{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exogenous Variables and Time Series Forecasting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Region_Code</th>\n",
" <th>Total_Sales</th>\n",
" <th>Avg_Sales</th>\n",
" <th>Total_Orders</th>\n",
" <th>Avg_Orders</th>\n",
" <th>Num_Stores</th>\n",
" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2018-01-01</td>\n",
" <td>R4</td>\n",
" <td>2286812</td>\n",
" <td>45736</td>\n",
" <td>2914</td>\n",
" <td>58</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-01-01</td>\n",
" <td>R2</td>\n",
" <td>4436859</td>\n",
" <td>42256</td>\n",
" <td>5644</td>\n",
" <td>54</td>\n",
" <td>105</td>\n",
" <td>1</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-01-01</td>\n",
" <td>R3</td>\n",
" <td>3527439</td>\n",
" <td>41017</td>\n",
" <td>4599</td>\n",
" <td>53</td>\n",
" <td>86</td>\n",
" <td>1</td>\n",
" <td>86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2018-01-01</td>\n",
" <td>R1</td>\n",
" <td>5094374</td>\n",
" <td>41084</td>\n",
" <td>6509</td>\n",
" <td>52</td>\n",
" <td>124</td>\n",
" <td>1</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2018-01-02</td>\n",
" <td>R4</td>\n",
" <td>2545119</td>\n",
" <td>50902</td>\n",
" <td>3057</td>\n",
" <td>61</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>2019-05-30</td>\n",
" <td>R4</td>\n",
" <td>1966320</td>\n",
" <td>39326</td>\n",
" <td>2829</td>\n",
" <td>57</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>2019-05-31</td>\n",
" <td>R2</td>\n",
" <td>4351299</td>\n",
" <td>41441</td>\n",
" <td>6411</td>\n",
" <td>61</td>\n",
" <td>105</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>2019-05-31</td>\n",
" <td>R4</td>\n",
" <td>1909319</td>\n",
" <td>38186</td>\n",
" <td>2746</td>\n",
" <td>55</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>2019-05-31</td>\n",
" <td>R1</td>\n",
" <td>5900798</td>\n",
" <td>47587</td>\n",
" <td>9433</td>\n",
" <td>76</td>\n",
" <td>124</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2063</th>\n",
" <td>2019-05-31</td>\n",
" <td>R3</td>\n",
" <td>3440408</td>\n",
" <td>40005</td>\n",
" <td>5012</td>\n",
" <td>58</td>\n",
" <td>86</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2064 rows × 9 columns</p>\n",
"</div>"
],
"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": {
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" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
" <th>idx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>R4</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>R2</td>\n",
" <td>2018-01-01</td>\n",
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" <td>1</td>\n",
" <td>105</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>R3</td>\n",
" <td>2018-01-01</td>\n",
" <td>3527439</td>\n",
" <td>1</td>\n",
" <td>86</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>R1</td>\n",
" <td>2018-01-01</td>\n",
" <td>5094374</td>\n",
" <td>1</td>\n",
" <td>124</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>R4</td>\n",
" <td>2018-01-02</td>\n",
" <td>2545119</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <tr>\n",
" <th>2059</th>\n",
" <td>R4</td>\n",
" <td>2019-05-30</td>\n",
" <td>1966320</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>R4</td>\n",
" <td>2019-05-31</td>\n",
" <td>1909319</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>R1</td>\n",
" <td>2019-05-31</td>\n",
" <td>5900798</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>R2</td>\n",
" <td>2019-05-31</td>\n",
" <td>4351299</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2063</th>\n",
" <td>R3</td>\n",
" <td>2019-05-31</td>\n",
" <td>3440408</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>516</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2064 rows × 6 columns</p>\n",
"</div>"
],
"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": {
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" <td>0</td>\n",
" <td>93</td>\n",
" <td>412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1645</th>\n",
" <td>R4</td>\n",
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" <th>1646</th>\n",
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" <tr>\n",
" <th>1647</th>\n",
" <td>R3</td>\n",
" <td>2019-02-16</td>\n",
" <td>3960552</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>412</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1648 rows × 6 columns</p>\n",
"</div>"
],
"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",
"1640 R3 2019-02-15 3300873 0 73 411\n",
"1644 R2 2019-02-16 4485144 0 93 412\n",
"1645 R4 2019-02-16 2120472 0 49 412\n",
"1646 R1 2019-02-16 6426930 0 108 412\n",
"1647 R3 2019-02-16 3960552 0 76 412\n",
"\n",
"[1648 rows x 6 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <td>2019-02-18</td>\n",
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" <td>78</td>\n",
" <td>414</td>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>R4</td>\n",
" <td>2019-05-30</td>\n",
" <td>1966320</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>R4</td>\n",
" <td>2019-05-31</td>\n",
" <td>1909319</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>R1</td>\n",
" <td>2019-05-31</td>\n",
" <td>5900798</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>R2</td>\n",
" <td>2019-05-31</td>\n",
" <td>4351299</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2063</th>\n",
" <td>R3</td>\n",
" <td>2019-05-31</td>\n",
" <td>3440408</td>\n",
" <td>1</td>\n",
" <td>9</td>\n",
" <td>516</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>416 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" unique_id ds y Holiday Total_Discounts idx\n",
"1648 R3 2019-02-17 4253736 0 83 413\n",
"1649 R1 2019-02-17 6858420 0 120 413\n",
"1650 R4 2019-02-17 2341383 0 49 413\n",
"1651 R2 2019-02-17 4888986 0 99 413\n",
"1652 R3 2019-02-18 3948027 0 78 414\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",
"[416 rows x 6 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"104"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prediction_window = test['idx'].max() - train['idx'].max()\n",
"prediction_window"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train = train.drop(columns=['idx'], axis=1)\n",
"test = test.drop(columns=['idx'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": 13,
"metadata": {},
"outputs": [],
"source": [
"fcst = StatsForecast( models=models, freq=\"D\", n_jobs=-1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fcst.fit(train);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"forecast = fcst.predict(h=prediction_window, level=[95], X_df=test.drop(columns=['y'], axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>unique_id</th>\n",
" <th>ds</th>\n",
" <th>AutoARIMA</th>\n",
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" <td>4.580992e+06</td>\n",
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" <td>5626605.500</td>\n",
" <td>6445336.000</td>\n",
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" <td>R1</td>\n",
" <td>2019-02-19</td>\n",
" <td>4717097.500</td>\n",
" <td>2.828473e+06</td>\n",
" <td>6605722.500</td>\n",
" <td>3911237.750</td>\n",
" <td>1.795879e+06</td>\n",
" <td>6026596.500</td>\n",
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" <td>R1</td>\n",
" <td>2019-02-20</td>\n",
" <td>5539322.500</td>\n",
" <td>3.644876e+06</td>\n",
" <td>7433769.500</td>\n",
" <td>5014326.500</td>\n",
" <td>3.016612e+06</td>\n",
" <td>7012041.000</td>\n",
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" <td>8040920.000</td>\n",
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" <td>2019-02-21</td>\n",
" <td>5380298.000</td>\n",
" <td>3.484707e+06</td>\n",
" <td>7275888.500</td>\n",
" <td>4978095.000</td>\n",
" <td>2.719740e+06</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>411</th>\n",
" <td>R4</td>\n",
" <td>2019-05-27</td>\n",
" <td>2244195.000</td>\n",
" <td>1.554406e+06</td>\n",
" <td>2933983.750</td>\n",
" <td>2056336.500</td>\n",
" <td>1.980181e+06</td>\n",
" <td>2132491.750</td>\n",
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" <td>1646278.875</td>\n",
" <td>2105238.750</td>\n",
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" <tr>\n",
" <th>412</th>\n",
" <td>R4</td>\n",
" <td>2019-05-28</td>\n",
" <td>2234609.750</td>\n",
" <td>1.544821e+06</td>\n",
" <td>2924398.500</td>\n",
" <td>1933951.875</td>\n",
" <td>1.729963e+06</td>\n",
" <td>2137941.000</td>\n",
" <td>1820966.250</td>\n",
" <td>1588354.000</td>\n",
" <td>2053578.625</td>\n",
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" <tr>\n",
" <th>413</th>\n",
" <td>R4</td>\n",
" <td>2019-05-29</td>\n",
" <td>2052491.625</td>\n",
" <td>1.362703e+06</td>\n",
" <td>2742280.250</td>\n",
" <td>1975758.500</td>\n",
" <td>1.717912e+06</td>\n",
" <td>2233604.500</td>\n",
" <td>1684806.250</td>\n",
" <td>1307494.750</td>\n",
" <td>2062117.875</td>\n",
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" <th>414</th>\n",
" <td>R4</td>\n",
" <td>2019-05-30</td>\n",
" <td>1860788.375</td>\n",
" <td>1.171000e+06</td>\n",
" <td>2550577.000</td>\n",
" <td>1572959.375</td>\n",
" <td>1.177561e+06</td>\n",
" <td>1968357.625</td>\n",
" <td>1647948.625</td>\n",
" <td>1221317.625</td>\n",
" <td>2074579.625</td>\n",
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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": [
{
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"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, \"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": {},
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"source": [
"plot(train, test, forecast, \"R1\", \"MSTL\")\n",
"plot(train, test, forecast, \"R2\", \"MSTL\")\n",
"plot(train, test, forecast, \"R3\", \"MSTL\")\n",
"plot(train, test, forecast, \"R4\", \"MSTL\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
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"source": [
"plot(train, test, forecast, \"R1\", \"AutoMFLES\")\n",
"plot(train, test, forecast, \"R2\", \"AutoMFLES\")\n",
"plot(train, test, forecast, \"R3\", \"AutoMFLES\")\n",
"plot(train, test, forecast, \"R4\", \"AutoMFLES\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Global Forecasting"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Total_Sales</th>\n",
" <th>Avg_Sales</th>\n",
" <th>Total_Orders</th>\n",
" <th>Avg_Orders</th>\n",
" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
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" <th>0</th>\n",
" <td>2018-01-01</td>\n",
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" <td>365</td>\n",
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" <th>1</th>\n",
" <td>2018-01-02</td>\n",
" <td>19592415</td>\n",
" <td>53678</td>\n",
" <td>25326</td>\n",
" <td>69</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
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" <th>2</th>\n",
" <td>2018-01-03</td>\n",
" <td>18652527</td>\n",
" <td>51103</td>\n",
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" <td>66</td>\n",
" <td>0</td>\n",
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" <th>3</th>\n",
" <td>2018-01-04</td>\n",
" <td>19956267</td>\n",
" <td>54675</td>\n",
" <td>25584</td>\n",
" <td>70</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
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" <th>4</th>\n",
" <td>2018-01-05</td>\n",
" <td>22902651</td>\n",
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" <td>28436</td>\n",
" <td>78</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>511</th>\n",
" <td>2019-05-27</td>\n",
" <td>17197023</td>\n",
" <td>47115</td>\n",
" <td>25447</td>\n",
" <td>70</td>\n",
" <td>0</td>\n",
" <td>321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>2019-05-28</td>\n",
" <td>18652065</td>\n",
" <td>51102</td>\n",
" <td>27184</td>\n",
" <td>74</td>\n",
" <td>0</td>\n",
" <td>319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>513</th>\n",
" <td>2019-05-29</td>\n",
" <td>16213497</td>\n",
" <td>44421</td>\n",
" <td>24047</td>\n",
" <td>66</td>\n",
" <td>0</td>\n",
" <td>193</td>\n",
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" <tr>\n",
" <th>514</th>\n",
" <td>2019-05-30</td>\n",
" <td>16082139</td>\n",
" <td>44061</td>\n",
" <td>24318</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
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" <tr>\n",
" <th>515</th>\n",
" <td>2019-05-31</td>\n",
" <td>15601825</td>\n",
" <td>42745</td>\n",
" <td>23602</td>\n",
" <td>65</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
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"</table>\n",
"<p>516 rows × 7 columns</p>\n",
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"text/plain": [
" Date Total_Sales Avg_Sales Total_Orders Avg_Orders Holiday \\\n",
"0 2018-01-01 15345484 42042 19666 54 1 \n",
"1 2018-01-02 19592415 53678 25326 69 0 \n",
"2 2018-01-03 18652527 51103 24047 66 0 \n",
"3 2018-01-04 19956267 54675 25584 70 0 \n",
"4 2018-01-05 22902651 62747 28436 78 0 \n",
".. ... ... ... ... ... ... \n",
"511 2019-05-27 17197023 47115 25447 70 0 \n",
"512 2019-05-28 18652065 51102 27184 74 0 \n",
"513 2019-05-29 16213497 44421 24047 66 0 \n",
"514 2019-05-30 16082139 44061 24318 67 0 \n",
"515 2019-05-31 15601825 42745 23602 65 1 \n",
"\n",
" Total_Discounts \n",
"0 365 \n",
"1 365 \n",
"2 365 \n",
"3 364 \n",
"4 364 \n",
".. ... \n",
"511 321 \n",
"512 319 \n",
"513 193 \n",
"514 76 \n",
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"train_agg"
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{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df = train_agg[[\"Date\", \"Total_Sales\" , \"Holiday\",\"Total_Discounts\"]]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>ds</th>\n",
" <th>y</th>\n",
" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
" <th>idx</th>\n",
" <th>unique_id</th>\n",
" </tr>\n",
" </thead>\n",
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" <th>0</th>\n",
" <td>2018-01-01</td>\n",
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" <td>365</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2018-01-02</td>\n",
" <td>19592415</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-01-03</td>\n",
" <td>18652527</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2018-01-04</td>\n",
" <td>19956267</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2018-01-05</td>\n",
" <td>22902651</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>511</th>\n",
" <td>2019-05-27</td>\n",
" <td>17197023</td>\n",
" <td>0</td>\n",
" <td>321</td>\n",
" <td>512</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>2019-05-28</td>\n",
" <td>18652065</td>\n",
" <td>0</td>\n",
" <td>319</td>\n",
" <td>513</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>513</th>\n",
" <td>2019-05-29</td>\n",
" <td>16213497</td>\n",
" <td>0</td>\n",
" <td>193</td>\n",
" <td>514</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>514</th>\n",
" <td>2019-05-30</td>\n",
" <td>16082139</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>515</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>515</th>\n",
" <td>2019-05-31</td>\n",
" <td>15601825</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>516</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>516 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" ds y Holiday Total_Discounts idx unique_id\n",
"0 2018-01-01 15345484 1 365 1 1\n",
"1 2018-01-02 19592415 0 365 2 1\n",
"2 2018-01-03 18652527 0 365 3 1\n",
"3 2018-01-04 19956267 0 364 4 1\n",
"4 2018-01-05 22902651 0 364 5 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",
"[516 rows x 6 columns]"
]
},
"execution_count": 18,
"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\",\"Date\": \"ds\"})\n",
"df=df.sort_values(by='ds')\n",
"df[\"unique_id\"] = 1\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th></th>\n",
" <th>ds</th>\n",
" <th>y</th>\n",
" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
" <th>idx</th>\n",
" <th>unique_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2018-01-01</td>\n",
" <td>15345484</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2018-01-02</td>\n",
" <td>19592415</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2018-01-03</td>\n",
" <td>18652527</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2018-01-04</td>\n",
" <td>19956267</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2018-01-05</td>\n",
" <td>22902651</td>\n",
" <td>0</td>\n",
" <td>364</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>2019-02-12</td>\n",
" <td>12384495</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>408</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>408</th>\n",
" <td>2019-02-13</td>\n",
" <td>12457656</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>409</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>2019-02-14</td>\n",
" <td>13301838</td>\n",
" <td>0</td>\n",
" <td>139</td>\n",
" <td>410</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>410</th>\n",
" <td>2019-02-15</td>\n",
" <td>14306637</td>\n",
" <td>0</td>\n",
" <td>307</td>\n",
" <td>411</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>411</th>\n",
" <td>2019-02-16</td>\n",
" <td>16993098</td>\n",
" <td>0</td>\n",
" <td>326</td>\n",
" <td>412</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>412 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" ds y Holiday Total_Discounts idx unique_id\n",
"0 2018-01-01 15345484 1 365 1 1\n",
"1 2018-01-02 19592415 0 365 2 1\n",
"2 2018-01-03 18652527 0 365 3 1\n",
"3 2018-01-04 19956267 0 364 4 1\n",
"4 2018-01-05 22902651 0 364 5 1\n",
".. ... ... ... ... ... ...\n",
"407 2019-02-12 12384495 0 7 408 1\n",
"408 2019-02-13 12457656 0 39 409 1\n",
"409 2019-02-14 13301838 0 139 410 1\n",
"410 2019-02-15 14306637 0 307 411 1\n",
"411 2019-02-16 16993098 0 326 412 1\n",
"\n",
"[412 rows x 6 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ds</th>\n",
" <th>y</th>\n",
" <th>Holiday</th>\n",
" <th>Total_Discounts</th>\n",
" <th>idx</th>\n",
" <th>unique_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>2019-02-17</td>\n",
" <td>18342525</td>\n",
" <td>0</td>\n",
" <td>351</td>\n",
" <td>413</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>2019-02-18</td>\n",
" <td>16319103</td>\n",
" <td>0</td>\n",
" <td>319</td>\n",
" <td>414</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>414</th>\n",
" <td>2019-02-19</td>\n",
" <td>15816166</td>\n",
" <td>1</td>\n",
" <td>222</td>\n",
" <td>415</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>415</th>\n",
" <td>2019-02-20</td>\n",
" <td>12800094</td>\n",
" <td>0</td>\n",
" <td>59</td>\n",
" <td>416</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>416</th>\n",
" <td>2019-02-21</td>\n",
" <td>12383694</td>\n",
" <td>0</td>\n",
" <td>37</td>\n",
" <td>417</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>511</th>\n",
" <td>2019-05-27</td>\n",
" <td>17197023</td>\n",
" <td>0</td>\n",
" <td>321</td>\n",
" <td>512</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>512</th>\n",
" <td>2019-05-28</td>\n",
" <td>18652065</td>\n",
" <td>0</td>\n",
" <td>319</td>\n",
" <td>513</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>513</th>\n",
" <td>2019-05-29</td>\n",
" <td>16213497</td>\n",
" <td>0</td>\n",
" <td>193</td>\n",
" <td>514</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>514</th>\n",
" <td>2019-05-30</td>\n",
" <td>16082139</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>515</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>515</th>\n",
" <td>2019-05-31</td>\n",
" <td>15601825</td>\n",
" <td>1</td>\n",
" <td>39</td>\n",
" <td>516</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>104 rows × 6 columns</p>\n",
"</div>"
],
"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": {
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2019-02-17</td>\n",
" <td>17897164.0</td>\n",
" <td>13301840.0</td>\n",
" <td>22492488.0</td>\n",
" <td>20815704.0</td>\n",
" <td>18865470.0</td>\n",
" <td>22765938.0</td>\n",
" <td>16443518.0</td>\n",
" <td>15116372.00</td>\n",
" <td>17770664.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2019-02-18</td>\n",
" <td>17645540.0</td>\n",
" <td>12640227.0</td>\n",
" <td>22650854.0</td>\n",
" <td>17287290.0</td>\n",
" <td>15781578.0</td>\n",
" <td>18793000.0</td>\n",
" <td>16055208.0</td>\n",
" <td>15050656.00</td>\n",
" <td>17059760.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>2019-02-19</td>\n",
" <td>13183467.0</td>\n",
" <td>8105391.5</td>\n",
" <td>18261542.0</td>\n",
" <td>10942386.0</td>\n",
" <td>5298980.0</td>\n",
" <td>16585792.0</td>\n",
" <td>11509044.0</td>\n",
" <td>2970222.25</td>\n",
" <td>20047866.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
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" <td>2019-02-20</td>\n",
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" <td>13533622.0</td>\n",
" <td>8225240.0</td>\n",
" <td>18842004.0</td>\n",
" <td>12900191.0</td>\n",
" <td>4727806.50</td>\n",
" <td>21072574.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2019-02-21</td>\n",
" <td>14426889.0</td>\n",
" <td>9332863.0</td>\n",
" <td>19520914.0</td>\n",
" <td>13593423.0</td>\n",
" <td>7848842.5</td>\n",
" <td>19338004.0</td>\n",
" <td>12633228.0</td>\n",
" <td>3807427.25</td>\n",
" <td>21459028.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>1</td>\n",
" <td>2019-05-27</td>\n",
" <td>17838166.0</td>\n",
" <td>12724844.0</td>\n",
" <td>22951490.0</td>\n",
" <td>16683699.0</td>\n",
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"</table>\n",
"<p>104 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" unique_id ds AutoARIMA AutoARIMA-lo-95 AutoARIMA-hi-95 \\\n",
"0 1 2019-02-17 17897164.0 13301840.0 22492488.0 \n",
"1 1 2019-02-18 17645540.0 12640227.0 22650854.0 \n",
"2 1 2019-02-19 13183467.0 8105391.5 18261542.0 \n",
"3 1 2019-02-20 14814651.0 9723128.0 19906174.0 \n",
"4 1 2019-02-21 14426889.0 9332863.0 19520914.0 \n",
".. ... ... ... ... ... \n",
"99 1 2019-05-27 17838166.0 12724844.0 22951490.0 \n",
"100 1 2019-05-28 17814770.0 12701448.0 22928094.0 \n",
"101 1 2019-05-29 16340833.0 11227510.0 21454156.0 \n",
"102 1 2019-05-30 14972177.0 9858854.0 20085500.0 \n",
"103 1 2019-05-31 11072638.0 5959315.0 16185961.0 \n",
"\n",
" MSTL MSTL-lo-95 MSTL-hi-95 AutoMFLES AutoMFLES-lo-95 \\\n",
"0 20815704.0 18865470.0 22765938.0 16443518.0 15116372.00 \n",
"1 17287290.0 15781578.0 18793000.0 16055208.0 15050656.00 \n",
"2 10942386.0 5298980.0 16585792.0 11509044.0 2970222.25 \n",
"3 13533622.0 8225240.0 18842004.0 12900191.0 4727806.50 \n",
"4 13593423.0 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": [
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"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\")"
]
},
{
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"execution_count": null,
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"outputs": [],
"source": []
}
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