diff --git a/Ricardo/Alternative-Time-Series/Alternative-Time-Series.ipynb b/Ricardo/Alternative-Time-Series/Alternative-Time-Series.ipynb new file mode 100644 index 0000000000..eef8dc9e3c --- /dev/null +++ b/Ricardo/Alternative-Time-Series/Alternative-Time-Series.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datesales
02022-09-03 17:10:08.07932855.292157
12022-09-04 17:10:08.07932853.803211
22022-09-05 17:10:08.07932858.141693
32022-09-06 17:10:08.07932864.530899
42022-09-07 17:10:08.07932866.013633
\n", + "
" + ], + "text/plain": [ + " date sales\n", + "0 2022-09-03 17:10:08.079328 55.292157\n", + "1 2022-09-04 17:10:08.079328 53.803211\n", + "2 2022-09-05 17:10:08.079328 58.141693\n", + "3 2022-09-06 17:10:08.079328 64.530899\n", + "4 2022-09-07 17:10:08.079328 66.013633" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "sales_data = pd.read_csv('/workspaces/machine-learning-python-template-ds-2023/Ricardo/Alternative-Time-Series/sales.csv')\n", + "\n", + "sales_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "date\n", + "2022-09-03 17:10:08.079328 55.292157\n", + "2022-09-04 17:10:08.079328 53.803211\n", + "2022-09-05 17:10:08.079328 58.141693\n", + "2022-09-06 17:10:08.079328 64.530899\n", + "2022-09-07 17:10:08.079328 66.013633\n", + "Name: sales, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sales_data['date'] = pd.to_datetime(sales_data['date'])\n", + "sales_data = sales_data.set_index('date')\n", + "time_series = sales_data['sales']\n", + "\n", + "time_series.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "fig, axis = plt.subplots(figsize=(15, 5))\n", + "\n", + "sns.lineplot(x=time_series.index, y=time_series.values)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Augmented Dickey-Fuller Test:\n" + ] + }, + { + "data": { + "text/plain": [ + "Test Statistic 0.545414\n", + "p-value 0.986190\n", + "# Lags Used 13.000000\n", + "# Observations Used 352.000000\n", + "Critical Value (1%) -3.449065\n", + "Critical Value (5%) -2.869786\n", + "Critical Value (10%) -2.571163\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from statsmodels.tsa.stattools import adfuller\n", + "\n", + "def adf_test(time_series):\n", + " print('Augmented Dickey-Fuller Test:')\n", + " adf_test = adfuller(time_series, autolag='AIC')\n", + " adf_output = pd.Series(adf_test[0:4], index=['Test Statistic', 'p-value', '# Lags Used', '# Observations Used'])\n", + " for key, value in adf_test[4].items():\n", + " adf_output['Critical Value (%s)' % key] = value\n", + " return adf_output\n", + "\n", + "adf_test(time_series)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from statsmodels.tsa.seasonal import seasonal_decompose\n", + "\n", + "decomposition = seasonal_decompose(time_series, model='additive', period=30)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trend = decomposition.trend\n", + "\n", + "fig, axis = plt.subplots(figsize=(15, 5))\n", + "\n", + "sns.lineplot(data=time_series)\n", + "sns.lineplot(data = trend)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sesonal = decomposition.seasonal\n", + "\n", + "fig, axis = plt.subplots(figsize=(15, 5))\n", + "\n", + "sns.lineplot(data=time_series)\n", + "sns.lineplot(data = sesonal)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "residual = decomposition.resid\n", + "\n", + "fig, axis = plt.subplots(figsize=(15, 5))\n", + "\n", + "sns.lineplot(data=time_series)\n", + "sns.lineplot(data = residual)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performing stepwise search to minimize aic\n", + " ARIMA(2,1,2)(0,0,0)[0] intercept : AIC=1843.274, Time=0.58 sec\n", + " ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=2096.542, Time=0.02 sec\n", + " ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=1970.972, Time=0.05 sec\n", + " ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=1839.586, Time=0.14 sec\n", + " ARIMA(0,1,0)(0,0,0)[0] : AIC=2209.657, Time=0.02 sec\n", + " ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=1839.552, Time=0.40 sec\n", + " ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=1841.532, Time=0.45 sec\n", + " ARIMA(1,1,2)(0,0,0)[0] intercept : AIC=1843.586, Time=0.18 sec\n", + " ARIMA(0,1,2)(0,0,0)[0] intercept : AIC=1839.607, Time=0.38 sec\n", + " ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=1922.648, Time=0.07 sec\n", + " ARIMA(1,1,1)(0,0,0)[0] : AIC=inf, Time=0.36 sec\n", + "\n", + "Best model: ARIMA(1,1,1)(0,0,0)[0] intercept\n", + "Total fit time: 2.701 seconds\n" + ] + } + ], + "source": [ + "from pmdarima import auto_arima\n", + "\n", + "model = auto_arima(time_series, trace=True, error_action='ignore', suppress_warnings=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2023-09-04 17:10:08.079328 1002.158148\n", + "2023-09-05 17:10:08.079328 1004.831669\n", + "2023-09-06 17:10:08.079328 1007.423289\n", + "2023-09-07 17:10:08.079328 1010.021630\n", + "2023-09-08 17:10:08.079328 1012.619419\n", + "2023-09-09 17:10:08.079328 1015.217254\n", + "2023-09-10 17:10:08.079328 1017.815084\n", + "2023-09-11 17:10:08.079328 1020.412915\n", + "2023-09-12 17:10:08.079328 1023.010746\n", + "2023-09-13 17:10:08.079328 1025.608577\n", + "2023-09-14 17:10:08.079328 1028.206409\n", + "2023-09-15 17:10:08.079328 1030.804240\n", + "2023-09-16 17:10:08.079328 1033.402071\n", + "2023-09-17 17:10:08.079328 1035.999902\n", + "2023-09-18 17:10:08.079328 1038.597733\n", + "2023-09-19 17:10:08.079328 1041.195564\n", + "2023-09-20 17:10:08.079328 1043.793395\n", + "2023-09-21 17:10:08.079328 1046.391226\n", + "2023-09-22 17:10:08.079328 1048.989057\n", + "2023-09-23 17:10:08.079328 1051.586888\n", + "2023-09-24 17:10:08.079328 1054.184719\n", + "2023-09-25 17:10:08.079328 1056.782550\n", + "2023-09-26 17:10:08.079328 1059.380381\n", + "2023-09-27 17:10:08.079328 1061.978212\n", + "2023-09-28 17:10:08.079328 1064.576043\n", + "2023-09-29 17:10:08.079328 1067.173874\n", + "2023-09-30 17:10:08.079328 1069.771705\n", + "2023-10-01 17:10:08.079328 1072.369536\n", + "2023-10-02 17:10:08.079328 1074.967367\n", + "2023-10-03 17:10:08.079328 1077.565198\n", + "Freq: D, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast = model.predict(n_periods=30)\n", + "forecast" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axis = plt.subplots(figsize=(15, 5))\n", + "\n", + "sns.lineplot(data = time_series)\n", + "sns.lineplot(data=forecast, c= 'red')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ricardo/Alternative-Time-Series/sales.csv b/Ricardo/Alternative-Time-Series/sales.csv new file mode 100644 index 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