{ "cells": [ { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error\n", "from statsmodels.stats.stattools import durbin_watson\n", "from statsmodels.tsa.api import VAR\n", "from statsmodels.tsa.stattools import adfuller\n", "from statsmodels.tsa.stattools import grangercausalitytests\n", "from statsmodels.tsa.stattools import kpss" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "df_apple = pd.read_csv('../coal-price-data/investing/AAPL Historical Data.csv')\n", "df_walmart = pd.read_csv('../coal-price-data/investing/WMT Historical Data.csv')\n", "df_tesla = pd.read_csv('../coal-price-data/investing/TSLA Historical Data.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# df_apple.rename(columns = {'Price':'apple'}, inplace = True)\n", "# df_walmart.rename(columns = {'Price':'walmart'}, inplace = True)\n", "# df_tesla.rename(columns = {'Price':'tesla'}, inplace = True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DatePriceOpenHighLowVol.Change %
002/01/2024182.52183.97191.00179.2645.12M-1.02%
101/01/2024184.40187.15196.38180.171.19B-4.22%
212/01/2023192.53190.33199.62187.451.06B1.36%
311/01/2023189.95171.00192.93170.121.10B11.23%
410/01/2023170.77171.22182.34165.671.17B-0.26%
........................
51305/01/19810.150.130.150.12590.42M15.38%
51404/01/19810.130.110.130.11536.93M18.18%
51503/01/19810.110.120.120.10700.72M-8.33%
51602/01/19810.120.120.130.11321.62M-7.69%
51701/01/19810.130.150.160.13608.99M-13.33%
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" ], "text/plain": [ " Date Price Open High Low Vol. Change %\n", "0 02/01/2024 182.52 183.97 191.00 179.26 45.12M -1.02%\n", "1 01/01/2024 184.40 187.15 196.38 180.17 1.19B -4.22%\n", "2 12/01/2023 192.53 190.33 199.62 187.45 1.06B 1.36%\n", "3 11/01/2023 189.95 171.00 192.93 170.12 1.10B 11.23%\n", "4 10/01/2023 170.77 171.22 182.34 165.67 1.17B -0.26%\n", ".. ... ... ... ... ... ... ...\n", "513 05/01/1981 0.15 0.13 0.15 0.12 590.42M 15.38%\n", "514 04/01/1981 0.13 0.11 0.13 0.11 536.93M 18.18%\n", "515 03/01/1981 0.11 0.12 0.12 0.10 700.72M -8.33%\n", "516 02/01/1981 0.12 0.12 0.13 0.11 321.62M -7.69%\n", "517 01/01/1981 0.13 0.15 0.16 0.13 608.99M -13.33%\n", "\n", "[518 rows x 7 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_apple" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = pd.merge(df_apple[['Date', 'Price']], df_walmart[['Date', 'Price']], on='Date', how='right').rename(columns = {'Price_x':'apple', 'Price_y':'walmart'})\n", "df = df.merge(df_tesla[['Date', 'Price']], on='Date', how='right').rename(columns={'Price':'tesla'})" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Dateapplewalmarttesla
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Dateapplewalmarttesla
02024-02-01182.52175.56191.97
12024-01-01184.40165.25187.29
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companyapplewalmarttesla
Date
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2024-01-01184.40165.25187.29
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"matches": "x", "showticklabels": false }, "xaxis3": { "anchor": "y3", "domain": [ 0, 1 ], "matches": "x", "showticklabels": false }, "yaxis": { "anchor": "x", "domain": [ 0, 0.2866666666666666 ], "title": { "text": "value" } }, "yaxis2": { "anchor": "x2", "domain": [ 0.35666666666666663, 0.6433333333333333 ], "title": { "text": "value" } }, "yaxis3": { "anchor": "x3", "domain": [ 0.7133333333333333, 0.9999999999999999 ], "title": { "text": "value" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.area(df, facet_col='company', facet_col_wrap=1)\n", "fig.update_yaxes(matches=None)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "n_obs = 20\n", "df_train, df_test = df[0:-n_obs], df[-n_obs:]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def adf_test(df):\n", " result = adfuller(df.values)\n", " print('ADF Statistics: %f' % result[0])\n", " print('p-value: %f' % result[1])\n", " print('Critical values:')\n", " for key, value in result[4].items():\n", " print('\\t%s: %.3f' % (key, value))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ADF Test: Apple time series\n", "ADF Statistics: -2.793474\n", "p-value: 0.059212\n", "Critical values:\n", "\t1%: -3.480\n", "\t5%: -2.883\n", "\t10%: -2.578\n", "ADF Test: Walmart time series\n", "ADF Statistics: -1.407972\n", "p-value: 0.578448\n", "Critical values:\n", "\t1%: -3.478\n", "\t5%: -2.882\n", "\t10%: -2.578\n", "ADF Test: Tesla time series\n", "ADF Statistics: -1.195298\n", "p-value: 0.675615\n", "Critical values:\n", "\t1%: -3.482\n", "\t5%: -2.884\n", "\t10%: -2.579\n" ] } ], "source": [ "print('ADF Test: Apple time series')\n", "adf_test(df_train['apple'])\n", "print('ADF Test: Walmart time series')\n", "adf_test(df_train['walmart'])\n", "print('ADF Test: Tesla time series')\n", "adf_test(df_train['tesla'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def kpss_test(df): \n", " statistic, p_value, n_lags, critical_values = kpss(df.values)\n", " \n", " print(f'KPSS Statistic: {statistic}')\n", " print(f'p-value: {p_value}')\n", " print(f'num lags: {n_lags}')\n", " print('Critial Values:')\n", " for key, value in critical_values.items():\n", " print(f' {key} : {value}')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KPSS Test: Apple time series\n", "KPSS Statistic: 1.4799049926667052\n", "p-value: 0.01\n", "num lags: 8\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n", "KPSS Test: Walmart time series\n", "KPSS Statistic: 1.5504555338108945\n", "p-value: 0.01\n", "num lags: 8\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n", "KPSS Test: Tesla time series\n", "KPSS Statistic: 1.2070673514705184\n", "p-value: 0.01\n", "num lags: 8\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is smaller than the p-value returned.\n", "\n", "\n", "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is smaller than the p-value returned.\n", "\n", "\n", "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is smaller than the p-value returned.\n", "\n", "\n" ] } ], "source": [ "print('KPSS Test: Apple time series')\n", "kpss_test(df_train['apple'])\n", "print('KPSS Test: Walmart time series')\n", "kpss_test(df_train['walmart'])\n", "print('KPSS Test: Tesla time series')\n", "kpss_test(df_train['tesla'])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "company=apple
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"showticklabels": false }, "xaxis3": { "anchor": "y3", "domain": [ 0, 1 ], "matches": "x", "showticklabels": false }, "yaxis": { "anchor": "x", "domain": [ 0, 0.2866666666666666 ], "title": { "text": "value" } }, "yaxis2": { "anchor": "x2", "domain": [ 0.35666666666666663, 0.6433333333333333 ], "title": { "text": "value" } }, "yaxis3": { "anchor": "x3", "domain": [ 0.7133333333333333, 0.9999999999999999 ], "title": { "text": "value" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_train_transformed = df_train.diff().dropna()\n", "\n", "fig = px.line(df_train_transformed, facet_col=\"company\", facet_col_wrap=1)\n", "fig.update_yaxes(matches=None)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ADF Test: Apple time series transformed\n", "ADF Statistics: -3.799168\n", "p-value: 0.002916\n", "Critical values:\n", "\t1%: -3.480\n", "\t5%: -2.883\n", "\t10%: -2.578\n", "ADF Test: Walmart time series transformed\n", "ADF Statistics: -10.306569\n", "p-value: 0.000000\n", "Critical values:\n", "\t1%: -3.478\n", "\t5%: -2.882\n", "\t10%: -2.578\n", "ADF Test: Tesla time series transformed\n", "ADF Statistics: -2.927547\n", "p-value: 0.042237\n", "Critical values:\n", "\t1%: -3.482\n", "\t5%: -2.884\n", "\t10%: -2.579\n" ] } ], "source": [ "print('ADF Test: Apple time series transformed')\n", "adf_test(df_train_transformed['apple'])\n", "print('ADF Test: Walmart time series transformed')\n", "adf_test(df_train_transformed['walmart'])\n", "print('ADF Test: Tesla time series transformed')\n", "adf_test(df_train_transformed['tesla'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KPSS Test: Apple time series transformed\n", "KPSS Statistic: 0.30642603993334216\n", "p-value: 0.1\n", "num lags: 7\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n", "KPSS Test: Walmart time series transformed\n", "KPSS Statistic: 0.19811078226585627\n", "p-value: 0.1\n", "num lags: 8\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n", "KPSS Test: Tesla time series transformed\n", "KPSS Statistic: 0.09114707308088876\n", "p-value: 0.1\n", "num lags: 17\n", "Critial Values:\n", " 10% : 0.347\n", " 5% : 0.463\n", " 2.5% : 0.574\n", " 1% : 0.739\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is greater than the p-value returned.\n", "\n", "\n", "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is greater than the p-value returned.\n", "\n", "\n", "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n", "\n", "The test statistic is outside of the range of p-values available in the\n", "look-up table. The actual p-value is greater than the p-value returned.\n", "\n", "\n" ] } ], "source": [ "print('KPSS Test: Apple time series transformed')\n", "kpss_test(df_train_transformed['apple'])\n", "print('KPSS Test: Walmart time series transformed')\n", "kpss_test(df_train_transformed['walmart'])\n", "print('KPSS Test: Tesla time series transformed')\n", "kpss_test(df_train_transformed['tesla'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", "\n", "No frequency information was provided, so inferred frequency -1MS will be used.\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n", "\n", "A date index has been provided, but it is not monotonic and so will be ignored when e.g. forecasting.\n", "\n" ] } ], "source": [ "model = VAR(df_train_transformed)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lag Order = 1\n", "AIC : 13.121845837129593\n", "BIC : 13.371634039180403\n", "FPE : 499763.6483155377\n", "HQIC: 13.223349623202385 \n", "\n", "Lag Order = 2\n", "AIC : 13.018387880139725\n", "BIC : 13.457564885089665\n", "FPE : 450734.1429337017\n", "HQIC: 13.196854225926092 \n", "\n", "Lag Order = 3\n", "AIC : 12.924920345350833\n", "BIC : 13.555272293052827\n", "FPE : 410713.6140093425\n", "HQIC: 13.181076523988663 \n", "\n", "Lag Order = 4\n", "AIC : 12.765344110806351\n", "BIC : 13.58868571794374\n", "FPE : 350455.45003947406\n", "HQIC: 13.099928235318213 \n", "\n", "Lag Order = 5\n", "AIC : 12.679059830627413\n", "BIC : 13.697235025464702\n", "FPE : 321966.3544416425\n", "HQIC: 13.092821062630037 \n", "\n", "Lag Order = 6\n", "AIC : 12.592854593217835\n", "BIC : 13.80773716819742\n", "FPE : 296040.6198897234\n", "HQIC: 13.086553356094514 \n", "\n", "Lag Order = 7\n", "AIC : 12.578950684208595\n", "BIC : 13.992444966992267\n", "FPE : 292889.94491444435\n", "HQIC: 13.153358880821687 \n", "\n", "Lag Order = 8\n", "AIC : 12.557421041800211\n", "BIC : 14.171462585377117\n", "FPE : 287906.2769831479\n", "HQIC: 13.213322276819387 \n", "\n", "Lag Order = 9\n", "AIC : 12.299921356873089\n", "BIC : 14.116477649379629\n", "FPE : 223834.39221817514\n", "HQIC: 13.03811116379309 \n", "\n", "Lag Order = 10\n", "AIC : 12.169635543959231\n", "BIC : 14.19070673888121\n", "FPE : 197959.9758313992\n", "HQIC: 12.990921616878586 \n", "\n", "Lag Order = 11\n", "AIC : 12.13748096072811\n", "BIC : 14.365100628181215\n", "FPE : 193510.55627011927\n", "HQIC: 13.042683390957396 \n", "\n", "Lag Order = 12\n", "AIC : 11.761102756127734\n", "BIC : 14.197338655939397\n", "FPE : 134383.62461393472\n", "HQIC: 12.751054273692711 \n", "\n", "Lag Order = 13\n", "AIC : 11.747271045494621\n", "BIC : 14.394225922838235\n", "FPE : 134468.2646332004\n", "HQIC: 12.822817265595509 \n", "\n", "Lag Order = 14\n", "AIC : 11.708962819408555\n", "BIC : 14.568775223770228\n", "FPE : 131716.1307288914\n", "HQIC: 12.870962493892204 \n", "\n", "Lag Order = 15\n", "AIC : 11.523062909058199\n", "BIC : 14.597908037346535\n", "FPE : 111727.82568470361\n", "HQIC: 12.772388182954504 \n", "\n" ] } ], "source": [ "for i in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]:\n", " result = model.fit(i)\n", " print('Lag Order =', i)\n", " print('AIC : ', result.aic)\n", " print('BIC : ', result.bic)\n", " print('FPE : ', result.fpe)\n", " print('HQIC: ', result.hqic, '\\n')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " Summary of Regression Results \n", "==================================\n", "Model: VAR\n", "Method: OLS\n", "Date: Sun, 25, Feb, 2024\n", "Time: 21:40:09\n", "--------------------------------------------------------------------\n", "No. of Equations: 3.00000 BIC: 14.5979\n", "Nobs: 128.000 HQIC: 12.7724\n", "Log likelihood: -1144.35 FPE: 111728.\n", "AIC: 11.5231 Det(Omega_mle): 44477.8\n", "--------------------------------------------------------------------\n", "Results for equation apple\n", "==============================================================================\n", " coefficient std. error t-stat prob\n", "------------------------------------------------------------------------------\n", "const -0.699238 0.584786 -1.196 0.232\n", "L1.apple 0.014847 0.140038 0.106 0.916\n", "L1.walmart -0.113138 0.105005 -1.077 0.281\n", "L1.tesla 0.081087 0.038983 2.080 0.038\n", "L2.apple -0.167780 0.135627 -1.237 0.216\n", "L2.walmart -0.031901 0.104304 -0.306 0.760\n", "L2.tesla 0.109001 0.039080 2.789 0.005\n", "L3.apple 0.085712 0.136509 0.628 0.530\n", "L3.walmart 0.157405 0.102108 1.542 0.123\n", "L3.tesla -0.081028 0.038433 -2.108 0.035\n", "L4.apple -0.440097 0.112933 -3.897 0.000\n", "L4.walmart -0.028011 0.102538 -0.273 0.785\n", "L4.tesla 0.196295 0.038369 5.116 0.000\n", "L5.apple 0.187708 0.125176 1.500 0.134\n", "L5.walmart -0.059267 0.102669 -0.577 0.564\n", "L5.tesla -0.089774 0.042168 -2.129 0.033\n", "L6.apple -0.021143 0.124388 -0.170 0.865\n", "L6.walmart -0.236903 0.105059 -2.255 0.024\n", "L6.tesla 0.021333 0.040789 0.523 0.601\n", "L7.apple 0.034935 0.121061 0.289 0.773\n", "L7.walmart -0.196441 0.105540 -1.861 0.063\n", "L7.tesla 0.124142 0.039365 3.154 0.002\n", "L8.apple 0.263352 0.114911 2.292 0.022\n", "L8.walmart 0.124926 0.105791 1.181 0.238\n", "L8.tesla -0.064692 0.037117 -1.743 0.081\n", "L9.apple -0.161093 0.118119 -1.364 0.173\n", "L9.walmart 0.142378 0.105329 1.352 0.176\n", "L9.tesla -0.003729 0.036768 -0.101 0.919\n", "L10.apple 0.052834 0.118872 0.444 0.657\n", "L10.walmart -0.012512 0.106188 -0.118 0.906\n", "L10.tesla 0.014551 0.036445 0.399 0.690\n", "L11.apple -0.196153 0.120203 -1.632 0.103\n", "L11.walmart -0.189079 0.103137 -1.833 0.067\n", "L11.tesla 0.046769 0.033933 1.378 0.168\n", "L12.apple 0.281041 0.113450 2.477 0.013\n", "L12.walmart 0.248444 0.097517 2.548 0.011\n", "L12.tesla -0.131892 0.034494 -3.824 0.000\n", "L13.apple -0.033669 0.107860 -0.312 0.755\n", "L13.walmart 0.034415 0.101916 0.338 0.736\n", "L13.tesla -0.018055 0.036536 -0.494 0.621\n", "L14.apple -0.255695 0.103146 -2.479 0.013\n", "L14.walmart 0.139620 0.095459 1.463 0.144\n", "L14.tesla 0.100374 0.036446 2.754 0.006\n", "L15.apple 0.196757 0.104793 1.878 0.060\n", "L15.walmart 0.017978 0.097782 0.184 0.854\n", "L15.tesla -0.030502 0.036230 -0.842 0.400\n", "==============================================================================\n", "\n", "Results for equation walmart\n", "==============================================================================\n", " coefficient std. error t-stat prob\n", "------------------------------------------------------------------------------\n", "const -0.103331 0.633837 -0.163 0.870\n", "L1.apple 0.144375 0.151784 0.951 0.342\n", "L1.walmart -0.076127 0.113813 -0.669 0.504\n", "L1.tesla -0.048764 0.042252 -1.154 0.248\n", "L2.apple -0.125694 0.147003 -0.855 0.393\n", "L2.walmart -0.017047 0.113053 -0.151 0.880\n", "L2.tesla -0.008949 0.042358 -0.211 0.833\n", "L3.apple 0.162286 0.147959 1.097 0.273\n", "L3.walmart -0.060079 0.110672 -0.543 0.587\n", "L3.tesla 0.031460 0.041656 0.755 0.450\n", "L4.apple 0.080359 0.122405 0.657 0.512\n", "L4.walmart -0.068210 0.111139 -0.614 0.539\n", "L4.tesla 0.023108 0.041587 0.556 0.578\n", "L5.apple 0.025574 0.135675 0.188 0.850\n", "L5.walmart -0.232336 0.111281 -2.088 0.037\n", "L5.tesla 0.020971 0.045705 0.459 0.646\n", "L6.apple -0.201452 0.134822 -1.494 0.135\n", "L6.walmart -0.134846 0.113871 -1.184 0.236\n", "L6.tesla 0.068019 0.044210 1.539 0.124\n", "L7.apple 0.186968 0.131215 1.425 0.154\n", "L7.walmart -0.110714 0.114393 -0.968 0.333\n", "L7.tesla -0.016329 0.042667 -0.383 0.702\n", "L8.apple 0.175006 0.124550 1.405 0.160\n", "L8.walmart -0.016260 0.114665 -0.142 0.887\n", "L8.tesla -0.072481 0.040230 -1.802 0.072\n", "L9.apple 0.198956 0.128027 1.554 0.120\n", "L9.walmart -0.084669 0.114164 -0.742 0.458\n", "L9.tesla -0.004090 0.039852 -0.103 0.918\n", "L10.apple -0.091041 0.128843 -0.707 0.480\n", "L10.walmart 0.052878 0.115095 0.459 0.646\n", "L10.tesla -0.003499 0.039502 -0.089 0.929\n", "L11.apple -0.212327 0.130285 -1.630 0.103\n", "L11.walmart -0.065408 0.111788 -0.585 0.558\n", "L11.tesla 0.070514 0.036780 1.917 0.055\n", "L12.apple 0.341238 0.122966 2.775 0.006\n", "L12.walmart -0.128880 0.105697 -1.219 0.223\n", "L12.tesla -0.035130 0.037387 -0.940 0.347\n", "L13.apple -0.009063 0.116907 -0.078 0.938\n", "L13.walmart 0.138496 0.110464 1.254 0.210\n", "L13.tesla -0.016126 0.039601 -0.407 0.684\n", "L14.apple 0.050583 0.111798 0.452 0.651\n", "L14.walmart 0.123218 0.103466 1.191 0.234\n", "L14.tesla 0.005768 0.039503 0.146 0.884\n", "L15.apple 0.153644 0.113583 1.353 0.176\n", "L15.walmart -0.039947 0.105984 -0.377 0.706\n", "L15.tesla -0.054155 0.039269 -1.379 0.168\n", "==============================================================================\n", "\n", "Results for equation tesla\n", "==============================================================================\n", " coefficient std. error t-stat prob\n", "------------------------------------------------------------------------------\n", "const -2.063049 1.878392 -1.098 0.272\n", "L1.apple -0.171895 0.449815 -0.382 0.702\n", "L1.walmart -0.105253 0.337288 -0.312 0.755\n", "L1.tesla 0.190132 0.125216 1.518 0.129\n", "L2.apple -0.046830 0.435648 -0.107 0.914\n", "L2.walmart -0.125800 0.335034 -0.375 0.707\n", "L2.tesla 0.315252 0.125529 2.511 0.012\n", "L3.apple 0.806135 0.438482 1.838 0.066\n", "L3.walmart -0.279177 0.327980 -0.851 0.395\n", "L3.tesla -0.426441 0.123450 -3.454 0.001\n", "L4.apple -0.685840 0.362751 -1.891 0.059\n", "L4.walmart 0.055336 0.329363 0.168 0.867\n", "L4.tesla 0.397299 0.123244 3.224 0.001\n", "L5.apple 0.071231 0.402077 0.177 0.859\n", "L5.walmart -0.108462 0.329783 -0.329 0.742\n", "L5.tesla -0.050922 0.135447 -0.376 0.707\n", "L6.apple -0.389905 0.399548 -0.976 0.329\n", "L6.walmart -0.362135 0.337460 -1.073 0.283\n", "L6.tesla -0.029577 0.131017 -0.226 0.821\n", "L7.apple 0.402571 0.388859 1.035 0.301\n", "L7.walmart -0.773885 0.339006 -2.283 0.022\n", "L7.tesla 0.070900 0.126445 0.561 0.575\n", "L8.apple 0.057018 0.369107 0.154 0.877\n", "L8.walmart -0.253383 0.339812 -0.746 0.456\n", "L8.tesla -0.060940 0.119222 -0.511 0.609\n", "L9.apple -0.814436 0.379410 -2.147 0.032\n", "L9.walmart 0.692883 0.338329 2.048 0.041\n", "L9.tesla 0.170148 0.118103 1.441 0.150\n", "L10.apple 0.165284 0.381830 0.433 0.665\n", "L10.walmart -0.070246 0.341086 -0.206 0.837\n", "L10.tesla 0.111133 0.117065 0.949 0.342\n", "L11.apple -0.257494 0.386104 -0.667 0.505\n", "L11.walmart -0.834862 0.331288 -2.520 0.012\n", "L11.tesla 0.318169 0.108998 2.919 0.004\n", "L12.apple 1.048785 0.364413 2.878 0.004\n", "L12.walmart 0.390809 0.313235 1.248 0.212\n", "L12.tesla -0.268612 0.110798 -2.424 0.015\n", "L13.apple -0.201846 0.346456 -0.583 0.560\n", "L13.walmart 0.385814 0.327363 1.179 0.239\n", "L13.tesla -0.250162 0.117359 -2.132 0.033\n", "L14.apple -0.934654 0.331317 -2.821 0.005\n", "L14.walmart -0.212581 0.306624 -0.693 0.488\n", "L14.tesla 0.296885 0.117067 2.536 0.011\n", "L15.apple 1.015922 0.336607 3.018 0.003\n", "L15.walmart -0.438696 0.314087 -1.397 0.162\n", "L15.tesla -0.205261 0.116373 -1.764 0.078\n", "==============================================================================\n", "\n", "Correlation matrix of residuals\n", " apple walmart tesla\n", "apple 1.000000 0.281952 0.608528\n", "walmart 0.281952 1.000000 0.187816\n", "tesla 0.608528 0.187816 1.000000\n", "\n" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = model.fit(maxlags=15, ic='aic')\n", "results.summary()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "out = durbin_watson(results.resid)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "apple : 2.12\n", "walmart : 1.9\n", "tesla : 2.13\n" ] } ], "source": [ "for col, val in zip(df.columns, out):\n", " print(col, ':', round(val, 2))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "maxlag=15\n", "test = 'ssr_chi2test'" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False): \n", " \n", " df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n", " for c in df.columns:\n", " for r in df.index:\n", " test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n", " p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n", " if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n", " min_p_value = np.min(p_values)\n", " df.loc[r, c] = min_p_value\n", " df.columns = [var + '_x' for var in variables]\n", " df.index = [var + '_y' for var in variables]\n", " return df" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions should not print results\n", "\n", "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/stattools.py:1545: FutureWarning:\n", "\n", "verbose is deprecated since functions 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apple_xwalmart_xtesla_x
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" ], "text/plain": [ " apple_x walmart_x tesla_x\n", "apple_y 1.0 0.0003 0.0\n", "walmart_y 0.0 1.0000 0.0\n", "tesla_y 0.0 0.0000 1.0" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grangers_causation_matrix(df_train_transformed, variables = df_train_transformed.columns)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "lag_order = results.k_ar\n", "\n", "df_input = df_train_transformed.values[-lag_order:]\n", "df_forecast = results.forecast(y=df_input, steps=n_obs)\n", "df_forecast = (pd.DataFrame(df_forecast, index=df_test.index, columns=df_test.columns + '_pred'))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def invert_transformation(df, pred):\n", " forecast = df_forecast.copy()\n", " columns = df.columns\n", " for col in columns:\n", " forecast[str(col)+'_pred'] = df[col].iloc[-1] + forecast[str(col)+'_pred'].cumsum()\n", " return forecast" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "output = invert_transformation(df_train, df_forecast)\n", "\n", "combined = pd.concat([output['apple_pred'], df_test['apple'], output['walmart_pred'], df_test['walmart'], output['tesla_pred'], df_test['tesla']], axis=1)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forecast accuracy of Apple\n", "RMSE: 4.37\n", "MAE: 3.67\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/sklearn/metrics/_regression.py:483: FutureWarning:\n", "\n", "'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", "\n" ] } ], "source": [ "rmse = mean_squared_error(combined['apple_pred'], combined['apple'], squared=False)\n", "mae = mean_absolute_error(combined['apple_pred'], combined['apple'])\n", "\n", "print('Forecast accuracy of Apple')\n", "print('RMSE: ', round(rmse,2))\n", "print('MAE: ', round(mae,2))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forecast accuracy of Walmart\n", "RMSE: 10.5\n", "MAE: 9.71\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/sklearn/metrics/_regression.py:483: FutureWarning:\n", "\n", "'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", "\n" ] } ], "source": [ "rmse = mean_squared_error(combined['walmart_pred'], combined['walmart'], squared=False)\n", "mae = mean_absolute_error(combined['walmart_pred'], combined['walmart'])\n", "\n", "print('Forecast accuracy of Walmart')\n", "print('RMSE: ', round(rmse,2))\n", "print('MAE: ', round(mae,2))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forecast accuracy of Tesla\n", "RMSE: 11.66\n", "MAE: 9.66\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/sklearn/metrics/_regression.py:483: FutureWarning:\n", "\n", "'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", "\n" ] } ], "source": [ "rmse = mean_squared_error(combined['tesla_pred'], combined['tesla'], squared=False)\n", "mae = mean_absolute_error(combined['tesla_pred'], combined['tesla'])\n", "\n", "print('Forecast accuracy of Tesla')\n", "print('RMSE: ', round(rmse,2))\n", "print('MAE: ', round(mae,2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py311-kfp240-airflow251", "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 }