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  1. granger_causality_testing.ipynb +2214 -2
granger_causality_testing.ipynb CHANGED
@@ -2,13 +2,15 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 1,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
9
  "import pandas as pd\n",
10
  "import plotly.express as px\n",
11
- "from statsmodels.tsa.stattools import adfuller"
 
 
12
  ]
13
  },
14
  {
@@ -4722,6 +4724,2216 @@
4722
  "adf_test(df_train['tesla'])"
4723
  ]
4724
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4725
  {
4726
  "cell_type": "code",
4727
  "execution_count": null,
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 23,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
9
  "import pandas as pd\n",
10
  "import plotly.express as px\n",
11
+ "from statsmodels.tsa.api import VAR\n",
12
+ "from statsmodels.tsa.stattools import adfuller\n",
13
+ "from statsmodels.tsa.stattools import kpss"
14
  ]
15
  },
16
  {
 
4724
  "adf_test(df_train['tesla'])"
4725
  ]
4726
  },
4727
+ {
4728
+ "cell_type": "code",
4729
+ "execution_count": 17,
4730
+ "metadata": {},
4731
+ "outputs": [],
4732
+ "source": [
4733
+ "def kpss_test(df): \n",
4734
+ " statistic, p_value, n_lags, critical_values = kpss(df.values)\n",
4735
+ " \n",
4736
+ " print(f'KPSS Statistic: {statistic}')\n",
4737
+ " print(f'p-value: {p_value}')\n",
4738
+ " print(f'num lags: {n_lags}')\n",
4739
+ " print('Critial Values:')\n",
4740
+ " for key, value in critical_values.items():\n",
4741
+ " print(f' {key} : {value}')"
4742
+ ]
4743
+ },
4744
+ {
4745
+ "cell_type": "code",
4746
+ "execution_count": 18,
4747
+ "metadata": {},
4748
+ "outputs": [
4749
+ {
4750
+ "name": "stdout",
4751
+ "output_type": "stream",
4752
+ "text": [
4753
+ "KPSS Test: Apple time series\n",
4754
+ "KPSS Statistic: 1.4799049926667052\n",
4755
+ "p-value: 0.01\n",
4756
+ "num lags: 8\n",
4757
+ "Critial Values:\n",
4758
+ " 10% : 0.347\n",
4759
+ " 5% : 0.463\n",
4760
+ " 2.5% : 0.574\n",
4761
+ " 1% : 0.739\n",
4762
+ "KPSS Test: Walmart time series\n",
4763
+ "KPSS Statistic: 1.5504555338108945\n",
4764
+ "p-value: 0.01\n",
4765
+ "num lags: 8\n",
4766
+ "Critial Values:\n",
4767
+ " 10% : 0.347\n",
4768
+ " 5% : 0.463\n",
4769
+ " 2.5% : 0.574\n",
4770
+ " 1% : 0.739\n",
4771
+ "KPSS Test: Tesla time series\n",
4772
+ "KPSS Statistic: 1.2070673514705184\n",
4773
+ "p-value: 0.01\n",
4774
+ "num lags: 8\n",
4775
+ "Critial Values:\n",
4776
+ " 10% : 0.347\n",
4777
+ " 5% : 0.463\n",
4778
+ " 2.5% : 0.574\n",
4779
+ " 1% : 0.739\n"
4780
+ ]
4781
+ },
4782
+ {
4783
+ "name": "stderr",
4784
+ "output_type": "stream",
4785
+ "text": [
4786
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
4787
+ "\n",
4788
+ "The test statistic is outside of the range of p-values available in the\n",
4789
+ "look-up table. The actual p-value is smaller than the p-value returned.\n",
4790
+ "\n",
4791
+ "\n",
4792
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
4793
+ "\n",
4794
+ "The test statistic is outside of the range of p-values available in the\n",
4795
+ "look-up table. The actual p-value is smaller than the p-value returned.\n",
4796
+ "\n",
4797
+ "\n",
4798
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
4799
+ "\n",
4800
+ "The test statistic is outside of the range of p-values available in the\n",
4801
+ "look-up table. The actual p-value is smaller than the p-value returned.\n",
4802
+ "\n",
4803
+ "\n"
4804
+ ]
4805
+ }
4806
+ ],
4807
+ "source": [
4808
+ "print('KPSS Test: Apple time series')\n",
4809
+ "kpss_test(df_train['apple'])\n",
4810
+ "print('KPSS Test: Walmart time series')\n",
4811
+ "kpss_test(df_train['walmart'])\n",
4812
+ "print('KPSS Test: Tesla time series')\n",
4813
+ "kpss_test(df_train['tesla'])"
4814
+ ]
4815
+ },
4816
+ {
4817
+ "cell_type": "code",
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+ "application/vnd.plotly.v1+json": {
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+ "config": {
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+ "plotlyServerURL": "https://plot.ly"
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+ },
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+ "hovertemplate": "company=apple<br>Date=%{x}<br>value=%{y}<extra></extra>",
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+ "legendgroup": "apple",
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+ "line": {
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+ "color": "#636efa",
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+ "dash": "solid"
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+ "marker": {
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+ "symbol": "circle"
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+ "df_train_transformed = df_train.diff().dropna()\n",
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+ "\n",
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+ "fig = px.line(df_train_transformed, facet_col=\"company\", facet_col_wrap=1)\n",
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+ "ADF Test: Apple time series transformed\n",
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+ "ADF Statistics: -3.799168\n",
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+ "p-value: 0.002916\n",
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+ "Critical values:\n",
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+ "\t1%: -3.480\n",
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+ "ADF Test: Walmart time series transformed\n",
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+ "ADF Statistics: -10.306569\n",
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+ "p-value: 0.000000\n",
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+ "Critical values:\n",
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+ "\t1%: -3.478\n",
6708
+ "\t5%: -2.882\n",
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+ "\t10%: -2.578\n",
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+ "ADF Test: Tesla time series transformed\n",
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+ "ADF Statistics: -2.927547\n",
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+ "p-value: 0.042237\n",
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+ "Critical values:\n",
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+ "\t1%: -3.482\n",
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+ "\t5%: -2.884\n",
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+ "\t10%: -2.579\n"
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+ ]
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+ }
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+ "source": [
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+ "print('ADF Test: Apple time series transformed')\n",
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+ "adf_test(df_train_transformed['apple'])\n",
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+ "print('ADF Test: Walmart time series transformed')\n",
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+ "adf_test(df_train_transformed['walmart'])\n",
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+ "print('ADF Test: Tesla time series transformed')\n",
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+ "adf_test(df_train_transformed['tesla'])"
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+ },
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+ "execution_count": 21,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "KPSS Test: Apple time series transformed\n",
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+ "KPSS Statistic: 0.30642603993334216\n",
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+ "p-value: 0.1\n",
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+ "num lags: 7\n",
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+ "Critial Values:\n",
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+ " 10% : 0.347\n",
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+ " 5% : 0.463\n",
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+ " 2.5% : 0.574\n",
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+ " 1% : 0.739\n",
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+ "KPSS Test: Walmart time series transformed\n",
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+ "KPSS Statistic: 0.19811078226585627\n",
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+ "p-value: 0.1\n",
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+ "num lags: 8\n",
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+ "Critial Values:\n",
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+ " 10% : 0.347\n",
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+ " 5% : 0.463\n",
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+ " 2.5% : 0.574\n",
6755
+ " 1% : 0.739\n",
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+ "KPSS Test: Tesla time series transformed\n",
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+ "KPSS Statistic: 0.09114707308088876\n",
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+ "p-value: 0.1\n",
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+ "num lags: 17\n",
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+ "Critial Values:\n",
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+ " 10% : 0.347\n",
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+ " 5% : 0.463\n",
6763
+ " 2.5% : 0.574\n",
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+ " 1% : 0.739\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
6771
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
6772
+ "\n",
6773
+ "The test statistic is outside of the range of p-values available in the\n",
6774
+ "look-up table. The actual p-value is greater than the p-value returned.\n",
6775
+ "\n",
6776
+ "\n",
6777
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
6778
+ "\n",
6779
+ "The test statistic is outside of the range of p-values available in the\n",
6780
+ "look-up table. The actual p-value is greater than the p-value returned.\n",
6781
+ "\n",
6782
+ "\n",
6783
+ "/tmp/ipykernel_347985/249017143.py:2: InterpolationWarning:\n",
6784
+ "\n",
6785
+ "The test statistic is outside of the range of p-values available in the\n",
6786
+ "look-up table. The actual p-value is greater than the p-value returned.\n",
6787
+ "\n",
6788
+ "\n"
6789
+ ]
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+ }
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+ ],
6792
+ "source": [
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+ "print('KPSS Test: Apple time series transformed')\n",
6794
+ "kpss_test(df_train_transformed['apple'])\n",
6795
+ "print('KPSS Test: Walmart time series transformed')\n",
6796
+ "kpss_test(df_train_transformed['walmart'])\n",
6797
+ "print('KPSS Test: Tesla time series transformed')\n",
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+ "kpss_test(df_train_transformed['tesla'])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 25,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
6810
+ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n",
6811
+ "\n",
6812
+ "No frequency information was provided, so inferred frequency -1MS will be used.\n",
6813
+ "\n",
6814
+ "/home/ibnu/miniconda3/envs/py312/lib/python3.12/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning:\n",
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+ "\n",
6816
+ "A date index has been provided, but it is not monotonic and so will be ignored when e.g. forecasting.\n",
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+ "\n"
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+ ]
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+ }
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+ ],
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+ "source": [
6822
+ "model = VAR(df_train_transformed)"
6823
+ ]
6824
+ },
6825
+ {
6826
+ "cell_type": "code",
6827
+ "execution_count": 27,
6828
+ "metadata": {},
6829
+ "outputs": [
6830
+ {
6831
+ "name": "stdout",
6832
+ "output_type": "stream",
6833
+ "text": [
6834
+ "Lag Order = 1\n",
6835
+ "AIC : 13.121845837129593\n",
6836
+ "BIC : 13.371634039180403\n",
6837
+ "FPE : 499763.6483155377\n",
6838
+ "HQIC: 13.223349623202385 \n",
6839
+ "\n",
6840
+ "Lag Order = 2\n",
6841
+ "AIC : 13.018387880139725\n",
6842
+ "BIC : 13.457564885089665\n",
6843
+ "FPE : 450734.1429337017\n",
6844
+ "HQIC: 13.196854225926092 \n",
6845
+ "\n",
6846
+ "Lag Order = 3\n",
6847
+ "AIC : 12.924920345350833\n",
6848
+ "BIC : 13.555272293052827\n",
6849
+ "FPE : 410713.6140093425\n",
6850
+ "HQIC: 13.181076523988663 \n",
6851
+ "\n",
6852
+ "Lag Order = 4\n",
6853
+ "AIC : 12.765344110806351\n",
6854
+ "BIC : 13.58868571794374\n",
6855
+ "FPE : 350455.45003947406\n",
6856
+ "HQIC: 13.099928235318213 \n",
6857
+ "\n",
6858
+ "Lag Order = 5\n",
6859
+ "AIC : 12.679059830627413\n",
6860
+ "BIC : 13.697235025464702\n",
6861
+ "FPE : 321966.3544416425\n",
6862
+ "HQIC: 13.092821062630037 \n",
6863
+ "\n",
6864
+ "Lag Order = 6\n",
6865
+ "AIC : 12.592854593217835\n",
6866
+ "BIC : 13.80773716819742\n",
6867
+ "FPE : 296040.6198897234\n",
6868
+ "HQIC: 13.086553356094514 \n",
6869
+ "\n",
6870
+ "Lag Order = 7\n",
6871
+ "AIC : 12.578950684208595\n",
6872
+ "BIC : 13.992444966992267\n",
6873
+ "FPE : 292889.94491444435\n",
6874
+ "HQIC: 13.153358880821687 \n",
6875
+ "\n",
6876
+ "Lag Order = 8\n",
6877
+ "AIC : 12.557421041800211\n",
6878
+ "BIC : 14.171462585377117\n",
6879
+ "FPE : 287906.2769831479\n",
6880
+ "HQIC: 13.213322276819387 \n",
6881
+ "\n",
6882
+ "Lag Order = 9\n",
6883
+ "AIC : 12.299921356873089\n",
6884
+ "BIC : 14.116477649379629\n",
6885
+ "FPE : 223834.39221817514\n",
6886
+ "HQIC: 13.03811116379309 \n",
6887
+ "\n",
6888
+ "Lag Order = 10\n",
6889
+ "AIC : 12.169635543959231\n",
6890
+ "BIC : 14.19070673888121\n",
6891
+ "FPE : 197959.9758313992\n",
6892
+ "HQIC: 12.990921616878586 \n",
6893
+ "\n",
6894
+ "Lag Order = 11\n",
6895
+ "AIC : 12.13748096072811\n",
6896
+ "BIC : 14.365100628181215\n",
6897
+ "FPE : 193510.55627011927\n",
6898
+ "HQIC: 13.042683390957396 \n",
6899
+ "\n",
6900
+ "Lag Order = 12\n",
6901
+ "AIC : 11.761102756127734\n",
6902
+ "BIC : 14.197338655939397\n",
6903
+ "FPE : 134383.62461393472\n",
6904
+ "HQIC: 12.751054273692711 \n",
6905
+ "\n",
6906
+ "Lag Order = 13\n",
6907
+ "AIC : 11.747271045494621\n",
6908
+ "BIC : 14.394225922838235\n",
6909
+ "FPE : 134468.2646332004\n",
6910
+ "HQIC: 12.822817265595509 \n",
6911
+ "\n",
6912
+ "Lag Order = 14\n",
6913
+ "AIC : 11.708962819408555\n",
6914
+ "BIC : 14.568775223770228\n",
6915
+ "FPE : 131716.1307288914\n",
6916
+ "HQIC: 12.870962493892204 \n",
6917
+ "\n",
6918
+ "Lag Order = 15\n",
6919
+ "AIC : 11.523062909058199\n",
6920
+ "BIC : 14.597908037346535\n",
6921
+ "FPE : 111727.82568470361\n",
6922
+ "HQIC: 12.772388182954504 \n",
6923
+ "\n"
6924
+ ]
6925
+ }
6926
+ ],
6927
+ "source": [
6928
+ "for i in [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]:\n",
6929
+ " result = model.fit(i)\n",
6930
+ " print('Lag Order =', i)\n",
6931
+ " print('AIC : ', result.aic)\n",
6932
+ " print('BIC : ', result.bic)\n",
6933
+ " print('FPE : ', result.fpe)\n",
6934
+ " print('HQIC: ', result.hqic, '\\n')"
6935
+ ]
6936
+ },
6937
  {
6938
  "cell_type": "code",
6939
  "execution_count": null,