{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exogenous Variables and Time Series Forecasting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/gautamnaik1994/SalesForecasting_ML_CaseStudy/blob/main/notebooks/modelling/02.SARIMAX.ipynb?flush_cache=true)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import plotly.io as pio\n", "pio.renderers.default = \"colab+notebook_connected+vscode\"\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import duckdb as db\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import optuna\n", "import warnings\n", "\n", "warnings.filterwarnings('ignore')\n", "\n", "from IPython.display import display, Markdown\n", "from statsforecast import StatsForecast\n", "from statsforecast.models import AutoARIMA, MSTL, MFLES, AutoMFLES\n", "# ConformalIntervals\n", "from statsforecast.models import ConformalIntervals\n", "# mape\n", "from sklearn.metrics import mean_absolute_percentage_error\n", "# sarimax\n", "from statsmodels.tsa.statespace.sarimax import SARIMAX\n", "\n", "optuna.logging.set_verbosity(optuna.logging.ERROR)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "orig = pd.read_parquet(\"../../data/processed/train_enhanced.parquet\")\n", "train_agg = pd.read_parquet(\"../../data/processed/train_agg.parquet\")\n", "train_region_code_agg = pd.read_parquet(\"../../data/processed/train_region_code_agg.parquet\")\n", "holiday_df= pd.read_csv(\"../../data/processed/holidays.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Region Sales Forecasting" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateRegion_CodeTotal_SalesAvg_SalesTotal_OrdersAvg_OrdersNum_StoresHolidayTotal_Discounts
02018-01-01R422868124573629145850150
12018-01-01R24436859422565644541051105
22018-01-01R335274394101745995386186
32018-01-01R15094374410846509521241124
42018-01-02R425451195090230576150050
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20592019-05-30R41966320393262829575004
20602019-05-31R2435129941441641161105111
20612019-05-31R41909319381862746555011
20622019-05-31R1590079847587943376124118
20632019-05-31R33440408400055012588619
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2064 rows × 9 columns

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" ], "text/plain": [ " Date Region_Code Total_Sales Avg_Sales Total_Orders Avg_Orders \\\n", "0 2018-01-01 R4 2286812 45736 2914 58 \n", "1 2018-01-01 R2 4436859 42256 5644 54 \n", "2 2018-01-01 R3 3527439 41017 4599 53 \n", "3 2018-01-01 R1 5094374 41084 6509 52 \n", "4 2018-01-02 R4 2545119 50902 3057 61 \n", "... ... ... ... ... ... ... \n", "2059 2019-05-30 R4 1966320 39326 2829 57 \n", "2060 2019-05-31 R2 4351299 41441 6411 61 \n", "2061 2019-05-31 R4 1909319 38186 2746 55 \n", "2062 2019-05-31 R1 5900798 47587 9433 76 \n", "2063 2019-05-31 R3 3440408 40005 5012 58 \n", "\n", " Num_Stores Holiday Total_Discounts \n", "0 50 1 50 \n", "1 105 1 105 \n", "2 86 1 86 \n", "3 124 1 124 \n", "4 50 0 50 \n", "... ... ... ... \n", "2059 50 0 4 \n", "2060 105 1 11 \n", "2061 50 1 1 \n", "2062 124 1 18 \n", "2063 86 1 9 \n", "\n", "[2064 rows x 9 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_region_code_agg" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = train_region_code_agg[[\"Region_Code\" ,\"Date\", \"Total_Sales\" , \"Holiday\",\"Total_Discounts\"]]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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unique_iddsyHolidayTotal_Discountsidx
0R42018-01-0122868121501
1R22018-01-01443685911051
2R32018-01-0135274391861
3R12018-01-01509437411241
4R42018-01-0225451190502
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" ], "text/plain": [ " unique_id ds y Holiday Total_Discounts idx\n", "0 R4 2018-01-01 2286812 1 50 1\n", "1 R2 2018-01-01 4436859 1 105 1\n", "2 R3 2018-01-01 3527439 1 86 1\n", "3 R1 2018-01-01 5094374 1 124 1\n", "4 R4 2018-01-02 2545119 0 50 2\n", "... ... ... ... ... ... ...\n", "2059 R4 2019-05-30 1966320 0 4 515\n", "2061 R4 2019-05-31 1909319 1 1 516\n", "2062 R1 2019-05-31 5900798 1 18 516\n", "2060 R2 2019-05-31 4351299 1 11 516\n", "2063 R3 2019-05-31 3440408 1 9 516\n", "\n", "[2064 rows x 6 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "date_mapping = {date: idx + 1 for idx, date in enumerate(sorted(df['Date'].unique()))}\n", "df['idx'] = df['Date'].map(date_mapping)\n", "df = df.rename(columns={\"Total_Sales\": \"y\", \"Region_Code\": \"unique_id\", \"Date\": \"ds\"})\n", "df=df.sort_values(by='ds')\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "threshold = 0.8\n", "train = df[df['idx'] <= df['idx'].max() * threshold]\n", "test = df[df['idx'] > df['idx'].max() * threshold]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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unique_iddsyHolidayTotal_Discountsidx
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1R22018-01-01443685911051
2R32018-01-0135274391861
3R12018-01-01509437411241
4R42018-01-0225451190502
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unique_iddsyHolidayTotal_Discountsidx
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1649R12019-02-1768584200120413
1650R42019-02-172341383049413
1651R22019-02-174888986099413
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1R12019-02-186439762.0004.580992e+068298532.5006418570.5005.809900e+067027240.5006035970.5005626605.5006445336.000
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411R42019-05-272244195.0001.554406e+062933983.7502056336.5001.980181e+062132491.7501875758.8751646278.8752105238.750
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" ], "text/plain": [ " unique_id ds AutoARIMA AutoARIMA-lo-95 AutoARIMA-hi-95 \\\n", "0 R1 2019-02-17 6666489.000 4.967450e+06 8365528.000 \n", "1 R1 2019-02-18 6439762.000 4.580992e+06 8298532.500 \n", "2 R1 2019-02-19 4717097.500 2.828473e+06 6605722.500 \n", "3 R1 2019-02-20 5539322.500 3.644876e+06 7433769.500 \n", "4 R1 2019-02-21 5380298.000 3.484707e+06 7275888.500 \n", ".. ... ... ... ... ... \n", "411 R4 2019-05-27 2244195.000 1.554406e+06 2933983.750 \n", "412 R4 2019-05-28 2234609.750 1.544821e+06 2924398.500 \n", "413 R4 2019-05-29 2052491.625 1.362703e+06 2742280.250 \n", "414 R4 2019-05-30 1860788.375 1.171000e+06 2550577.000 \n", "415 R4 2019-05-31 1395954.375 7.061657e+05 2085743.125 \n", "\n", " MSTL MSTL-lo-95 MSTL-hi-95 AutoMFLES AutoMFLES-lo-95 \\\n", "0 7860963.500 7.173004e+06 8548923.000 6284394.000 5568465.000 \n", "1 6418570.500 5.809900e+06 7027240.500 6035970.500 5626605.500 \n", "2 3911237.750 1.795879e+06 6026596.500 4297116.000 1185046.375 \n", "3 5014326.500 3.016612e+06 7012041.000 5055353.000 2069786.250 \n", "4 4978095.000 2.719740e+06 7236449.000 4924604.000 1592243.250 \n", ".. ... ... ... ... ... \n", "411 2056336.500 1.980181e+06 2132491.750 1875758.875 1646278.875 \n", "412 1933951.875 1.729963e+06 2137941.000 1820966.250 1588354.000 \n", "413 1975758.500 1.717912e+06 2233604.500 1684806.250 1307494.750 \n", "414 1572959.375 1.177561e+06 1968357.625 1647948.625 1221317.625 \n", "415 1374726.000 9.185508e+05 1830901.250 1486565.500 1341881.250 \n", "\n", " AutoMFLES-hi-95 \n", "0 7000323.000 \n", "1 6445336.000 \n", "2 7409185.500 \n", "3 8040920.000 \n", "4 8256965.000 \n", ".. ... \n", "411 2105238.750 \n", "412 2053578.625 \n", "413 2062117.875 \n", "414 2074579.625 \n", "415 1631249.875 \n", "\n", "[416 rows x 11 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAPE using ARIMA: 0.1742272055815889\n", "MAPE using MSTL: 0.17424597681344803\n", "MAPE using AutoMFLES: 0.19126523176303173\n" ] } ], "source": [ "merged =test.merge(forecast, on=['unique_id', 'ds'], how='left')\n", "print(\"MAPE using ARIMA: \", mean_absolute_percentage_error(merged['y'], merged['AutoARIMA']))\n", "print(\"MAPE using MSTL: \", mean_absolute_percentage_error(merged['y'], merged['MSTL']))\n", "print(\"MAPE using AutoMFLES: \", mean_absolute_percentage_error(merged['y'], merged['AutoMFLES']))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "Train", "type": "scatter", "x": [ "2018-01-01T00:00:00", "2018-01-02T00:00:00", "2018-01-03T00:00:00", "2018-01-04T00:00:00", 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x=filtered_forecast['ds'], \n", " y=filtered_forecast[f\"{model_name}-lo-95\"], \n", " mode='lines', \n", " name='Lower Bound',\n", " fill='tonexty', # Fill area between this trace and the previous one\n", " fillcolor='rgba(0, 100, 80, 0.2)', # Set fill color with opacity\n", " line=dict(width=0),\n", " showlegend=False\n", " ))\n", "\n", "\n", " fig.update_layout(title=f'Total Sales Forecast for {unique_id}', xaxis_title='Date', yaxis_title='Total Sales')\n", " fig.show()\n", "\n", "plot(train, test, forecast, \"R1\", \"AutoARIMA\")\n", "plot(train, test, forecast, \"R2\", \"AutoARIMA\")\n", "plot(train, test, forecast, \"R3\", \"AutoARIMA\")\n", "plot(train, test, forecast, \"R4\", \"AutoARIMA\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "Train", "type": "scatter", "x": [ "2018-01-01T00:00:00", 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5122019-05-28186520655110227184740319
5132019-05-29162134974442124047660193
5142019-05-3016082139440612431867076
5152019-05-3115601825427452360265139
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" ], "text/plain": [ " ds y Holiday Total_Discounts idx unique_id\n", "412 2019-02-17 18342525 0 351 413 1\n", "413 2019-02-18 16319103 0 319 414 1\n", "414 2019-02-19 15816166 1 222 415 1\n", "415 2019-02-20 12800094 0 59 416 1\n", "416 2019-02-21 12383694 0 37 417 1\n", ".. ... ... ... ... ... ...\n", "511 2019-05-27 17197023 0 321 512 1\n", "512 2019-05-28 18652065 0 319 513 1\n", "513 2019-05-29 16213497 0 193 514 1\n", "514 2019-05-30 16082139 0 76 515 1\n", "515 2019-05-31 15601825 1 39 516 1\n", "\n", "[104 rows x 6 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "104" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prediction_window = test['idx'].max() - train['idx'].max()\n", "prediction_window" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "train = train.drop(columns=['idx'], axis=1)\n", "test = test.drop(columns=['idx'], axis=1)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "seasonality = [7, 12, 30]\n", "models = [\n", " AutoARIMA(\n", " season_length=12,\n", " d=0,\n", " approximation=True,\n", " stationary=True\n", " ),\n", " MSTL(season_length=[7, 12, 30],\n", " trend_forecaster=AutoARIMA(\n", " d=0,\n", " approximation=True,\n", " stationary=True,\n", " prediction_intervals=ConformalIntervals(h=prediction_window),\n", " )),\n", "\n", " AutoMFLES(\n", " season_length=seasonality,\n", " test_size=20,\n", " prediction_intervals=ConformalIntervals(h=prediction_window),\n", " )\n", "]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "fcst = StatsForecast( models=models, freq=\"D\", n_jobs=-1)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "fcst.fit(train);" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "forecast = fcst.predict(h=prediction_window, level=[95], X_df=test.drop(columns=['y'], axis=1))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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unique_iddsAutoARIMAAutoARIMA-lo-95AutoARIMA-hi-95MSTLMSTL-lo-95MSTL-hi-95AutoMFLESAutoMFLES-lo-95AutoMFLES-hi-95
012019-02-1717897164.013301840.022492488.020815704.018865470.022765938.016443518.015116372.0017770664.0
112019-02-1817645540.012640227.022650854.017287290.015781578.018793000.016055208.015050656.0017059760.0
212019-02-1913183467.08105391.518261542.010942386.05298980.016585792.011509044.02970222.2520047866.0
312019-02-2014814651.09723128.019906174.013533622.08225240.018842004.012900191.04727806.5021072574.0
412019-02-2114426889.09332863.019520914.013593423.07848842.519338004.012633228.03807427.2521459028.0
....................................
9912019-05-2717838166.012724844.022951490.016683699.016239398.017128000.016079478.015046672.0017112284.0
10012019-05-2817814770.012701448.022928094.015773848.014561388.016986308.016055208.014594145.0017516272.0
10112019-05-2916340833.011227510.021454156.015926136.013938983.017913290.014526238.011550098.0017502378.0
10212019-05-3014972177.09858854.020085500.012472893.09664525.015281260.013106480.010257496.0015955465.0
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7848842.5 19338004.0 12633228.0 3807427.25 \n", ".. ... ... ... ... ... \n", "99 16683699.0 16239398.0 17128000.0 16079478.0 15046672.00 \n", "100 15773848.0 14561388.0 16986308.0 16055208.0 14594145.00 \n", "101 15926136.0 13938983.0 17913290.0 14526238.0 11550098.00 \n", "102 12472893.0 9664525.0 15281260.0 13106480.0 10257496.00 \n", "103 10983630.0 7566960.5 14400299.0 9288397.0 8639927.00 \n", "\n", " AutoMFLES-hi-95 \n", "0 17770664.0 \n", "1 17059760.0 \n", "2 20047866.0 \n", "3 21072574.0 \n", "4 21459028.0 \n", ".. ... \n", "99 17112284.0 \n", "100 17516272.0 \n", "101 17502378.0 \n", "102 15955465.0 \n", "103 9936868.0 \n", "\n", "[104 rows x 11 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAPE using ARIMA: 0.17022523364912714\n", "MAPE using MSTL: 0.1693179254887603\n", "MAPE using AutoMFLES: 0.18515452838892862\n" ] } ], "source": [ "merged =test.merge(forecast, on=['unique_id', 'ds'], how='left')\n", "print(\"MAPE using ARIMA: \", mean_absolute_percentage_error(merged['y'], merged['AutoARIMA']))\n", "print(\"MAPE using MSTL: \", mean_absolute_percentage_error(merged['y'], merged['MSTL']))\n", "print(\"MAPE using AutoMFLES: \", mean_absolute_percentage_error(merged['y'], merged['AutoMFLES']))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "Train", "type": "scatter", "x": [ "2018-01-01T00:00:00", "2018-01-02T00:00:00", "2018-01-03T00:00:00", "2018-01-04T00:00:00", "2018-01-05T00:00:00", "2018-01-06T00:00:00", "2018-01-07T00:00:00", "2018-01-08T00:00:00", "2018-01-09T00:00:00", "2018-01-10T00:00:00", "2018-01-11T00:00:00", "2018-01-12T00:00:00", "2018-01-13T00:00:00", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot(train, test, forecast, unique_id, model_name):\n", " filtered_train = train[train['unique_id'] == unique_id]\n", " filtered_test = test[test['unique_id'] == unique_id]\n", " filtered_forecast = forecast[forecast['unique_id'] == unique_id]\n", "\n", " fig = go.Figure()\n", "\n", " fig.add_trace(go.Scatter(x=filtered_train['ds'], y=filtered_train['y'], mode='lines', name='Train'))\n", " fig.add_trace(go.Scatter(x=filtered_test['ds'], y=filtered_test['y'], mode='lines', name='Test'))\n", " fig.add_trace(go.Scatter(x=filtered_forecast['ds'], y=filtered_forecast[model_name], mode='lines', name='Forecast'))\n", " fig.add_trace(go.Scatter(\n", " x=filtered_forecast['ds'], \n", " y=filtered_forecast[f\"{model_name}-hi-95\"], \n", " mode='lines', \n", " name='Upper Bound',\n", " line=dict(width=0),\n", " showlegend=False\n", " ))\n", "\n", " # Add lower bound as an area plot\n", " fig.add_trace(go.Scatter(\n", " x=filtered_forecast['ds'], \n", " y=filtered_forecast[f\"{model_name}-lo-95\"], \n", " mode='lines', \n", " name='Lower Bound',\n", " fill='tonexty', # Fill area between this trace and the previous one\n", " fillcolor='rgba(0, 100, 80, 0.2)', # Set fill color with opacity\n", " line=dict(width=0),\n", " showlegend=False\n", " ))\n", "\n", "\n", " fig.update_layout(title=f'Total Sales Forecast for {unique_id}', xaxis_title='Date', yaxis_title='Total Sales')\n", " fig.show()\n", "\n", "plot(train, test, forecast, 1, \"AutoARIMA\")\n", "plot(train, test, forecast, 1, \"MSTL\")\n", "plot(train, test, forecast, 1, \"AutoMFLES\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }