Vector Autoregression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Time-Series Analysis with VAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "from statsmodels.tsa.api import VAR # vector autoregression\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "from statsmodels.tsa.base.datetools import dates_from_str\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20, 4)})\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mayo</th>\n",
       "      <th>sandalet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-22</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-29</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-06</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-20</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mayo  sandalet\n",
       "Week                      \n",
       "2015-11-22     6         2\n",
       "2015-11-29     5         1\n",
       "2015-12-06     8         1\n",
       "2015-12-13     5         2\n",
       "2015-12-20     5         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"beachwear.csv\")\n",
    "data = data.set_index(\"Week\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be using weekly search volume data from Google Trends for two Turkish search terms: \"mayo\" (meaning swimsuit) & \"sandalet\" (meaning sandals). Google scales search volume data between 0-100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Granger Causality Test\n",
    "\n",
    "We will be using Granger Causality Test in order to see if these variables are useful in forecasting one another as well as themselves. The null hypothesis is that lagged x-values do not explain the variation in y. [Click here](https://www.statisticshowto.com/granger-causality/) to read more about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y = mayo, X = mayo, P Values = [1.0, 1.0]\n",
      "Y = sandalet, X = mayo, P Values = [0.0, 0.00211]\n",
      "Y = mayo, X = sandalet, P Values = [0.0, 0.0]\n",
      "Y = sandalet, X = sandalet, P Values = [1.0, 1.0]\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mayo_x</th>\n",
       "      <th>sandalet_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mayo_y</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sandalet_y</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mayo_x  sandalet_x\n",
       "mayo_y         1.0         0.0\n",
       "sandalet_y     0.0         1.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def grangers_causality_matrix(data, variables, test='ssr_chi2test', maxlag=2, verbose=True):\n",
    "\n",
    "    dataset = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
    "\n",
    "    for c in dataset.columns:\n",
    "        for r in dataset.index:\n",
    "            test_result = grangercausalitytests(data[[r,c]], maxlag=maxlag, verbose=False)\n",
    "            p_values = [round(test_result[i+1][0][test][1], 5) for i in range(maxlag)]\n",
    "            if verbose:\n",
    "                print(f'Y = {r}, X = {c}, P Values = {p_values}')\n",
    "\n",
    "            min_p_value = np.min(p_values)\n",
    "            dataset.loc[r,c] = min_p_value\n",
    "\n",
    "    dataset.columns = [var + '_x' for var in variables]\n",
    "\n",
    "    dataset.index = [var + '_y' for var in variables]\n",
    "\n",
    "    return dataset\n",
    "\n",
    "grangers_causality_matrix(data, variables=data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see, our p-values are smaller than 0.05 and therefore, the null hypothesis is rejected. That means these series can be used to predict each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented Dickey-Fuller Test\n",
    "\n",
    "We will apply unit root testing to see whether the data is stationary or not. The null hypothesis is that the time series has a unit root and is therefore, non-stationary. If our data contains non-stationary series, we will be de-trending the data using log returns to make it more stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mayo - ADF Statistic: -4.278713\n",
      "Mayo - p-value: 0.000483\n",
      "\n",
      "Sandalet - ADF Statistic: -4.343050\n",
      "Sandalet - p-value: 0.000374\n"
     ]
    }
   ],
   "source": [
    "mayo = data[\"mayo\"].values\n",
    "sandalet = data[\"sandalet\"].values\n",
    "\n",
    "mayo_result = adfuller(mayo)\n",
    "print('Mayo - ADF Statistic: %f' % mayo_result[0])\n",
    "print('Mayo - p-value: %f' %  mayo_result[1])\n",
    "\n",
    "sandalet_result = adfuller(sandalet)\n",
    "print('\\nSandalet - ADF Statistic: %f' % sandalet_result[0])\n",
    "print('Sandalet - p-value: %f' %  sandalet_result[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since our p-values are smaller than 0.05, our null hypothesis is rejected and that means both these series are stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training The Model\n",
    "\n",
    "We will now be training the model. For the lag order selection process, we will be using the model's built-in method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>VAR Order Selection (* highlights the minimums)</caption>\n",
       "<tr>\n",
       "   <td></td>      <th>AIC</th>         <th>BIC</th>         <th>FPE</th>        <th>HQIC</th>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>0</th>  <td>     10.43</td>  <td>     10.47</td>  <td> 3.397e+04</td>  <td>     10.45</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1</th>  <td>     6.284</td>  <td>     6.384</td>  <td>     536.1</td>  <td>     6.325</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>  <td>     6.228</td>  <td>     6.394</td>  <td>     506.7</td>  <td>     6.295</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>  <td>     6.086</td>  <td>     6.319*</td> <td>     439.7</td>  <td>     6.180*</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>4</th>  <td>     6.090</td>  <td>     6.389</td>  <td>     441.6</td>  <td>     6.211</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>5</th>  <td>     6.057</td>  <td>     6.423</td>  <td>     427.3</td>  <td>     6.205</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>6</th>  <td>     6.053</td>  <td>     6.484</td>  <td>     425.3</td>  <td>     6.227</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>7</th>  <td>     6.062</td>  <td>     6.560</td>  <td>     429.3</td>  <td>     6.263</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>8</th>  <td>     6.031*</td> <td>     6.596</td>  <td>     416.5*</td> <td>     6.260</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>9</th>  <td>     6.060</td>  <td>     6.691</td>  <td>     428.7</td>  <td>     6.315</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>10</th> <td>     6.062</td>  <td>     6.760</td>  <td>     429.9</td>  <td>     6.344</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>11</th> <td>     6.099</td>  <td>     6.863</td>  <td>     446.3</td>  <td>     6.408</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>12</th> <td>     6.112</td>  <td>     6.942</td>  <td>     452.3</td>  <td>     6.448</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>13</th> <td>     6.129</td>  <td>     7.026</td>  <td>     460.5</td>  <td>     6.492</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>14</th> <td>     6.165</td>  <td>     7.128</td>  <td>     477.7</td>  <td>     6.555</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>15</th> <td>     6.189</td>  <td>     7.218</td>  <td>     489.8</td>  <td>     6.605</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = VAR(data)\n",
    "lag_orders = model.select_order(15)\n",
    "lag_orders.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's think business for a moment. Say we are a retail company selling beachwear. Both false positives (BIC lead) & false negatives (AIC lead) may mislead us significantly & cripple our marketing efforts. In that case, we would fit two different models that have lag orders 3 and 8. However, since this is just an example case, we will be applying 8 (AIC lead) for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 25, Feb, 2021\n",
       "Time:                     13:22:46\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         2.00000    BIC:                    6.50648\n",
       "Nobs:                     205.000    HQIC:                   6.17827\n",
       "Log likelihood:          -1158.19    FPE:                    386.107\n",
       "AIC:                      5.95535    Det(Omega_mle):         329.237\n",
       "--------------------------------------------------------------------\n",
       "Results for equation mayo\n",
       "==============================================================================\n",
       "                 coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------\n",
       "const               2.524524         0.665970            3.791           0.000\n",
       "L1.mayo             0.724808         0.083110            8.721           0.000\n",
       "L1.sandalet         0.345585         0.109248            3.163           0.002\n",
       "L2.mayo            -0.060749         0.104313           -0.582           0.560\n",
       "L2.sandalet         0.120981         0.166737            0.726           0.468\n",
       "L3.mayo            -0.052632         0.103250           -0.510           0.610\n",
       "L3.sandalet         0.099251         0.172787            0.574           0.566\n",
       "L4.mayo             0.054160         0.103321            0.524           0.600\n",
       "L4.sandalet        -0.106082         0.175662           -0.604           0.546\n",
       "L5.mayo             0.004814         0.102450            0.047           0.963\n",
       "L5.sandalet         0.012663         0.173978            0.073           0.942\n",
       "L6.mayo            -0.329731         0.101388           -3.252           0.001\n",
       "L6.sandalet         0.399051         0.171204            2.331           0.020\n",
       "L7.mayo             0.286144         0.099668            2.871           0.004\n",
       "L7.sandalet        -0.523418         0.162994           -3.211           0.001\n",
       "L8.mayo            -0.205071         0.072502           -2.829           0.005\n",
       "L8.sandalet         0.373754         0.122653            3.047           0.002\n",
       "==============================================================================\n",
       "\n",
       "Results for equation sandalet\n",
       "==============================================================================\n",
       "                 coefficient       std. error           t-stat            prob\n",
       "------------------------------------------------------------------------------\n",
       "const               2.223314         0.519762            4.278           0.000\n",
       "L1.mayo            -0.103494         0.064864           -1.596           0.111\n",
       "L1.sandalet         1.266745         0.085264           14.857           0.000\n",
       "L2.mayo            -0.140623         0.081412           -1.727           0.084\n",
       "L2.sandalet        -0.366596         0.130132           -2.817           0.005\n",
       "L3.mayo            -0.013985         0.080583           -0.174           0.862\n",
       "L3.sandalet         0.317235         0.134853            2.352           0.019\n",
       "L4.mayo             0.100201         0.080638            1.243           0.214\n",
       "L4.sandalet         0.074501         0.137097            0.543           0.587\n",
       "L5.mayo            -0.025246         0.079958           -0.316           0.752\n",
       "L5.sandalet         0.016383         0.135782            0.121           0.904\n",
       "L6.mayo            -0.055095         0.079129           -0.696           0.486\n",
       "L6.sandalet         0.008581         0.133618            0.064           0.949\n",
       "L7.mayo             0.052805         0.077787            0.679           0.497\n",
       "L7.sandalet        -0.103760         0.127210           -0.816           0.415\n",
       "L8.mayo            -0.071402         0.056585           -1.262           0.207\n",
       "L8.sandalet         0.040841         0.095725            0.427           0.670\n",
       "==============================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                mayo  sandalet\n",
       "mayo        1.000000  0.525722\n",
       "sandalet    0.525722  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lag_order = 8\n",
    "results = model.fit(lag_order, ic=\"aic\")\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Durbin-Watson Statistic\n",
    "\n",
    "We will now check whether there is any correlation left in the residuals and if so, we will have to increase the order of our model. Values of this statistic varies between 0 & 4. Values closer to 2 mean that there is no significant serial correlation. Values close to 4 indicate strong negative correlation while those closer to 0 imply strong positive correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mayo : 2.01\n",
      "sandalet : 2.02\n"
     ]
    }
   ],
   "source": [
    "dw_r = durbin_watson(results.resid)\n",
    "\n",
    "for col, val in zip(data.columns, dw_r):\n",
    "    print(col, ':', round(val, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see, our values are close to 2. Therefore, we will be moving on to the next step.\n",
    "\n",
    "## Forecasting\n",
    "\n",
    "We will now do forecasting for 5 observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mayo_forecast</th>\n",
       "      <th>sandalet_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.034922</td>\n",
       "      <td>4.270794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.167580</td>\n",
       "      <td>4.704665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.707308</td>\n",
       "      <td>5.063059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.348480</td>\n",
       "      <td>6.481977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.553084</td>\n",
       "      <td>7.579432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mayo_forecast  sandalet_forecast\n",
       "0       9.034922           4.270794\n",
       "1       9.167580           4.704665\n",
       "2       8.707308           5.063059\n",
       "3      11.348480           6.481977\n",
       "4      10.553084           7.579432"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc = results.forecast(data.values[-lag_order:], steps=5)\n",
    "fc = pd.DataFrame(fc, columns=[\"mayo_forecast\", \"sandalet_forecast\"])\n",
    "fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_forecast(5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n",
    "\n",
    "We will begin testing with splitting the data into test and training datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = data[0:-5], data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=int(len(data.columns)/2), ncols=2, dpi=150, figsize=(10,3))\n",
    "\n",
    "for i, (col, ax) in enumerate(zip(data.columns, axes.flatten())):\n",
    "    fc[col + '_forecast'].plot(legend=True, ax=ax).autoscale(axis='x',tight=True)\n",
    "    df_test[col][-5:].plot(legend=True, ax=ax);\n",
    "    ax.set_title(col + \": Forecast vs Actuals\")\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "    ax.set_ylim((0,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Our predictions seem to be pretty accurate."
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}