{ "cells": [ { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import plotly.express as px\n", "from statsmodels.tsa.api import VAR\n", "from statsmodels.tsa.stattools import adfuller\n", "from statsmodels.tsa.stattools import kpss" ] }, { "cell_type": "code", "execution_count": 2, "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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Dateapplewalmarttesla
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101/01/2024184.40165.25187.29
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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
2023-12-01192.53157.65248.48
2023-11-01189.95155.69240.08
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2010-07-019.1951.191.33
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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": 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": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 2 }