{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Module 8\n", "\n", "## Video 36: Predictive Modelling I\n", "**Python for the Energy Industry**\n", "\n", "When working with time series data, we may want to predict the likely future values of the data. One common way of doing this is to use an autoregressive integrated moving average [(ARIMA)](https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average) model. That is the subject of this lesson.\n", "\n", "We will work with data giving the daily global floating storage of crude:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# initial imports\n", "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime\n", "from dateutil.relativedelta import relativedelta\n", "import vortexasdk as v\n", "\n", "# The cargo unit for the time series (barrels)\n", "TS_UNIT = 'b'\n", "\n", "# The granularity of the time series\n", "TS_FREQ = 'day'\n", "\n", "# datetimes to access last 7 weeks of data\n", "now = datetime.utcnow()\n", "seven_weeks_ago = now - relativedelta(weeks=7)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Find crude ID\n", "crude = [p.id for p in v.Products().search('crude').to_list() if p.name=='Crude']\n", "assert len(crude) == 1\n", "\n", "search_result = v.CargoTimeSeries().search(\n", " timeseries_frequency=TS_FREQ,\n", " timeseries_unit=TS_UNIT,\n", " filter_products=crude,\n", " filter_time_min=seven_weeks_ago,\n", " filter_time_max=now,\n", " # Filter for cargo in floating storage\n", " filter_activity=\"storing_state\",\n", " # Only get floating storage that lasted longer than 21 days\n", " timeseries_activity_time_span_min=1000 * 60 * 60 * 24 * 14,\n", ")\n", "\n", "df_floating_storage = search_result.to_df()\n", "df_floating_storage = df_floating_storage.rename(columns = {'key': 'date', 'value': 'quantity'})[['date','quantity']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by taking a look at the data:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_floating_storage.plot(x='date',y='quantity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next lesson we will discuss choosing an appropriate ARIMA model. For now, we will start with a first-order autoregressive model, ARIMA(1,0,0). We will fit this onto the first 6 weeks of data, and use the 7th week to test the model's predictions:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from statsmodels.tsa.arima.model import ARIMA\n", "arima = ARIMA(df_floating_storage['quantity'].head(42),order=(1,0,0))\n", "res = arima.fit()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a summary of our fit:" ] }, { "cell_type": "code", "execution_count": 6, "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", "
SARIMAX Results
Dep. Variable: quantity No. Observations: 42
Model: ARIMA(1, 0, 0) Log Likelihood -685.209
Date: Fri, 11 Dec 2020 AIC 1376.418
Time: 16:21:57 BIC 1381.631
Sample: 0 HQIC 1378.329
- 42
Covariance Type: opg
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
const 5.112e+07 9.75e-10 5.24e+16 0.000 5.11e+07 5.11e+07
ar.L1 0.8094 0.078 10.433 0.000 0.657 0.961
sigma2 8.615e+12 1.22e-15 7.04e+27 0.000 8.61e+12 8.61e+12
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Ljung-Box (L1) (Q): 0.19 Jarque-Bera (JB): 0.37
Prob(Q): 0.66 Prob(JB): 0.83
Heteroskedasticity (H): 0.35 Skew: -0.09
Prob(H) (two-sided): 0.06 Kurtosis: 3.42


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.94e+37. Standard errors may be unstable." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: quantity No. Observations: 42\n", "Model: ARIMA(1, 0, 0) Log Likelihood -685.209\n", "Date: Fri, 11 Dec 2020 AIC 1376.418\n", "Time: 16:21:57 BIC 1381.631\n", "Sample: 0 HQIC 1378.329\n", " - 42 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.112e+07 9.75e-10 5.24e+16 0.000 5.11e+07 5.11e+07\n", "ar.L1 0.8094 0.078 10.433 0.000 0.657 0.961\n", "sigma2 8.615e+12 1.22e-15 7.04e+27 0.000 8.61e+12 8.61e+12\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.19 Jarque-Bera (JB): 0.37\n", "Prob(Q): 0.66 Prob(JB): 0.83\n", "Heteroskedasticity (H): 0.35 Skew: -0.09\n", "Prob(H) (two-sided): 0.06 Kurtosis: 3.42\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "[2] Covariance matrix is singular or near-singular, with condition number 1.94e+37. Standard errors may be unstable.\n", "\"\"\"" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by simply getting the predictions of the model over the time range on which it was fitted. We do this as follows:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pred = res.get_prediction()\n", "df_floating_storage['prediction'] = pred.predicted_mean\n", "df_floating_storage.plot(x='date',y=['quantity','prediction'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's extend our prediction to the week following the fitted region:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pred = res.get_prediction(end=49)\n", "df_floating_storage['prediction'] = pred.predicted_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also add to our plot the 95% confidence region. We can put these values into our dataframe as follows:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df_floating_storage['lower'] = pred.conf_int()['lower quantity']\n", "df_floating_storage['upper'] = pred.conf_int()['upper quantity']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `fill_between` function to shade the confidence region in green, with 10% opacity. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = df_floating_storage.plot(x='date',y=['quantity','prediction'])\n", "ax.fill_between(df_floating_storage['date'].values,df_floating_storage['lower'],df_floating_storage['upper'],color='g',alpha=.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The true values do indeed lie within the confidence interval. We can also check whether our model is biased by looking at a plot of the residuals:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pd.DataFrame(res.resid).plot(kind='kde')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this looks roughly normal and is not strongly skewed, our model is not particularly biased.\n", "\n", "### Exercise\n", "\n", "Have a go at repeating this analysis (fitting an ARIMA(1,0,0) model, predicting, and looking at residuals) for a different time series, e.g. US crude exports." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }