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ML-For-Beginners/7-TimeSeries/2-ARIMA/solution/notebook.ipynb

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{
"cells": [
{
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"cell_type": "markdown",
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"source": [
"# Time series forecasting with ARIMA\n",
"\n",
"In this notebook, we demonstrate how to:\n",
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"- prepare time series data for training an ARIMA time series forecasting model\n",
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"- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
"- evaluate the model \n",
"\n",
"\n",
"The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
"\n",
"<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
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],
"metadata": {}
},
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{
"cell_type": "markdown",
"source": [
"## Install Dependencies\n",
"Get started by installing some of the required dependencies. These libraries with their corresponding versions are known to work for the solution:\n",
"\n",
"* `statsmodels == 0.12.2`\n",
"* `matplotlib == 3.4.2`\n",
"* `scikit-learn == 0.24.2`\n"
],
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 16,
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"source": [
"!pip install statsmodels"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"/bin/sh: pip: command not found\n"
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]
}
],
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"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 17,
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"source": [
"import os\n",
"import warnings\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import datetime as dt\n",
"import math\n",
"\n",
"from pandas.plotting import autocorrelation_plot\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from common.utils import load_data, mape\n",
"from IPython.display import Image\n",
"\n",
"%matplotlib inline\n",
"pd.options.display.float_format = '{:,.2f}'.format\n",
"np.set_printoptions(precision=2)\n",
"warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
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],
"outputs": [],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 18,
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"source": [
"energy = load_data('./data')[['load']]\n",
"energy.head(10)"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>load</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-01-01 00:00:00</th>\n",
" <td>2,698.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 01:00:00</th>\n",
" <td>2,558.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 02:00:00</th>\n",
" <td>2,444.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 03:00:00</th>\n",
" <td>2,402.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 04:00:00</th>\n",
" <td>2,403.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 05:00:00</th>\n",
" <td>2,453.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 06:00:00</th>\n",
" <td>2,560.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 07:00:00</th>\n",
" <td>2,719.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 08:00:00</th>\n",
" <td>2,916.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 09:00:00</th>\n",
" <td>3,105.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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],
"text/plain": [
" load\n",
"2012-01-01 00:00:00 2,698.00\n",
"2012-01-01 01:00:00 2,558.00\n",
"2012-01-01 02:00:00 2,444.00\n",
"2012-01-01 03:00:00 2,402.00\n",
"2012-01-01 04:00:00 2,403.00\n",
"2012-01-01 05:00:00 2,453.00\n",
"2012-01-01 06:00:00 2,560.00\n",
"2012-01-01 07:00:00 2,719.00\n",
"2012-01-01 08:00:00 2,916.00\n",
"2012-01-01 09:00:00 3,105.00"
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]
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},
"metadata": {},
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"execution_count": 18
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}
],
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"metadata": {}
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},
{
"cell_type": "markdown",
"source": [
"Plot all available load data (January 2012 to Dec 2014)"
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],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 19,
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"source": [
"energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
"plt.xlabel('timestamp', fontsize=12)\n",
"plt.ylabel('load', fontsize=12)\n",
"plt.show()"
],
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"outputs": [
{
"output_type": "display_data",
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
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]
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},
"metadata": {
"needs_background": "light"
}
}
],
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"metadata": {}
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},
{
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"cell_type": "markdown",
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"source": [
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"## Create training and testing data sets\n"
],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 20,
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"source": [
"train_start_dt = '2014-11-01 00:00:00'\n",
"test_start_dt = '2014-12-30 00:00:00' "
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],
"outputs": [],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 21,
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"source": [
"energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
" .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
" .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
"plt.xlabel('timestamp', fontsize=12)\n",
"plt.ylabel('load', fontsize=12)\n",
"plt.show()"
],
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"outputs": [
{
"output_type": "display_data",
"data": {
rolling back solution notebook to original example by FL
3 years ago
"image/png": "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
editing solution notebook supporting edit for HORIZON loop
3 years ago
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
rolling back solution notebook to original example by FL
3 years ago
]
time series work
4 years ago
},
"metadata": {
"needs_background": "light"
}
}
],
editing solution notebook supporting edit for HORIZON loop
3 years ago
"metadata": {}
time series 1
4 years ago
},
{
"cell_type": "code",
rolling back solution notebook to original example by FL
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"execution_count": 22,
editing solution notebook supporting edit for HORIZON loop
3 years ago
"source": [
"train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
"test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
"\n",
"print('Training data shape: ', train.shape)\n",
"print('Test data shape: ', test.shape)"
],
time series work
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
editing solution notebook supporting edit for HORIZON loop
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"Training data shape: (1416, 1)\n",
"Test data shape: (48, 1)\n"
time series work
4 years ago
]
}
],
editing solution notebook supporting edit for HORIZON loop
3 years ago
"metadata": {}
time series 1
4 years ago
},
{
"cell_type": "code",
rolling back solution notebook to original example by FL
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"execution_count": 23,
editing solution notebook supporting edit for HORIZON loop
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"source": [
"scaler = MinMaxScaler()\n",
"train['load'] = scaler.fit_transform(train)\n",
"train.head(10)"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
editing solution notebook supporting edit for HORIZON loop
3 years ago
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>load</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-11-01 00:00:00</th>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 01:00:00</th>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 02:00:00</th>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 03:00:00</th>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 04:00:00</th>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 05:00:00</th>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 06:00:00</th>\n",
" <td>0.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 07:00:00</th>\n",
" <td>0.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 08:00:00</th>\n",
" <td>0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-11-01 09:00:00</th>\n",
" <td>0.48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
rolling back solution notebook to original example by FL
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],
"text/plain": [
" load\n",
"2014-11-01 00:00:00 0.10\n",
"2014-11-01 01:00:00 0.07\n",
"2014-11-01 02:00:00 0.05\n",
"2014-11-01 03:00:00 0.04\n",
"2014-11-01 04:00:00 0.06\n",
"2014-11-01 05:00:00 0.10\n",
"2014-11-01 06:00:00 0.19\n",
"2014-11-01 07:00:00 0.31\n",
"2014-11-01 08:00:00 0.40\n",
"2014-11-01 09:00:00 0.48"
editing solution notebook supporting edit for HORIZON loop
3 years ago
]
time series work
4 years ago
},
"metadata": {},
rolling back solution notebook to original example by FL
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"execution_count": 23
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4 years ago
}
],
editing solution notebook supporting edit for HORIZON loop
3 years ago
"metadata": {}
time series 1
4 years ago
},
{
"cell_type": "markdown",
"source": [
"Original vs scaled data:"
editing solution notebook supporting edit for HORIZON loop
3 years ago
],
"metadata": {}
time series 1
4 years ago
},
{
"cell_type": "code",
rolling back solution notebook to original example by FL
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"execution_count": 24,
editing solution notebook supporting edit for HORIZON loop
3 years ago
"source": [
"energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
"train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
"plt.show()"
],
time series work
4 years ago
"outputs": [
{
"output_type": "display_data",
"data": {
rolling back solution notebook to original example by FL
3 years ago
"image/png": "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
editing solution notebook supporting edit for HORIZON loop
3 years ago
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
rolling back solution notebook to original example by FL
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]
time series work
4 years ago
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
rolling back solution notebook to original example by FL
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editing solution notebook supporting edit for HORIZON loop
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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},
"metadata": {
"needs_background": "light"
}
}
],
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"metadata": {}
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{
"cell_type": "markdown",
"source": [
"Let's also scale the test data"
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"metadata": {}
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{
"cell_type": "code",
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"execution_count": 25,
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"source": [
"test['load'] = scaler.transform(test)\n",
"test.head()"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>load</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-12-30 00:00:00</th>\n",
" <td>0.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 01:00:00</th>\n",
" <td>0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 02:00:00</th>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 03:00:00</th>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 04:00:00</th>\n",
" <td>0.30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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],
"text/plain": [
" load\n",
"2014-12-30 00:00:00 0.33\n",
"2014-12-30 01:00:00 0.29\n",
"2014-12-30 02:00:00 0.27\n",
"2014-12-30 03:00:00 0.27\n",
"2014-12-30 04:00:00 0.30"
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},
"metadata": {},
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"execution_count": 25
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}
],
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"metadata": {}
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{
"cell_type": "markdown",
"source": [
"## Implement ARIMA method"
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"metadata": {}
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{
"cell_type": "code",
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"execution_count": 26,
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"source": [
"# Specify the number of steps to forecast ahead\n",
"HORIZON = 3\n",
"print('Forecasting horizon:', HORIZON, 'hours')"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Forecasting horizon: 3 hours\n"
]
}
],
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"metadata": {}
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{
"cell_type": "code",
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"execution_count": 27,
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"source": [
"order = (4, 1, 0)\n",
"seasonal_order = (1, 1, 0, 24)\n",
"\n",
"model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
"results = model.fit()\n",
"\n",
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"print(results.summary())\n"
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],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" SARIMAX Results \n",
"==========================================================================================\n",
"Dep. Variable: load No. Observations: 1416\n",
"Model: SARIMAX(4, 1, 0)x(1, 1, 0, 24) Log Likelihood 3477.239\n",
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"Date: Thu, 30 Sep 2021 AIC -6942.477\n",
"Time: 14:36:28 BIC -6911.050\n",
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"Sample: 11-01-2014 HQIC -6930.725\n",
" - 12-29-2014 \n",
"Covariance Type: opg \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"ar.L1 0.8403 0.016 52.226 0.000 0.809 0.872\n",
"ar.L2 -0.5220 0.034 -15.388 0.000 -0.588 -0.456\n",
"ar.L3 0.1536 0.044 3.470 0.001 0.067 0.240\n",
"ar.L4 -0.0778 0.036 -2.158 0.031 -0.148 -0.007\n",
"ar.S.L24 -0.2327 0.024 -9.718 0.000 -0.280 -0.186\n",
"sigma2 0.0004 8.32e-06 47.358 0.000 0.000 0.000\n",
"===================================================================================\n",
"Ljung-Box (L1) (Q): 0.05 Jarque-Bera (JB): 1464.60\n",
"Prob(Q): 0.83 Prob(JB): 0.00\n",
"Heteroskedasticity (H): 0.84 Skew: 0.14\n",
"Prob(H) (two-sided): 0.07 Kurtosis: 8.02\n",
"===================================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
]
}
],
"metadata": {}
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{
"cell_type": "markdown",
"source": [
"## Evaluate the model"
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"metadata": {}
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{
"cell_type": "markdown",
"source": [
"Create a test data point for each HORIZON step."
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"metadata": {}
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{
"cell_type": "code",
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"execution_count": 28,
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"source": [
"test_shifted = test.copy()\n",
"\n",
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"for t in range(1, HORIZON):\n",
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" test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
" \n",
"test_shifted = test_shifted.dropna(how='any')\n",
"test_shifted.head(5)"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>load</th>\n",
" <th>load+1</th>\n",
" <th>load+2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-12-30 00:00:00</th>\n",
" <td>0.33</td>\n",
" <td>0.29</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 01:00:00</th>\n",
" <td>0.29</td>\n",
" <td>0.27</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 02:00:00</th>\n",
" <td>0.27</td>\n",
" <td>0.27</td>\n",
" <td>0.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 03:00:00</th>\n",
" <td>0.27</td>\n",
" <td>0.30</td>\n",
" <td>0.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30 04:00:00</th>\n",
" <td>0.30</td>\n",
" <td>0.41</td>\n",
" <td>0.57</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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],
"text/plain": [
" load load+1 load+2\n",
"2014-12-30 00:00:00 0.33 0.29 0.27\n",
"2014-12-30 01:00:00 0.29 0.27 0.27\n",
"2014-12-30 02:00:00 0.27 0.27 0.30\n",
"2014-12-30 03:00:00 0.27 0.30 0.41\n",
"2014-12-30 04:00:00 0.30 0.41 0.57"
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]
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},
"metadata": {},
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"execution_count": 28
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}
],
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"metadata": {}
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{
"cell_type": "markdown",
"source": [
"Make predictions on the test data"
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"metadata": {}
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{
"cell_type": "code",
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"execution_count": 29,
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"source": [
"%%time\n",
"training_window = 720 # dedicate 30 days (720 hours) for training\n",
"\n",
"train_ts = train['load']\n",
"test_ts = test_shifted\n",
"\n",
"history = [x for x in train_ts]\n",
"history = history[(-training_window):]\n",
"\n",
"predictions = list()\n",
"\n",
"# let's user simpler model for demonstration\n",
"order = (2, 1, 0)\n",
"seasonal_order = (1, 1, 0, 24)\n",
"\n",
"for t in range(test_ts.shape[0]):\n",
" model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
" model_fit = model.fit()\n",
" yhat = model_fit.forecast(steps = HORIZON)\n",
" predictions.append(yhat)\n",
" obs = list(test_ts.iloc[t])\n",
" # move the training window\n",
" history.append(obs[0])\n",
" history.pop(0)\n",
" print(test_ts.index[t])\n",
" print(t+1, ': predicted =', yhat, 'expected =', obs)"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2014-12-30 00:00:00\n",
"1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
"2014-12-30 01:00:00\n",
"2 : predicted = [0.3 0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
"2014-12-30 02:00:00\n",
"3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
"2014-12-30 03:00:00\n",
"4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
"2014-12-30 04:00:00\n",
"5 : predicted = [0.3 0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
"2014-12-30 05:00:00\n",
"6 : predicted = [0.4 0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
"2014-12-30 06:00:00\n",
"7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
"2014-12-30 07:00:00\n",
"8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
"2014-12-30 08:00:00\n",
"9 : predicted = [0.75 0.8 0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
"2014-12-30 09:00:00\n",
"10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
"2014-12-30 10:00:00\n",
"11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
"2014-12-30 11:00:00\n",
"12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
"2014-12-30 12:00:00\n",
"13 : predicted = [0.7 0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
"2014-12-30 13:00:00\n",
"14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
"2014-12-30 14:00:00\n",
"15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
"2014-12-30 15:00:00\n",
"16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
"2014-12-30 16:00:00\n",
"17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
"2014-12-30 17:00:00\n",
"18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
"2014-12-30 18:00:00\n",
"19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
"2014-12-30 19:00:00\n",
"20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
"2014-12-30 20:00:00\n",
"21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
"2014-12-30 21:00:00\n",
"22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
"2014-12-30 22:00:00\n",
"23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
"2014-12-30 23:00:00\n",
"24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
"2014-12-31 00:00:00\n",
"25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
"2014-12-31 01:00:00\n",
"26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
"2014-12-31 02:00:00\n",
"27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
"2014-12-31 03:00:00\n",
"28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
"2014-12-31 04:00:00\n",
"29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
"2014-12-31 05:00:00\n",
"30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
"2014-12-31 06:00:00\n",
"31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
"2014-12-31 07:00:00\n",
"32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
"2014-12-31 08:00:00\n",
"33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
"2014-12-31 09:00:00\n",
"34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
"2014-12-31 10:00:00\n",
"35 : predicted = [0.8 0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
"2014-12-31 11:00:00\n",
"36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
"2014-12-31 12:00:00\n",
"37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
"2014-12-31 13:00:00\n",
"38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
"2014-12-31 14:00:00\n",
"39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
"2014-12-31 15:00:00\n",
"40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
"2014-12-31 16:00:00\n",
"41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
"2014-12-31 17:00:00\n",
"42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
"2014-12-31 18:00:00\n",
"43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
"2014-12-31 19:00:00\n",
"44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
"2014-12-31 20:00:00\n",
"45 : predicted = [0.7 0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
"2014-12-31 21:00:00\n",
"46 : predicted = [0.6 0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
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"CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
"Wall time: 2min 36s\n"
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]
}
],
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"metadata": {
"scrolled": true
}
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},
{
"cell_type": "markdown",
"source": [
"Compare predictions to actual load"
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],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 30,
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"source": [
"eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
"eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
"eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
"eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
"eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
"eval_df.head()"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>h</th>\n",
" <th>prediction</th>\n",
" <th>actual</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-12-30 00:00:00</td>\n",
" <td>t+1</td>\n",
" <td>3,008.74</td>\n",
" <td>3,023.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-12-30 01:00:00</td>\n",
" <td>t+1</td>\n",
" <td>2,955.53</td>\n",
" <td>2,935.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-12-30 02:00:00</td>\n",
" <td>t+1</td>\n",
" <td>2,900.17</td>\n",
" <td>2,899.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-12-30 03:00:00</td>\n",
" <td>t+1</td>\n",
" <td>2,917.69</td>\n",
" <td>2,886.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-12-30 04:00:00</td>\n",
" <td>t+1</td>\n",
" <td>2,946.99</td>\n",
" <td>2,963.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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],
"text/plain": [
" timestamp h prediction actual\n",
"0 2014-12-30 00:00:00 t+1 3,008.74 3,023.00\n",
"1 2014-12-30 01:00:00 t+1 2,955.53 2,935.00\n",
"2 2014-12-30 02:00:00 t+1 2,900.17 2,899.00\n",
"3 2014-12-30 03:00:00 t+1 2,917.69 2,886.00\n",
"4 2014-12-30 04:00:00 t+1 2,946.99 2,963.00"
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]
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},
"metadata": {},
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"execution_count": 30
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}
],
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"metadata": {}
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},
{
"cell_type": "markdown",
"source": [
"Compute the **mean absolute percentage error (MAPE)** over all predictions\n",
"\n",
"$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$"
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],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 31,
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"source": [
"if(HORIZON > 1):\n",
" eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
" print(eval_df.groupby('h')['APE'].mean())"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"h\n",
"t+1 0.01\n",
"t+2 0.01\n",
"t+3 0.02\n",
"Name: APE, dtype: float64\n"
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]
}
],
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"metadata": {}
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},
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{
"cell_type": "markdown",
"source": [],
"metadata": {}
},
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{
"cell_type": "code",
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"execution_count": 32,
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"source": [
"print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"One step forecast MAPE: 0.5570581332313952 %\n"
]
}
],
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"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 33,
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"source": [
"print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
],
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Multi-step forecast MAPE: 1.1460048657704118 %\n"
]
}
],
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"metadata": {}
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},
{
"cell_type": "markdown",
"source": [
"Plot the predictions vs the actuals for the first week of the test set"
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],
"metadata": {}
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},
{
"cell_type": "code",
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"execution_count": 34,
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"source": [
"if(HORIZON == 1):\n",
" ## Plotting single step forecast\n",
" eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
"\n",
"else:\n",
" ## Plotting multi step forecast\n",
" plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
" for t in range(1, HORIZON+1):\n",
" plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
"\n",
" fig = plt.figure(figsize=(15, 8))\n",
" ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
" ax = fig.add_subplot(111)\n",
" for t in range(1, HORIZON+1):\n",
" x = plot_df['timestamp'][(t-1):]\n",
" y = plot_df['t+'+str(t)][0:len(x)]\n",
" ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
" \n",
" ax.legend(loc='best')\n",
" \n",
"plt.xlabel('timestamp', fontsize=12)\n",
"plt.ylabel('load', fontsize=12)\n",
"plt.show()"
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],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"No handles with labels found to put in legend.\n"
]
},
{
"output_type": "display_data",
"data": {
rolling back solution notebook to original example by FL
3 years ago
"image/png": "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
editing solution notebook supporting edit for HORIZON loop
3 years ago
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
rolling back solution notebook to original example by FL
3 years ago
]
editing solution notebook supporting edit for HORIZON loop
3 years ago
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
time series 1
4 years ago
},
{
"cell_type": "code",
"execution_count": null,
editing solution notebook supporting edit for HORIZON loop
3 years ago
"source": [],
time series 1
4 years ago
"outputs": [],
editing solution notebook supporting edit for HORIZON loop
3 years ago
"metadata": {}
time series 1
4 years ago
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"kernelspec": {
inform about required libraries and versions
3 years ago
"name": "python3",
rolling back solution notebook to original example by FL
3 years ago
"display_name": "Python 3.7.0 64-bit"
time series 1
4 years ago
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
rolling back solution notebook to original example by FL
3 years ago
"version": "3.7.0"
time series 1
4 years ago
},
"nteract": {
"version": "nteract-front-end@1.0.0"
time series work
4 years ago
},
"metadata": {
"interpreter": {
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
}
inform about required libraries and versions
3 years ago
},
"interpreter": {
rolling back solution notebook to original example by FL
3 years ago
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
time series 1
4 years ago
}
},
"nbformat": 4,
"nbformat_minor": 2
Changed pip to !pip
3 years ago
}