demo5_statsmodels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0933cae4-36ea-42aa-b76f-6fead9dd7fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_predict\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4400d56-9bc8-41b7-bee7-fa311c63cbea",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Create Pandas timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3521deb1-60a0-46c9-8bf9-49f9fbea6f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "datain = pd.read_csv(\"datasets/csv4.csv\")\n",
    "datain['Date'] = pd.to_datetime(datain['Date'])\n",
    "datalist = datain['Value'].to_numpy()\n",
    "dsize = datalist.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abfae8ef-3355-4e3c-9783-6cb6fec5f39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-10-28 14:29:00    11.2\n",
       "2021-10-28 15:29:00    10.8\n",
       "2021-10-28 16:29:00    10.8\n",
       "2021-10-28 17:29:00    10.5\n",
       "2021-10-28 18:29:00    10.3\n",
       "                       ... \n",
       "2021-11-20 15:29:00     6.0\n",
       "2021-11-20 16:29:00     6.4\n",
       "2021-11-20 17:29:00     5.2\n",
       "2021-11-20 18:29:00     4.6\n",
       "2021-11-20 19:29:00     4.3\n",
       "Freq: H, Length: 558, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.Series(datain['Value'][1:].values, datain['Date'][1:].values).asfreq('H')\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8adae382-6af6-4fe3-81a5-6ce785709aed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25303d18550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis = np.arange(dsize)\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(x_axis, datalist, color=\"b\", label=\"Real\")\n",
    "plt.legend(loc=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5a53296e-945e-4295-98c5-ed2bc3746983",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Huan.Le\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "model = ARIMA(series, order=(50, 0, 0), trend=\"n\")\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b0cb35aa-2015-49e1-8144-6d45959aed57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>558</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(50, 0, 0)</td> <th>  Log Likelihood     </th> <td>-1038.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 24 May 2022</td> <th>  AIC                </th> <td>2178.049</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>10:09:52</td>     <th>  BIC                </th> <td>2398.591</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-28-2021</td>    <th>  HQIC               </th> <td>2264.178</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-20-2021</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    1.0070</td> <td>    0.029</td> <td>   34.426</td> <td> 0.000</td> <td>    0.950</td> <td>    1.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.0920</td> <td>    0.043</td> <td>   -2.141</td> <td> 0.032</td> <td>   -0.176</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.0455</td> <td>    0.049</td> <td>   -0.936</td> <td> 0.349</td> <td>   -0.141</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>   -0.0462</td> <td>    0.054</td> <td>   -0.856</td> <td> 0.392</td> <td>   -0.152</td> <td>    0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td>    0.0892</td> <td>    0.063</td> <td>    1.408</td> <td> 0.159</td> <td>   -0.035</td> <td>    0.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L6</th>  <td>   -0.1477</td> <td>    0.066</td> <td>   -2.249</td> <td> 0.025</td> <td>   -0.276</td> <td>   -0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L7</th>  <td>    0.0123</td> <td>    0.078</td> <td>    0.158</td> <td> 0.875</td> <td>   -0.141</td> <td>    0.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L8</th>  <td>    0.0274</td> <td>    0.083</td> <td>    0.331</td> <td> 0.741</td> <td>   -0.135</td> <td>    0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L9</th>  <td>    0.0501</td> <td>    0.101</td> <td>    0.497</td> <td> 0.620</td> <td>   -0.148</td> <td>    0.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L10</th> <td>    0.0267</td> <td>    0.108</td> <td>    0.247</td> <td> 0.805</td> <td>   -0.185</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L11</th> <td>   -0.0512</td> <td>    0.115</td> <td>   -0.445</td> <td> 0.656</td> <td>   -0.277</td> <td>    0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L12</th> <td>   -0.0130</td> <td>    0.112</td> <td>   -0.116</td> <td> 0.908</td> <td>   -0.232</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L13</th> <td>    0.0001</td> <td>    0.091</td> <td>    0.001</td> <td> 0.999</td> <td>   -0.178</td> <td>    0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L14</th> <td>    0.0115</td> <td>    0.103</td> <td>    0.111</td> <td> 0.912</td> <td>   -0.191</td> <td>    0.214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L15</th> <td>    0.0432</td> <td>    0.092</td> <td>    0.469</td> <td> 0.639</td> <td>   -0.137</td> <td>    0.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L16</th> <td>   -0.0671</td> <td>    0.095</td> <td>   -0.706</td> <td> 0.480</td> <td>   -0.253</td> <td>    0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L17</th> <td>    0.0298</td> <td>    0.071</td> <td>    0.419</td> <td> 0.675</td> <td>   -0.110</td> <td>    0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L18</th> <td>    0.0317</td> <td>    0.056</td> <td>    0.562</td> <td> 0.574</td> <td>   -0.079</td> <td>    0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L19</th> <td>   -0.0183</td> <td>    0.058</td> <td>   -0.312</td> <td> 0.755</td> <td>   -0.133</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L20</th> <td>   -0.0317</td> <td>    0.061</td> <td>   -0.524</td> <td> 0.600</td> <td>   -0.151</td> <td>    0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L21</th> <td>    0.0849</td> <td>    0.067</td> <td>    1.276</td> <td> 0.202</td> <td>   -0.046</td> <td>    0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L22</th> <td>   -0.0646</td> <td>    0.066</td> <td>   -0.985</td> <td> 0.325</td> <td>   -0.193</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L23</th> <td>    0.1825</td> <td>    0.058</td> <td>    3.134</td> <td> 0.002</td> <td>    0.068</td> <td>    0.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L24</th> <td>    0.1190</td> <td>    0.053</td> <td>    2.248</td> <td> 0.025</td> <td>    0.015</td> <td>    0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L25</th> <td>   -0.2700</td> <td>    0.049</td> <td>   -5.493</td> <td> 0.000</td> <td>   -0.366</td> <td>   -0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L26</th> <td>    0.1502</td> <td>    0.058</td> <td>    2.581</td> <td> 0.010</td> <td>    0.036</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L27</th> <td>    0.0524</td> <td>    0.059</td> <td>    0.890</td> <td> 0.374</td> <td>   -0.063</td> <td>    0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L28</th> <td>   -0.1343</td> <td>    0.063</td> <td>   -2.115</td> <td> 0.034</td> <td>   -0.259</td> <td>   -0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L29</th> <td>    0.0444</td> <td>    0.068</td> <td>    0.651</td> <td> 0.515</td> <td>   -0.089</td> <td>    0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L30</th> <td>    0.0530</td> <td>    0.085</td> <td>    0.625</td> <td> 0.532</td> <td>   -0.113</td> <td>    0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L31</th> <td>   -0.0475</td> <td>    0.099</td> <td>   -0.480</td> <td> 0.631</td> <td>   -0.241</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L32</th> <td>   -0.0325</td> <td>    0.102</td> <td>   -0.319</td> <td> 0.749</td> <td>   -0.232</td> <td>    0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L33</th> <td>    0.1095</td> <td>    0.111</td> <td>    0.986</td> <td> 0.324</td> <td>   -0.108</td> <td>    0.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L34</th> <td>   -0.0611</td> <td>    0.135</td> <td>   -0.453</td> <td> 0.651</td> <td>   -0.326</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L35</th> <td>    0.0241</td> <td>    0.130</td> <td>    0.185</td> <td> 0.853</td> <td>   -0.231</td> <td>    0.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L36</th> <td>   -0.0224</td> <td>    0.131</td> <td>   -0.172</td> <td> 0.864</td> <td>   -0.279</td> <td>    0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L37</th> <td>    0.0315</td> <td>    0.129</td> <td>    0.243</td> <td> 0.808</td> <td>   -0.222</td> <td>    0.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L38</th> <td>   -0.0672</td> <td>    0.129</td> <td>   -0.521</td> <td> 0.603</td> <td>   -0.320</td> <td>    0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L39</th> <td>    0.0317</td> <td>    0.124</td> <td>    0.255</td> <td> 0.799</td> <td>   -0.211</td> <td>    0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L40</th> <td>    0.0388</td> <td>    0.117</td> <td>    0.332</td> <td> 0.740</td> <td>   -0.190</td> <td>    0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L41</th> <td>   -0.0039</td> <td>    0.108</td> <td>   -0.036</td> <td> 0.971</td> <td>   -0.216</td> <td>    0.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L42</th> <td>   -0.0750</td> <td>    0.082</td> <td>   -0.915</td> <td> 0.360</td> <td>   -0.236</td> <td>    0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L43</th> <td>    0.0976</td> <td>    0.071</td> <td>    1.381</td> <td> 0.167</td> <td>   -0.041</td> <td>    0.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L44</th> <td>   -0.0750</td> <td>    0.079</td> <td>   -0.945</td> <td> 0.345</td> <td>   -0.230</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L45</th> <td>    0.0112</td> <td>    0.069</td> <td>    0.163</td> <td> 0.870</td> <td>   -0.123</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L46</th> <td>   -0.0279</td> <td>    0.069</td> <td>   -0.404</td> <td> 0.686</td> <td>   -0.164</td> <td>    0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L47</th> <td>    0.0762</td> <td>    0.090</td> <td>    0.844</td> <td> 0.399</td> <td>   -0.101</td> <td>    0.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L48</th> <td>    0.0664</td> <td>    0.060</td> <td>    1.099</td> <td> 0.272</td> <td>   -0.052</td> <td>    0.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L49</th> <td>    0.0091</td> <td>    0.060</td> <td>    0.152</td> <td> 0.879</td> <td>   -0.108</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L50</th> <td>   -0.1212</td> <td>    0.047</td> <td>   -2.585</td> <td> 0.010</td> <td>   -0.213</td> <td>   -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    2.5604</td> <td>    0.119</td> <td>   21.559</td> <td> 0.000</td> <td>    2.328</td> <td>    2.793</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.09</td> <th>  Jarque-Bera (JB):  </th> <td>1510.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.76</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.34</td> <th>  Skew:              </th>  <td>0.43</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>11.02</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  558\n",
       "Model:                ARIMA(50, 0, 0)   Log Likelihood               -1038.024\n",
       "Date:                Tue, 24 May 2022   AIC                           2178.049\n",
       "Time:                        10:09:52   BIC                           2398.591\n",
       "Sample:                    10-28-2021   HQIC                          2264.178\n",
       "                         - 11-20-2021                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          1.0070      0.029     34.426      0.000       0.950       1.064\n",
       "ar.L2         -0.0920      0.043     -2.141      0.032      -0.176      -0.008\n",
       "ar.L3         -0.0455      0.049     -0.936      0.349      -0.141       0.050\n",
       "ar.L4         -0.0462      0.054     -0.856      0.392      -0.152       0.060\n",
       "ar.L5          0.0892      0.063      1.408      0.159      -0.035       0.213\n",
       "ar.L6         -0.1477      0.066     -2.249      0.025      -0.276      -0.019\n",
       "ar.L7          0.0123      0.078      0.158      0.875      -0.141       0.165\n",
       "ar.L8          0.0274      0.083      0.331      0.741      -0.135       0.190\n",
       "ar.L9          0.0501      0.101      0.497      0.620      -0.148       0.248\n",
       "ar.L10         0.0267      0.108      0.247      0.805      -0.185       0.238\n",
       "ar.L11        -0.0512      0.115     -0.445      0.656      -0.277       0.174\n",
       "ar.L12        -0.0130      0.112     -0.116      0.908      -0.232       0.206\n",
       "ar.L13         0.0001      0.091      0.001      0.999      -0.178       0.178\n",
       "ar.L14         0.0115      0.103      0.111      0.912      -0.191       0.214\n",
       "ar.L15         0.0432      0.092      0.469      0.639      -0.137       0.224\n",
       "ar.L16        -0.0671      0.095     -0.706      0.480      -0.253       0.119\n",
       "ar.L17         0.0298      0.071      0.419      0.675      -0.110       0.170\n",
       "ar.L18         0.0317      0.056      0.562      0.574      -0.079       0.142\n",
       "ar.L19        -0.0183      0.058     -0.312      0.755      -0.133       0.096\n",
       "ar.L20        -0.0317      0.061     -0.524      0.600      -0.151       0.087\n",
       "ar.L21         0.0849      0.067      1.276      0.202      -0.046       0.215\n",
       "ar.L22        -0.0646      0.066     -0.985      0.325      -0.193       0.064\n",
       "ar.L23         0.1825      0.058      3.134      0.002       0.068       0.297\n",
       "ar.L24         0.1190      0.053      2.248      0.025       0.015       0.223\n",
       "ar.L25        -0.2700      0.049     -5.493      0.000      -0.366      -0.174\n",
       "ar.L26         0.1502      0.058      2.581      0.010       0.036       0.264\n",
       "ar.L27         0.0524      0.059      0.890      0.374      -0.063       0.168\n",
       "ar.L28        -0.1343      0.063     -2.115      0.034      -0.259      -0.010\n",
       "ar.L29         0.0444      0.068      0.651      0.515      -0.089       0.178\n",
       "ar.L30         0.0530      0.085      0.625      0.532      -0.113       0.219\n",
       "ar.L31        -0.0475      0.099     -0.480      0.631      -0.241       0.146\n",
       "ar.L32        -0.0325      0.102     -0.319      0.749      -0.232       0.167\n",
       "ar.L33         0.1095      0.111      0.986      0.324      -0.108       0.327\n",
       "ar.L34        -0.0611      0.135     -0.453      0.651      -0.326       0.204\n",
       "ar.L35         0.0241      0.130      0.185      0.853      -0.231       0.279\n",
       "ar.L36        -0.0224      0.131     -0.172      0.864      -0.279       0.234\n",
       "ar.L37         0.0315      0.129      0.243      0.808      -0.222       0.285\n",
       "ar.L38        -0.0672      0.129     -0.521      0.603      -0.320       0.186\n",
       "ar.L39         0.0317      0.124      0.255      0.799      -0.211       0.275\n",
       "ar.L40         0.0388      0.117      0.332      0.740      -0.190       0.268\n",
       "ar.L41        -0.0039      0.108     -0.036      0.971      -0.216       0.208\n",
       "ar.L42        -0.0750      0.082     -0.915      0.360      -0.236       0.086\n",
       "ar.L43         0.0976      0.071      1.381      0.167      -0.041       0.236\n",
       "ar.L44        -0.0750      0.079     -0.945      0.345      -0.230       0.080\n",
       "ar.L45         0.0112      0.069      0.163      0.870      -0.123       0.146\n",
       "ar.L46        -0.0279      0.069     -0.404      0.686      -0.164       0.108\n",
       "ar.L47         0.0762      0.090      0.844      0.399      -0.101       0.253\n",
       "ar.L48         0.0664      0.060      1.099      0.272      -0.052       0.185\n",
       "ar.L49         0.0091      0.060      0.152      0.879      -0.108       0.126\n",
       "ar.L50        -0.1212      0.047     -2.585      0.010      -0.213      -0.029\n",
       "sigma2         2.5604      0.119     21.559      0.000       2.328       2.793\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.09   Jarque-Bera (JB):              1510.86\n",
       "Prob(Q):                              0.76   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.34   Skew:                             0.43\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.02\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b27990b8-78e1-4abf-98d9-79d978f2254e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "fig = plot_predict(results, end='2021-11-25', ax=ax)\n",
    "legend = ax.legend(loc=\"upper left\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}