXKe0bN5BIuam2sSFSbtq8e78sD5OberfMc1blzFmzQ/Z5BC29lq5y0dVhdY1DXYGmq/VCjy5NGkjDXM1be2TznoAQqd/LyUh1hPi6jsu1zYtqiqX2DbOdFV6R8kuNY2T7ImeVYiROkXKTztPCzakGtilwhL9I8RIp9+gcLtI4qA899ffjPRo1SHcE85rGwb6t8h2ROVys6UNRXR0Wje/mr9spezwPbry5qfZxsHo8ueNgXWO4pjmV5p6m+eHnVLrac0Dbus2p3Hl2TQydVfOVEnb+7uamcDGh4o+2rWnepA8S9SFWuFhrmpcpHRrXnNd0zqW/45ruRWrLW5Fyz9bd+2VpmLylu2D0AVdd/Vpbn3Rc1vHZe+Skp0qf1gU1jpHuvEnnCsG/KpHze+XLxX3yJHvts5Kz+olqOTGj+dHO30o3fFHts7S8tpLaoJWUbvy6+mf5HSW1QUsp3/WDVBQHC/ySkiYZzY6Uij2rpXx38AMhPVFGsyHOyrHSjV+JVHqekog410vL7yAVe1ZJ+e7gB5NqW6bT30op3fBlmP62k9TcNlKxe5WU7wnftrx4vVTsWlatbXqTQZKSliUlm6ZJSkXw2JCS3VTSC7uG7ZPa2uSS4IUhuuV57969zlhu+pGfH1h9b7otieg/IpuHugpsGzZskFtuucUpRO89rr02WKnWzx577DEpLQ3+MSbCifG4piazbrf+Nx6n5pwQgAAEIAABCEAAAhCAAAQgECcCp3fMkIt6ZEnhpglSuPmFOF3F7tO2uVpXxwaKMqSnp0t5eXm1BSMmUtKa4Rx1J4DIVsUuksAWCW8y12S79JlZssCzJVE5XDOklRzerkjmrN0pD38arN7rCqv7z+0hk2dvkHcWBJZCa7tBbfPl2qPbOsv4dcth6NMW3U7auihHHvh4hcz3bE3TtsP6NJUzejeVFVuL5a/vBW910WXzurUkRVLkl6/Ml7KQBwe//3EH6di4gbwzf5NTY8V76HL/sSf6tsL83yvzq636uOFH7aVr01x5/ItV8u2q4KfiZ/ZuIuf0aeaszrrt7eDl5no+3ZalT/B+PWlh0FYA/WzMcW2cJ+Zaf0JroXiPdkWZ8ofTfFsbbpi8UHbtDzbo58e0Fn2SGbq9Rr//0Hk9nNoQWgfFu0VVPzuhc5FccngrmfbDNnniq8Bycv2sUU66/PUc39bA299ZIut2BgvHlx/ewqlR9MrM9fL+4sBWC/3+MZ2K5IrBrZytvKHbwPTzP5/Z2VnJNnHWennPs01DPzuyfYFTv27B+l0y7n/BT510e9oD5/Vwnpze++GKaiv3zuvfzKlbNWXORnl7fmB7q553YOs8+dmx7ZxVmL98uXqs3XpqR2nTMEfenLdJ3pgbHBP6VPXnx7aVf322MmiLmJ737D5N5azeTZ2VaH96N3j7sA6v/7qwl+hKuDGvLAjatqZtrz+pnXRvlifvL9oir8wK9rmu7Lrx5I7OKsFb3greBqNtdVuPbjF55us1Tj0873F6ryYyvG8z2bKnRG4O2ULjtD2/p7O15HdTFgVta9PPrh3Sylm19/myrfLsNM8WAhFpkZchd5zZVXbuK5UbpyyplgLvHd5N8rPS5YXp6+Tj7wPbUPWLx3UqdOq/aE2u60O23+jnusVY65CEixfdpqcr2cLFi9ZquvLI1jJv3S558JPgeNFtEeNG+rZz/e29ZU69JO+hNRZ/3L2JTJ69Ud5ZEBwvg9rky7XHtK0xXtzc9PqcjfJWDbGmT5/vfr/m3PTwpyuDtnho34b1bSpn9GoaNjdpLZtfnejbdhUuN934o/bSpWmuvLtgk0yaHRzDusXy1yd1cGo/aY0g73FGryYyrG8z50n5H8LEmuZSrTlz45RFstOzDVLP8bOjWzvbzz5dulUmfBscL7rl/4+nd5Ede0vkt69Xj2E3Nz3/7Vr5xLN9Xs+rNa1G9m/u1O27IUys6Ta+guwM+ePbS4K2QWpbrVF0TKeGovUeH/0i+Gl6YVaq3DO8h1PDKnT7vLb9y1ldpHFuZti85saarogK3bqjbe86s4tsKy6R+z4KfkqvK+DUVp1w3//RCqfmkfcY0bepDO3VVN6ct1HemBsch7q9+5fHtXNWPP7fxIVBW970HDef0lHaN8qpc27SHPXo56ucmo7eQ+ucnd2nmVPTa+J3gW09+h03N+n9w9iJC4K2UOrn1x3fVnq3zK9Tbvrn+T2dukHjv1nj1Or0HkN7NpER/ZrJH95cHLTNUb9z1ZEt5cgODeWr5Vvlac+2cP2sSYN0+fPZ3ZxaXtdPDt765+SGs7tKUYMMeWnGevlwSfBYdmynIrl8cCtnC/Y/Pwue2+hK8PtGqF9F/vzu90HlIfRvWjPvpK6NI86LdGWS+jX0+NMZnZ1V0q/N3iBTQ+ZNh7fNl2uObivfb9ojf/8weCudbp11tyY/8PEPMn9DYJu1XmN436Zyeq+mzqq9uz8IbqvcHz6/h6zetk/u+m/wWKbzFt3qqytw//3ZSpnp2Sqt53XHwf8u3CyvhsSLrnD+zY86OPeb14UZB399Qltn1arW4vz6h2Cf+3PTnhL5Q7ix7ADGwXC5qXVhptw21Den+v3ri2WbZxuk/s0dB7WOqnc7dFBu2l8mN4SJJzc3RRoH73p3qVN7y3tcclhzp6RCpFjTFbhjQrYN6jnuPLOzNMvLkkmzN8i7IfGiJUx+OqSNaI2y+z6unpsePr+ns307XG46t18zOa1nE1m2uVj+9kHwWJaeKvLP83s583adv3tLBTi56dSO0r5hjrw9b5NMCZlT9W2ZJ/93fDtn18GdIbHmmzf1dObvkWJt7Y69ckdIyRZtq3MjnUdrLVTvodtqbz7VV3blN68trLaz45fHtnHqvUXKPdv3lsrvXq8+99HtpLoq+OY3FssWT3kIb276cvlWeaZabkqTP5/t28p+61uLZaNni7b+bdTgFnJ0p0by4ox18tGS4DmVm5uKS8rl168FbyXVtn89q4s0ys2U+z5YJos3B899fn9SazmjZyOp2PipVH47OohTRVYLyTtqnPO3vTNulcpdwfZmdB4lGa1PlT2fXObUevQeWYP+LrrSrXT5y1K6clLQZ6mNB0t2n99I+c6lsn/mH4JTXkq6NDj+OWeM3D/371K+JXiXWEaHCySj/blSsnKylC1/MahtSlFfyenvO9+eTy6XlMrge5XM/n+U9KKeUrLydSlb/p/gtoW9JGfAbVJRvFb2Tbu+Wh5ucPzzoqvS9i94WMo3fhb0eUa7EZLR8UIp2fCFlC18MLhtUV8pPPWtoL+xXbQaXmv/gMim4kQNW0QPJCqSWWTTAvk3vr5EllTVJLhwQFP5XVVtC2Vz7/tL5T8zfINbQXaa/GNED9E6CToAj3llvnxVdYOnBXHvH9FDWhT6Vgd+s3KHMwFz64L95sS2cukRvhcY6ITwximLZVlV/ZZjOxXKAyN7+WtSPvH5Cnnkc59I5N5Yu4Wztc6b1o1xa6n8bEhLufY4n4im2xfGvrrAP4nq2DBL/j68u7+ov25tGPtqQBBz62RpW63R8pvJi/11qY7qUCgPnecTL/TQ4qd/ed83idWJr9YS05oneuh2xetfW+i/Yb14YFO58RTfhE+P3762QN6vqrum9Z/uGdbdX7BY297w2kK/OHJ+/yZy02ldnXYfLNzk3MzqsKcCzD3Dujj1P/TQWkHXTVzonxyr6PTgeT38BZbveGuxTKmqB9G4QbrcM7y76PYQPeas2Sm/nbLIX6PFW6/NmfRNXOivFaQ3hePO7e4vyK11bn49aZG/ttcdQzv6X+SgNxhjJi5wCp7roXWatK1bwP3+D5bKc1V1bvSG5v6RPUVrCemhE0btk1v37sQuRfKPqvo4Gmta+8UVn7Tw+30juju1xpxYW7FDfvVqQPS6/oQ2ctmRbatiwlc35vOq4tf6EgIVj7TA++ptxXLD5CX+rZLHdCySB0b2cOrL6fH8N6vlvo99Qo/eWD94fk85omrbsm710lp8bs2wKwc3l+tO8tUH0eP6VxfIx1VCQ7uGWfK3c7pK9+a+JdmTZ62TP1XVTNKJ7QPn9vAX5NbtCypMza2qWaX1CB8a2cOpZajHW3M3yG1vL/PFRGqKaG009+USWivlxsmL/HVWRvRtLLee7hNW9fjDG4vknaraR81yfTHhvujBG2t6A6ax9qOqWNN6PyoqujdiA1vny4Mju0tu1csCdAKsIp36ScndfU4XOaXq5RKffr9Frp8c+OxPp3eUM6sKFrs3GFpzTw+dqGu86FYvPe57f6k8X5V7tNbP/ef2dHKPHvpiid9OWey/sfHWU3IEDK3dWBWHKkjdN7ybtHTjZeVOue6V+f7cdMNJbeSSwb540a3u101aKF9U1dvT7dkaL/pwQI8nPlshj3wRyE0PnKc1/Hx90vpRysHdSnlMx0InTyhPvekZ+2qg3lXHRtmiN29uUf/Q3HTNkS3k5yd09PtO85orXOnNxT3Dujlb7bQ+lPJ1a+Id0b6gKl58Rfu9tSb1pvv+c7v5X2Si8aIiv1sDyq255l705tcXOS/K0EMF2b8N6+Z/kYO+uEZvTtzc9PdhXeTEqnjRej9au8itcab1K514qSr+rgX9Na+pnzRe/nZ2Z/lxT18xY70hVbHYrR91es9G/psW/fyud5bIpDk+4aphTprcO7yHU8tTD61zo4KvW3dQY+2sqljTej96M6sFz/XQLbP623Fz05fLtsmvJi30P8C57dQOMnyAr4bfPz9eJk9+4xPNtd7muPMCeUtfDqFj2fIqwVfFZxWCVbzQ8UhztJt7ujbJcXyu9Xz0+GbFdue3vq/qqdH/HdtarjraVz9UX/CgseRu4w7NTRO+XiX/qKprqjWnNM40V+ihteuun7TIXzNPxcQHPWNZUG4q8sVSt6qXhmheu37SAv+DH2/9UN2urvWsArGmuam7/6UVb85ZL7e94xNzNNb+4clN24tLHFvd+owqLKlQmZWRLvoigN9OXuyvdXh278Zyx5mBvOWtbaQC2z3Du8mANj5btf6fjttu7vnr2V38LztS4VXHIy14roduOxs3opuzBV6Pu99dIi9/54ulouw0ubdqbqP/1hpQv3t9saypqg91ctci+fsIn8CvR03zIv1MhSXNL24Jrj+c0t5fEF3LG2gudXOTPgTU3OS+bOpf/1suj3/tE7f1gea4kT2cGqF66ItrNCaCYy0wb3r8sxXyrxpy07NfrZRxn/gEao2XB8/TGn2+340+UFJxaVGVWHx0pyJ58NzAOPjrV+fL/6rqXekDQp1TuS+BCB0HrxjUTMZW1ZvdtFNz02KZ5x/LCuTh83r6X9405bt1cse7PqFHh7dx5/rqZOmhL7/SeJlTVetT538aa+4LdW6aslDeXeQTKVrkaUz0kD6tfOOrbxxcKFuratt5x8E9Tm5a5K+BpvOmh87v6X/RwIeLN8tvpwRy0z3ndPG/KElrcmo8uQL2gNZ5znxBa/rOW6tzqsX+WmSndGsofxvuE2z1+Nt/l8hLs3yxpg8GtPafO5ZpbdLrXl3gzz23nNpezh3gK6CvL3rSHOLWIe7VvIH8Y0R3/8slvLVJczNSnHrJ7pxKXw5x4+TF/vqvx3cuch6Qa27S49+frJDHqh7G6jzbO5bpS7V+NXGh/0HimGNby0+qcpPmfJ2Df1ZVu1FFeuXUoYnvxSDVctPInnJEB18M68tGfvNazblJX8Rzz4c+4TB0nn3DpAXy4fe+0ixtCjPl7nO6Bc2jfzNpgeyoemB94YAm8rtTffNo3QasseTWElY+KqJpqRc9tA7uzW8u9c+p9Pd4fBffC7uc3DQl8BBAXw50+xm+8+px25uL5M35vjGySYM0Z07l5iYdy3Q+tqmqTuKZPRvJnVUCXPXclCdaG9B9OZzWXNbfjpvX7jqjk5xe9fIsrQWnfl1aVWv1gv6N5fenBfLlzjmPSOWCu50+Vea0lQZD/imZTQY4/y7dOleKv/yFyB7f7y611VDJP/YxX6xt/Fz2fHKlSIVPLE7r+0fJ63m17zyVFVL86ZVStv5j59/6koGcIY9JRoFvvNo150GpWHCv77tpDSTvuKclXbeL6jV3Lpe9X4yWyp2+hw/6koHc45+RlJRUPbHs+vQqqVj/ge+8+V0lZ8i/JaPIx1i3i+757GpJKffVp0vtdp3kD7jB+W/nup9cLRXr3/P9I6+zNBjyL8lo6Pvd7V3yvJTMvMnXp5QMyTt+vKQ3P9Z33j3rZf8X10j5tu98fWp2rDQ4brykpvlWdO3+9jYpX/aMr08FPX19KgzMx/TviGx+N1j/H9aLbO6LDubODS5GPnTo0LDbRkMjJplFNtdWFUj0pt37lkP3M73x1EFB37oWeuiNsn7uffON9zsqBjXIzAh6k5X7udbn0Wt635jpv2ZlpVNoXOs6hTv0vFrDKlx/tS6KDk7uIBraXkUDrfsRru2+kjLn7aK6Cif00JvCfSXhOeh39Q2kznk9b+9zz5GSmiap6RlSXlK9tpIzIJSUOUVlQ9vqzbl+prZ63wbmZaj/Hc5WndDrmwG1bbhDV6ZpfY9w/NWvepOoN0Ghh/5dfZOdGZ6hXtPXNvDWUfcc6hf1e7hY0u9onGlM6FPEsLFWWem8rTZ8TJQ5k+VwnDS+9dreN6G654h0TeVfXKJvQQy/nFpjSd9mpW/xCz0iXVNjKVJ8KyO1w72p8J7bjUN9I6E7YfZ+rvGi8RuuT8pA34Slb4Kr7lefrTX9XtMyshy/VpQF1xLR80SKUzdecrLSg96A6F7fF2vhYzhS7nHjReNXb+rDxksdc5PPdxVhY6023znxlCJBb8x0+xZNbooUT754SYmYtyLFS7jco33W/mpdpZripa65qba8Fik3RcprteUmzcE6uS8vDV514vrHyeFh8przm9O8VUMujTSW6UtUdMVtuNzjjBuaSzNSw49HEfJhbbnJN5ZpTFT/rUeKJXcs0/ztfROtd8ypa27SN/n54rR6n2rNW2XlYcey2mLJyS86Roa5puYXXclY4xhZUuaMRWHnKBHmRS7Dmn5zkeZNtcVaema2U9hb/xd6+HKTvvm7+nilturvqqY5lb5FW8eMcGNvrfESYRw8kNxU03wg0jjoy006ltUwv4kwDkbqk/u7qmkcjDS/UfbRxEtd5lS1xYu+0VTHo5rn2eHjRWMr0jxbH1xoyb1w8zwfQ98b6MMdaZlZUqFb7cLEsOZwzYnh2qroWFYeIZdGmIPrb06PcLknkEPqOKeKlENKy53dJ+FyaaS5z4HMm/Sc4eZ5KopVlpdKanpWWP7OW0BTMyQltbp/9G2caRm5QW/idE+i56ysLHPe9Fl9/ljhvAQhLTPwFuageWvZPudNnvoihWpttT5aRbmkpAeXcHK/V1G6x3dNFeZCjkh90oml9ik1M/D21QPukzLUN6lWCW+h10VkCxtaVv7RepEtWq/bILJFy4j2kQnonnetAZjML9EgBpKbQE6Ob2KlxV45IGAiAc3B+hCluLj6CyFMtIc+20cgLy9PSkpKnP9xQMBEAlo0vqysTPbvD/+ww0Sb6LNdBBDZ7PJ3JGsR2aKMBUS2KAHSXBDZCALTCSCyme5B+o/IRgyYTgCRzXQP0n9ENmLAdAKIbKZ7MHb9R2SLkiUiW5QAaY7IRgwYTwCRzXgXWm8AIpv1IWA8AEQ2411ovQGIbNaHgPEAENmMd2HMDEBkixIlIluUAGmOyEYMGE8Akc14F1pvACKb9SFgPABENuNdaL0BiGzWh4DxABDZjHdhzAxAZIsSJSJblABpjshGDBhPAJHNeBdabwAim/UhYDwARDbjXWi9AYhs1oeA8QAQ2Yx3YcwMQGSLEiUiW5QAaY7IRgwYTwCRzXgXWm8AIpv1IWA8AEQ2411ovQGIbNaHgPEAENmMd2HMDEBkixIlIluUAGmOyEYMGE8Akc14F1pvACKb9SFgPABENuNdaL0BiGzWh4DxABDZjHdhzAxAZIsSJSJblABpjshGDBhPAJHNeBdabwAim/UhYDwARDbjXWi9AYhs1oeA8QAQ2Yx3YcwMQGSLEiUiW5QAaY7IRgwYTwCRzXgXWm8AIpv1IWA8AEQ2411ovQGIbNaHgPEAENmMd2HMDEBkixlKTgQBCEAAAhCAAAQgAAEIQAACEIAABCBgKwFENls9j931hsDHH38sb7zxhtx33331pk90BAIHQ+Cxxx6TyspKGT169ME047sQqDcEJkyYIJs3b5axY8fWmz7REQgcDIFbb71VhgwZImecccbBNOO7EKg3BO666y7p06ePDB8+vN70iY5A4GAIXH755XLTTTdJr169DqYZ301CAohsSehUTDKLACKbWf6it9UJILIRFaYTQGQz3YP0H5GNGDCdACKb6R6k/4hsxIBLAJGNWIBAggkgsiXYAVw+agKIbFEj5AQJJoDIlmAHcPmoCSCyRY2QEySYACJbgh3A5aMmgMgWNcKkOQEiW9K4EkNMJYDIZqrn6LdLAJGNWDCdACKb6R6k/4hsxIDpBBDZTPcg/UdkIwZcAohsxAIEEkwAkS3BDuDyURNAZIsaISdIMAFEtgQ7gMtHTQCRLWqEnCDBBBDZEuwALh81AUS2qBEmzQkQ2ZLGlRgCAQhAAAIQgAAEIAABCEAAAhCAAAQgkCgCiGyJIs91IQABCEAAAhCAAAQgAAEIQAACEIAABJKGACJb0rgSQyAAAQhAAAIQgAAEIAABCEAAAhCAAAQSRQCRLVHkuS4EIAABCEAAAhCAAAQgAAEIQAACEIBA0hBAZEsaV2IIBCAAAQhAAAIQgAAEIAABCEAAAhCAQKIIILIlijzXTWoCU6ZMkZUrV8qYMWOC7NS/33nnnc7f+vTpI+PGjZOioiLn38uXL3e+v379en8b73e2b98uY8eOlblz5zqfP/roozJo0KCk5ohxiSPw0EMPSbt27WTYsGEHHMPe+HYbjRo1yonrffv2ib45bOrUqf7zEcOJ868NV451DHuZufGsf7vlllskOzvbBqTYeAgJ6Jj/xz/+0Rn3O3bsGHRlje3x48c7fxs6dGhQDHo/cxvpm0e9udz7HTdHH0LTuJRFBGrKw5FiONJcQtF5P2/RooXouUJ/IxYhxtQ4EqgpD4fek4XmWO3S9OnTZfTo0WHv+bwxHJrD42gOpz6EBBDZDiFsLpX8BLwJNXTiqp/pRMAV1vS/N2zY4J8cq8h2++23O/8LnSy4NzuCO4UAAA0VSURBVHSDBw92JsqRvpv8lLEwngS8A3/opKG2GNa206ZNCys66ITkueeek2uuucYRJPRcegPJ5Die3rTz3PGKYZemVzBmcmxnjMXTam98hRMQvHlW+6EPL5o3b+5/qKc5VY/Qh3xun2v7PJ62cW57CETKw7XFcKS5ROg8JPTf9hDG0ngSiJSHw92Tab694447/IsfIsVluLl0pJwdTzs5d/wIILLFjy1ntphA6Eq20ISsaEKFskjCmX6m4pwmcF35Fu58FuPG9DgQCH36fCAxHGliHNpF9ymgTkxYkRkHB3JKR8D1rsaMVQy751XENYnK4IdAtATCraAIlzcP5oZNv/vaa6+x+jJa59D+gAmE5uEDieFIc4nQz3jofMCu4It1IBAuD4cT0LwPLyKtQtYuhP4mEIrr4BgDmiCyGeAkumgegQMR2UInGqHbRb1bRWtL6OYRosf1ncCBCBShMRy6xSPSNiQmxvU9AszvXzxi2DuRPhhR2XyaWHCoCRyoyBaaS0O3i3pXJIfbhse2/UPtWbuudyAiW2gMR5pLuPOONm3aOGLxu+++G7Y8i12UsTZeBA5UZPPOB9atW1et/E9o6RR3Z5L2m/lwvLyX2PMisiWWP1dPUgLharKF3pDVtpLHu5103rx51Z4+s+UjSYOnnpgVro7KwcSwG98jRoyoVteNlZj1xMlJ3o1Yx3BoXkdkS/IASrB5Na2GCB37a1sF793GFG4FBdv2E+zoJL98uDx8MDEcbi6h7RcvXixffvmlUJMtyQMoweYd6MMO73wg9J7NG8OnnXaas8Vf58buLg5EtgQ7OU6XR2SLE1hOazeBcCJbuMLvkSYH3i2iS5cuDarnpnQR2eyOsXhbH25ifLAxHOl34K0hFG9bOL+dBGIdw6ErhFyq1GWzM77ibfWBFtzWfoS+SMnbN+/v4EBWd8bbLs5vF4FweTi0aHxtMeydS4TOK6jvalc8HWpra8rD3hrcbp/c1WrhtuW7ItwNN9wg9957r7CS7VB78tBfD5Ht0DPnihYQCCcuhJqtItpLL73kvDks3JvpvCLbtm3bqMlmQdzUJxPDTYwPNoZr2jaNwFafPJ28fYlHDHtpsZIteWOnPlhWW10ft496Q/fFF19EfNGBW5uwppzsXVVRH2ynD8lD4EDycG0x7I3bA9l+mjz0sCTRBA40D2tcHn300c7qtNA62mpDpBimJluivRyf6yOyxYcrZ7WcQG0iW7itou+//7507tzZ/2ZR70o13i5qeUAlwPzaJsahMawxOnnyZBk+fLgjGof7XJfIe5/eJcAsLmkRgVjHcCg6RDaLgikBph7IzV3oNiNtM3XqVLnoooucHod7wZJ3+yg3dwlwrGWXrC0Ph8ZobXMJzbv68g59GZi+CIyVbJYF1CE290DycOhcwL1ncx8oh86HD+ZlNYfYXC4XQwKIbDGEyakgEG75sFtU2Ls8Ptw20dC2oVuQQpfXU6yYeIsHgdCCw95YrS2GIxXcDn2xh9v3SC9HiId9nDP5CcQrhhHZkj926oOF4bble+cD3lwauk00XNvQuYJ3rkE9q/rg8eTsQ6Q8HCmGlUakuUTo58RwcsZPoq2qLQ974zvcPDb0ns37Ahq1zduekhOJ9nZ8ro/IFh+unBUCEIAABCAAAQhAAAIQgAAEIAABCEDAIgKIbBY5G1MhAAEIQAACEIAABCAAAQhAAAIQgAAE4kMAkS0+XDkrBCAAAQhAAAIQgAAEIAABCEAAAhCAgEUEENkscjamQgACEIAABCAAAQhAAAIQgAAEIAABCMSHACJbfLhyVghAAAIQgAAEIAABCEAAAhCAAAQgAAGLCCCyWeRsTIUABCAAAQhAAAIQgAAEIAABCEAAAhCIDwFEtvhw5awQgAAEIAABCEAAAhCAAAQgAAEIQAACFhFAZLPI2ZgKAQhAAAIQgAAEIAABCEAAAhCAAAQgEB8CiGzx4cpZIQABCEAAAhCAAAQgAAEIQAACEIAABCwigMhmkbMxFQIQgAAEIAABCEAAAhCAAAQgAAEIQCA+BBDZ4sOVs0IAAhCAAAQgAAEIQAACEIAABCAAAQhYRACRzSJnYyoEIAABCEAAAhCAAAQgAAEIQAACEIBAfAggssWHK2eFAAQgAAEIQAACEIAABCAAAQhAAAIQsIgAIptFzsZUCEAAAhCAAAQgAAEIQAACEIAABCAAgfgQQGSLD1fOCgEIQAACEIAABCAAAQhAAAIQgAAEIGARAUQ2i5yNqRCAAAQgAAEIQAACEIAABCAAAQhAAALxIYDIFh+unBUCEIAABCAAAQhAAAIQgAAEIAABCEDAIgKIbBY5G1MhAAEIQAACEIAABCAAAQhAAAIQgAAE4kMAkS0+XDkrBCAAAQhAAAIQcAg89NBDMn78+Bpp9OnTR+6++255+OGHpXnz5jJmzBjIQQACEIAABCAAAQgYSACRzUCn0WUIQAACEIAABMwksH37dhk7dqwjpA0aNMhMI+g1BCAAAQhAAAIQgEBYAohsBAYEIAABCEAAAhA4RARqEtn27dsnd911lwwePFiGDRsm7vcuu+wymTBhgsydO9fp4a233ir9+vVzRLr169c7f3v00UeDBLvly5cHfT5q1ChWxx0i/3IZCEAAAhCAAATsJoDIZrf/sR4CEIAABCAAgUNI4GBFNu3auHHjpKioSKZPny6jR48W3V7q/ZtuR3X/rQLb7bff7vyvY8eO4op3bEM9hE7mUhCAAAQgAAEIWEsAkc1a12M4BCAAAQhAAAKHmsDBimzebaXh2ob+TQW3du3aOavh3EPFOa8Qd6ht5noQgAAEIAABCEDAFgKIbLZ4GjshAAEIQAACEEg4gXiKbL1793a2nE6dOrWand7VbwmHQAcgAAEIQAACEIBAkhJAZEtSx2IWBCAAAQhAAAL1j8ChENncum71z3p6BAEIQAACEIAABJKbACJbcvsX6yAAAQhAAAIQqEcE4imy6dtKdVuoHrrNlAMCEIAABCAAAQhA4NASQGQ7tLy5GgQgAAEIQAACFhOIt8jmvhxB30Lq1mXTaz733HNyzTXXSHZ2tsX0MR0CEIAABCAAAQjElwAiW3z5cnYIQAACEIAABCDgJxBvkU0vpG8Y1ZVs69ev91/XK7rhDghAAAIQgAAEIACB+BBAZIsPV84KAQhAAAIQgAAEIAABCEAAAhCAAAQgYBEBRDaLnI2pEIAABCAAAQhAAAIQgAAEIAABCEAAAvEhgMgWH66cFQIQgAAEIAABCEAAAhCAAAQgAAEIQMAiAohsFjkbUyEAAQhAAAIQgAAEIAABCEAAAhCAAATiQwCRLT5cOSsEIAABCEAAAhCAAAQgAAEIQAACEICARQQQ2SxyNqZCAAIQgAAEIAABCEAAAhCAAAQgAAEIxIcAIlt8uHJWCEAAAhCAAAQgAAEIQAACEIAABCAAAYsIILJZ5GxMhQAEIAABCEAAAhCAAAQgAAEIQAACEIgPAUS2+HDlrBCAAAQgAAEIQAACEIAABCAAAQhAAAIWEUBks8jZmAoBCEAAAhCAAAQgAAEIQAACEIAABCAQHwKIbPHhylkhAAEIQAACEIAABCAAAQhAAAIQgAAELCKAyGaRszEVAhCAAAQgAAEIQAACEIAABCAAAQhAID4EENniw5WzQgACEIAABCAAAQhAAAIQgAAEIAABCFhEAJHNImdjKgQgAAEIQAACEIAABCAAAQhAAAIQgEB8CCCyxYcrZ4UABCAAAQhAAAIQgAAEIAABCEAAAhCwiAAim0XOxlQIQAACEIAABCAAAQhAAAIQgAAEIACB+BBAZIsPV84KAQhAAAIQgAAEIAABCEAAAhCAAAQgYBEBRDaLnI2pEIAABCAAAQhAAAIQgAAEIAABCEAAAvEhgMgWH66cFQIQgAAEIAABCEAAAhCAAAQgAAEIQMAiAohsFjkbUyEAAQhAAAIQgAAEIAABCEAAAhCAAATiQwCRLT5cOSsEIAABCEAAAhCAAAQgAAEIQAACEICARQQQ2SxyNqZCAAIQgAAEIAABCEAAAhCAAAQgAAEIxIcAIlt8uHJWCEAAAhCAAAQgAAEIQAACEIAABCAAAYsIILJZ5GxMhQAEIAABCEAAAhCAAAQgAAEIQAACEIgPAUS2+HDlrBCAAAQgAAEIQAACEIAABCAAAQhAAAIWEUBks8jZmAoBCEAAAhCAAAQgAAEIQAACEIAABCAQHwKIbPHhylkhAAEIQAACEIAABCAAAQhAAAIQgAAELCKAyGaRszEVAhCAAAQgAAEIQAACEIAABCAAAQhAID4EENniw5WzQgACEIAABCAAAQhAAAIQgAAEIAABCFhEAJHNImdjKgQgAAEIQAACEIAABCAAAQhAAAIQgEB8CCCyxYcrZ4UABCAAAQhAAAIQgAAEIAABCEAAAhCwiAAim0XOxlQIQAACEIAABCAAAQhAAAIQgAAEIACB+BD4f1B50JaMfyE6AAAAAElFTkSuQmCC", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(\n", " data=y,\n", " fh=fh,\n", " fold=fold,\n", " # fig_kwargs={'renderer': 'notebook'},\n", " verbose=False\n", ")\n", "exp.plot_model(plot=\"cv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Users can also specify the renderer for specific plot types" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exp.plot_model(fig_kwargs={'renderer': 'png'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Seasonal Period\n", "\n", "* Setting the seasonal period for time series models is one of the most important aspects that can dictate how accurate the model are.\n", "* By default, pycaret will try to try to derive the seasonal periods using the data characteristics. This is the preferred approach since it is data driven. \n", "* Users also have the option of deriving the seasonal period using the index frequency. This is not preferred since it is based on asumptions made from the data frequency.\n", "* Users also have the option of providing their own manual seasonal period if they have done their due diligence and dont want to rely on pycaret's algorithms.\n", "\n", "All these options are shown below" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 1: Auto Detection of Seasonal Period (Preferred)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id8371
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoauto
16Max Period to Consider60
17Seasonal Period(s) Tested[12, 24, 36, 11, 48]
18Significant Seasonal Period(s)[12, 24, 36, 11, 48]
19Significant Seasonal Period(s) without Harmonics[48, 36, 11]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[12]
24Primary Seasonality12
25Seasonality PresentTrue
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D1
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USId6ea
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations:**\n", "\n", "* The Seasonal Period was derived from data properties as 12.\n", "* Other harmonics such as 24, 36, 48 are also detected, but 12 has the most strength and hence is taken as the primary seasonal period.\n", "\n", "As specified above, users can change the seasonal period manually if they want based on their EDA. e.g. lets change it to 36 and see what happens" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 2: Manually provided Seasonal Periods" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id641
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)36
14Ignore Seasonality TestFalse
15Seasonality Detection Algouser_defined
16Max Period to Consider60
17Seasonal Period(s) Tested[36]
18Significant Seasonal Period(s)[1]
19Significant Seasonal Period(s) without Harmonics[1]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[1]
24Primary Seasonality1
25Seasonality PresentFalse
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D0
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USIa308
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, seasonal_period=36, fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations:**\n", " \n", "* In this case, the user specified a seasonal period of 36 and this also passed pycaret's internal seasonality tests. Hence, it is used for modeling.\n", "* If the user specified seasonal period does not pass the seasonality test, pycaret swicthes to using no seasonality (see below). " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id955
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)52
14Ignore Seasonality TestFalse
15Seasonality Detection Algouser_defined
16Max Period to Consider60
17Seasonal Period(s) Tested[52]
18Significant Seasonal Period(s)[1]
19Significant Seasonal Period(s) without Harmonics[1]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[1]
24Primary Seasonality1
25Seasonality PresentFalse
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D0
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USI7330
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, seasonal_period=52, fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Even then, if the user want to force pycaret to use a certain seasonal period, they can do that by specifying the `ignore_seasonality_test` argument**" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id6483
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)52
14Ignore Seasonality TestTrue
15Seasonality Detection Algouser_defined
16Max Period to Consider60
17Seasonal Period(s) Tested[52]
18Significant Seasonal Period(s)[52]
19Significant Seasonal Period(s) without Harmonics[52]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[52]
24Primary Seasonality52
25Seasonality PresentTrue
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D0
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USIa5ba
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp.setup(data=y, fh=fh, fold=fold, seasonal_period=52, ignore_seasonality_test=True, fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Method 3: Using the time series index (not preferred)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id1913
1TargetNumber of airline passengers
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(144, 1)
5Transformed data shape(144, 1)
6Transformed train set shape(132, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoindex
16Max Period to Consider60
17Seasonal Period(s) Tested[12]
18Significant Seasonal Period(s)[12]
19Significant Seasonal Period(s) without Harmonics[12]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[12]
24Primary Seasonality12
25Seasonality PresentTrue
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D1
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USI8cde
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp = TSForecastingExperiment()\n", "exp.setup(data=y, fh=fh, fold=fold, sp_detection=\"index\", fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations**:\n", "- PyCaret derives the seasonal period using the index frequency.\n", "- In this case, since we have monthly frequency, the seasonal period tested and used is 12.\n", "\n", "In some cases, when the frequency can not be derived from the index (see example below), user needs to switch to one of the other methods (auto detection or manualy specifying period)." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
x
0173.786244
1174.850941
2175.435101
3174.807199
4174.872474
\n", "
" ], "text/plain": [ " x\n", "0 173.786244\n", "1 174.850941\n", "2 175.435101\n", "3 174.807199\n", "4 174.872474" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y = get_data(\"1\", folder=\"time_series/ar1\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The index of your 'data' is of type ''. If the 'data' index is not of one of the following types: , , then 'seasonal_period' must be provided. Refer to docstring for options.\n" ] } ], "source": [ "try:\n", " exp = TSForecastingExperiment()\n", " exp.setup(data=y, fh=fh, fold=fold, sp_detection=\"index\", fig_kwargs=fig_kwargs)\n", "except ValueError as error:\n", " print(error)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Observations:**\n", "* The frequency/seasonal period could not be derived from the index.\n", "* The user needs to switch to one of the other methods (auto detection or manualy specifying period)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 DescriptionValue
0session_id785
1Targetx
2ApproachUnivariate
3Exogenous VariablesNot Present
4Original data shape(340, 1)
5Transformed data shape(340, 1)
6Transformed train set shape(328, 1)
7Transformed test set shape(12, 1)
8Rows with missing values0.0%
9Fold GeneratorExpandingWindowSplitter
10Fold Number3
11Enforce Prediction IntervalFalse
12Splits used for hyperparametersall
13User Defined Seasonal Period(s)None
14Ignore Seasonality TestFalse
15Seasonality Detection Algoauto
16Max Period to Consider40
17Seasonal Period(s) Tested[]
18Significant Seasonal Period(s)[1]
19Significant Seasonal Period(s) without Harmonics[1]
20Remove HarmonicsFalse
21Harmonics Order Methodharmonic_max
22Num Seasonalities to Use1
23All Seasonalities to Use[1]
24Primary Seasonality1
25Seasonality PresentFalse
26Target Strictly PositiveTrue
27Target White NoiseNo
28Recommended d1
29Recommended Seasonal D0
30PreprocessFalse
31CPU Jobs-1
32Use GPUFalse
33Log ExperimentFalse
34Experiment Namets-default-name
35USI5add
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eda = TSForecastingExperiment()\n", "eda.setup(data=y, fh=fh, fold=fold, sp_detection=\"auto\", max_sp_to_consider = 40, fig_kwargs=fig_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**That's it for this notebook. If you would like to see other demonstrations, feel free to open an issue on [GitHub](https://github.com/pycaret/pycaret/issues).** " ] } ], "metadata": { "interpreter": { "hash": "83be8a105015beb0be3130957f981d91e0431cfb610106a7fbaabcd7fd8062ab" }, "kernelspec": { "display_name": "pycaret_dev_sktime_16p1", "language": "python", "name": "pycaret_dev_sktime_16p1" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 4 }