_notebooks/2020-06-16-02-Seasonal-ARIMA-Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonal ARIMA Models\n",
    "> In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge. This is the Summary of lecture \"ARIMA Models in Python\", via datacamp.\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Time_Series_Analysis]\n",
    "- image: images/co2_predicted.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seasonal time series\n",
    "- Seasonal data\n",
    "    - Has predictable and repeated patterns\n",
    "    - Repeats after any amount of time\n",
    "- Seasonal decomposition\n",
    "    - time series = trend + seasonal + redisdual\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonal decompose\n",
    "You can think of a time series as being composed of trend, seasonal and residual components. This can be a good way to think about the data when you go about modeling it. If you know the period of the time series you can decompose it into these components.\n",
    "\n",
    "In this exercise you will decompose a time series showing the monthly milk production per cow in the USA. This will give you a clearer picture of the trend and the seasonal cycle. Since the data is monthly you will guess that the seasonality might be 12 time periods, however this won't always be the case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pounds_per_cow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            pounds_per_cow\n",
       "date                      \n",
       "1962-01-01           589.0\n",
       "1962-02-01           561.0\n",
       "1962-03-01           640.0\n",
       "1962-04-01           656.0\n",
       "1962-05-01           727.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk_production = pd.read_csv('./dataset/milk_production.csv', index_col='date', parse_dates=True)\n",
    "milk_production = milk_production.asfreq('MS')\n",
    "milk_production.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "# Perform additive decomposition\n",
    "decomp = seasonal_decompose(milk_production['pounds_per_cow'], period=12)\n",
    "\n",
    "# Plot decomposition\n",
    "decomp.plot();\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonal ACF and PACF\n",
    "Below is a time series showing the estimated number of water consumers in London. By eye you can't see any obvious seasonal pattern, however your eyes aren't the best tools you have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>water_consumers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>24963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-02-01</th>\n",
       "      <td>27380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-03-01</th>\n",
       "      <td>32588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-04-01</th>\n",
       "      <td>25511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-05-01</th>\n",
       "      <td>32313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            water_consumers\n",
       "date                       \n",
       "1983-01-01            24963\n",
       "1983-02-01            27380\n",
       "1983-03-01            32588\n",
       "1983-04-01            25511\n",
       "1983-05-01            32313"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water = pd.read_csv('./dataset/water.csv', index_col='date', parse_dates=True)\n",
    "water = water.asfreq('MS')\n",
    "water.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise you will use the ACF and PACF to test this data for seasonality. You can see from the plot above that the time series isn't stationary, so you should probably detrend it. You will detrend it by subtracting the moving average. Remember that you could use a window size of any value bigger than the likely period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "# Create figure and subplot\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot the ACF on ax1\n",
    "plot_acf(water['water_consumers'], lags=25, zero=False, ax=ax1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subtract the rolling mean\n",
    "water_2 = water - water.rolling(15).mean()\n",
    "\n",
    "# Drop the NaN values\n",
    "water_2 = water_2.dropna()\n",
    "\n",
    "# Create figure and subplots\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# Plot the ACF\n",
    "plot_acf(water_2['water_consumers'], lags=25, zero=False, ax=ax1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this figure, 12 time steps is the time period of the seasonal component."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SARIMA models\n",
    "- Seasonal ARIMA = SARIMA\n",
    "$$ \\text{SARIMA}(p, d, q)(P, D, Q)_S $$\n",
    "    - Non-seasonal orders\n",
    "        - p: autoregressive order\n",
    "        - d: differencing order\n",
    "        - q: moving average order\n",
    "    - Seasonal orders\n",
    "        - P: seasonal autoregressive order\n",
    "        - D: seasonal differencing order\n",
    "        - Q: seasonal moving average order\n",
    "        - S: Number of time steps per cycle\n",
    "- The SARIMA model\n",
    "    - ARIMA(2, 0, 1) model:\n",
    "$$ y_t = a_1 y_{t-1} + a_2 y_{t-2} + m_1 \\epsilon_{t-1} + \\epsilon_t $$\n",
    "    - $\\text{SARIMA}(0,0,0)(2, 0, 1)_7$ model:\n",
    "$$ y_t = a_7 y_{t-7} + a_{14} y_{t-14} + m_y \\epsilon_{t-7} + \\epsilon_t $$\n",
    "\n",
    "- Seasonal differencing\n",
    "    - Subtract the time series value of one season ago\n",
    "$$ \\Delta y_t = y_t - y_{t-S} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting SARIMA models\n",
    "Fitting SARIMA models is the beginning of the end of this journey into time series modeling.\n",
    "\n",
    "It is important that you get to know your way around the SARIMA model orders and so that's what you will focus on here.\n",
    "\n",
    "In this exercise, you will practice fitting different SARIMA models to a set of time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('./dataset/df1.csv', index_col=0, parse_dates=True)\n",
    "df2 = pd.read_csv('./dataset/df2.csv', index_col=0, parse_dates=True)\n",
    "df3 = pd.read_csv('./dataset/df3.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.asfreq('d')\n",
    "df2 = df2.asfreq('d')\n",
    "df3 = df3.asfreq('d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>90</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)x(1, 1, 0, 7)</td> <th>  Log Likelihood     </th> <td>-556.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                  <td>Tue, 16 Jun 2020</td>        <th>  AIC                </th> <td>1118.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                      <td>15:34:16</td>            <th>  BIC                </th> <td>1125.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                   <td>01-01-2013</td>           <th>  HQIC               </th> <td>1121.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                         <td>- 03-31-2013</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>    0.1032</td> <td>    0.103</td> <td>    1.002</td> <td> 0.316</td> <td>   -0.099</td> <td>    0.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L7</th> <td>    0.2781</td> <td>    0.104</td> <td>    2.665</td> <td> 0.008</td> <td>    0.074</td> <td>    0.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>  <td> 3.858e+04</td> <td> 7224.680</td> <td>    5.340</td> <td> 0.000</td> <td> 2.44e+04</td> <td> 5.27e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>41.76</td> <th>  Jarque-Bera (JB):  </th> <td>1.69</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.39</td>  <th>  Prob(JB):          </th> <td>0.43</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.35</td>  <th>  Skew:              </th> <td>-0.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.43</td>  <th>  Kurtosis:          </th> <td>2.37</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:                   90\n",
       "Model:             SARIMAX(1, 0, 0)x(1, 1, 0, 7)   Log Likelihood                -556.243\n",
       "Date:                           Tue, 16 Jun 2020   AIC                           1118.486\n",
       "Time:                                   15:34:16   BIC                           1125.742\n",
       "Sample:                               01-01-2013   HQIC                          1121.401\n",
       "                                    - 03-31-2013                                         \n",
       "Covariance Type:                             opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.1032      0.103      1.002      0.316      -0.099       0.305\n",
       "ar.S.L7        0.2781      0.104      2.665      0.008       0.074       0.483\n",
       "sigma2      3.858e+04   7224.680      5.340      0.000    2.44e+04    5.27e+04\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       41.76   Jarque-Bera (JB):                 1.69\n",
       "Prob(Q):                              0.39   Prob(JB):                         0.43\n",
       "Heteroskedasticity (H):               1.35   Skew:                            -0.15\n",
       "Prob(H) (two-sided):                  0.43   Kurtosis:                         2.37\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "# Create a SARIMAX model\n",
    "model = SARIMAX(df1, order=(1, 0, 0), seasonal_order=(1, 1, 0, 7))\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()\n",
    "\n",
    "# Print the results summary\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>80</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 1)x(1, 0, [], 4)</td> <th>  Log Likelihood     </th> <td>-560.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Tue, 16 Jun 2020</td>        <th>  AIC                </th> <td>1130.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>15:34:17</td>            <th>  BIC                </th> <td>1142.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>01-01-2013</td>           <th>  HQIC               </th> <td>1135.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 03-21-2013</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>    0.2701</td> <td>    0.162</td> <td>    1.672</td> <td> 0.095</td> <td>   -0.047</td> <td>    0.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>   <td>    0.5015</td> <td>    0.110</td> <td>    4.560</td> <td> 0.000</td> <td>    0.286</td> <td>    0.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>   <td>   -0.4271</td> <td>    0.178</td> <td>   -2.401</td> <td> 0.016</td> <td>   -0.776</td> <td>   -0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L4</th> <td>    0.1075</td> <td>    0.127</td> <td>    0.847</td> <td> 0.397</td> <td>   -0.141</td> <td>    0.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>  <td>  8.45e+04</td> <td> 1.63e+04</td> <td>    5.178</td> <td> 0.000</td> <td> 5.25e+04</td> <td> 1.16e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>30.80</td> <th>  Jarque-Bera (JB):  </th> <td>0.95</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.85</td>  <th>  Prob(JB):          </th> <td>0.62</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.60</td>  <th>  Skew:              </th> <td>-0.07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.20</td>  <th>  Kurtosis:          </th> <td>2.48</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:                   80\n",
       "Model:             SARIMAX(2, 1, 1)x(1, 0, [], 4)   Log Likelihood                -560.340\n",
       "Date:                            Tue, 16 Jun 2020   AIC                           1130.679\n",
       "Time:                                    15:34:17   BIC                           1142.526\n",
       "Sample:                                01-01-2013   HQIC                          1135.426\n",
       "                                     - 03-21-2013                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2701      0.162      1.672      0.095      -0.047       0.587\n",
       "ar.L2          0.5015      0.110      4.560      0.000       0.286       0.717\n",
       "ma.L1         -0.4271      0.178     -2.401      0.016      -0.776      -0.078\n",
       "ar.S.L4        0.1075      0.127      0.847      0.397      -0.141       0.356\n",
       "sigma2       8.45e+04   1.63e+04      5.178      0.000    5.25e+04    1.16e+05\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       30.80   Jarque-Bera (JB):                 0.95\n",
       "Prob(Q):                              0.85   Prob(JB):                         0.62\n",
       "Heteroskedasticity (H):               0.60   Skew:                            -0.07\n",
       "Prob(H) (two-sided):                  0.20   Kurtosis:                         2.48\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a SARIMAX model\n",
    "model = SARIMAX(df2, order=(2, 1, 1), seasonal_order=(1, 0, 0, 4))\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()\n",
    "\n",
    "# Print the results summary\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>100</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(0, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>-521.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Tue, 16 Jun 2020</td>         <th>  AIC                </th> <td>1048.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>15:34:17</td>             <th>  BIC                </th> <td>1056.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>01-01-2013</td>            <th>  HQIC               </th> <td>1051.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 04-10-2013</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>    0.4236</td> <td>    0.090</td> <td>    4.719</td> <td> 0.000</td> <td>    0.248</td> <td>    0.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.0898</td> <td>    0.116</td> <td>   -0.776</td> <td> 0.438</td> <td>   -0.317</td> <td>    0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 9347.1462</td> <td> 1407.490</td> <td>    6.641</td> <td> 0.000</td> <td> 6588.516</td> <td> 1.21e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>33.11</td> <th>  Jarque-Bera (JB):  </th> <td>0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.77</td>  <th>  Prob(JB):          </th> <td>0.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.77</td>  <th>  Skew:              </th> <td>0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.48</td>  <th>  Kurtosis:          </th> <td>3.05</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:                  100\n",
       "Model:             SARIMAX(1, 1, 0)x(0, 1, [1], 12)   Log Likelihood                -521.376\n",
       "Date:                              Tue, 16 Jun 2020   AIC                           1048.752\n",
       "Time:                                      15:34:17   BIC                           1056.149\n",
       "Sample:                                  01-01-2013   HQIC                          1051.730\n",
       "                                       - 04-10-2013                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.4236      0.090      4.719      0.000       0.248       0.600\n",
       "ma.S.L12      -0.0898      0.116     -0.776      0.438      -0.317       0.137\n",
       "sigma2      9347.1462   1407.490      6.641      0.000    6588.516    1.21e+04\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       33.11   Jarque-Bera (JB):                 0.02\n",
       "Prob(Q):                              0.77   Prob(JB):                         0.99\n",
       "Heteroskedasticity (H):               0.77   Skew:                             0.02\n",
       "Prob(H) (two-sided):                  0.48   Kurtosis:                         3.05\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a SARIMAX model\n",
    "model = SARIMAX(df3, order=(1, 1, 0), seasonal_order=(0, 1, 1, 12))\n",
    "\n",
    "# Fit the model\n",
    "results = model.fit()\n",
    "\n",
    "# Print the results summary\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing SARIMA order\n",
    "In this exercise you will find the appropriate model order for a new set of time series. This is monthly series of the number of employed persons in Australia (in thousands). The seasonal period of this time series is 12 months.\n",
    "\n",
    "You will create non-seasonal and seasonal ACF and PACF plots and use the table below to choose the appropriate model orders.\n",
    "\n",
    "|      | AR(p)                | MA(q)                | ARMA(p, q) |\n",
    "|------|----------------------|----------------------|------------|\n",
    "| ACF  | Tails off            | Cuts off after lag q | Tails off  |\n",
    "| PACF | Cuts off after lag p | Tails off            | Tails off  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>people_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>5985.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-02-01</th>\n",
       "      <td>6040.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-03-01</th>\n",
       "      <td>6054.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-04-01</th>\n",
       "      <td>6038.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-05-01</th>\n",
       "      <td>6031.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            people_employed\n",
       "date                       \n",
       "1978-01-01           5985.7\n",
       "1978-02-01           6040.6\n",
       "1978-03-01           6054.2\n",
       "1978-04-01           6038.3\n",
       "1978-05-01           6031.3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aus_employment = pd.read_csv('./dataset/aus_employment.csv', index_col='date', parse_dates=True)\n",
    "aus_employment = aus_employment.asfreq('MS')\n",
    "aus_employment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "\n",
    "# Take the first and seasonal differences and drop NaNs\n",
    "aus_employment_diff = aus_employment.diff().diff(12).dropna()\n",
    "\n",
    "# Create the figure \n",
    "fig, (ax1, ax2) = plt.subplots(2,1,figsize=(8,6))\n",
    "\n",
    "# Plot the ACF on ax1\n",
    "plot_acf(aus_employment_diff, lags=11, zero=False, ax=ax1);\n",
    "\n",
    "# Plot the PACF on ax2\n",
    "plot_pacf(aus_employment_diff, lags=11, zero=False, ax=ax2);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make list of lags\n",
    "lags = [12, 24, 36, 48, 60]\n",
    "\n",
    "# Create the figure \n",
    "fig, (ax1, ax2) = plt.subplots(2,1,figsize=(8,6))\n",
    "\n",
    "# Plot the ACF on ax1\n",
    "plot_acf(aus_employment_diff, lags=lags, zero=False, ax=ax1);\n",
    "\n",
    "# Plot the PACF on ax2\n",
    "plot_pacf(aus_employment_diff, lags=lags, zero=False, ax=ax2);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-seasonal ACF doesn't show any of the usual patterns of MA, AR or ARMA models so we choose none of these. The Seaosnal ACF and PACF look like an MA(1) model. : $\\text{SARIMAX}(0,1,0)(0,1,1)_{12}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SARIMA vs ARIMA forecasts\n",
    "In this exercise, you will see the effect of using a SARIMA model instead of an ARIMA model on your forecasts of seasonal time series.\n",
    "\n",
    "Two models, an ARIMA(3,1,2) and a SARIMA(0,1,1)(1,1,1)12, have been fit to the Wisconsin employment time series. These were the best ARIMA model and the best SARIMA model available according to the AIC.\n",
    "\n",
    "In the exercise you will use these two models to make dynamic future forecast for 25 months and plot these predictions alongside held out data for this period, wisconsin_test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_in_employment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1973-10-01</th>\n",
       "      <td>374.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-11-01</th>\n",
       "      <td>380.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-12-01</th>\n",
       "      <td>384.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>360.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-02-01</th>\n",
       "      <td>354.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number_in_employment\n",
       "date                            \n",
       "1973-10-01                 374.5\n",
       "1973-11-01                 380.2\n",
       "1973-12-01                 384.6\n",
       "1974-01-01                 360.6\n",
       "1974-02-01                 354.4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wisconsin_test = pd.read_csv('./dataset/wisconsin_test.csv', index_col='date', parse_dates=True)\n",
    "wisconsin_test = wisconsin_test.asfreq('MS')\n",
    "dates = wisconsin_test.index\n",
    "wisconsin_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "model = SARIMAX(wisconsin_test, order=(3, 1, 2), trend='c', \n",
    "                enforce_stationarity=True, enforce_invertibility=True)\n",
    "\n",
    "arima_results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/statespace/sarimax.py:868: UserWarning: Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
      "  ' zeros.' % warning_description)\n",
      "/home/chanseok/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "model = SARIMAX(wisconsin_test, order=(0, 1, 1), seasonal_order=(1, 1, 1, 12), trend='c', \n",
    "                enforce_stationarity=True, enforce_invertibility=True)\n",
    "\n",
    "sarima_results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'seasonal_periods': 0,\n",
       " 'measurement_error': False,\n",
       " 'time_varying_regression': False,\n",
       " 'simple_differencing': False,\n",
       " 'enforce_stationarity': True,\n",
       " 'enforce_invertibility': True,\n",
       " 'hamilton_representation': False,\n",
       " 'concentrate_scale': False,\n",
       " 'trend_offset': 1,\n",
       " 'order': (3, 1, 2),\n",
       " 'seasonal_order': (0, 0, 0, 0),\n",
       " 'k_diff': 1,\n",
       " 'k_seasonal_diff': 0,\n",
       " 'k_ar': 3,\n",
       " 'k_ma': 2,\n",
       " 'k_seasonal_ar': 0,\n",
       " 'k_seasonal_ma': 0,\n",
       " 'k_ar_params': 3,\n",
       " 'k_ma_params': 2,\n",
       " 'trend': 'c',\n",
       " 'k_trend': 1,\n",
       " 'k_exog': 0,\n",
       " 'mle_regression': False,\n",
       " 'state_regression': False}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arima_results.specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'seasonal_periods': 12,\n",
       " 'measurement_error': False,\n",
       " 'time_varying_regression': False,\n",
       " 'simple_differencing': False,\n",
       " 'enforce_stationarity': True,\n",
       " 'enforce_invertibility': True,\n",
       " 'hamilton_representation': False,\n",
       " 'concentrate_scale': False,\n",
       " 'trend_offset': 1,\n",
       " 'order': (0, 1, 1),\n",
       " 'seasonal_order': (1, 1, 1, 12),\n",
       " 'k_diff': 1,\n",
       " 'k_seasonal_diff': 1,\n",
       " 'k_ar': 0,\n",
       " 'k_ma': 1,\n",
       " 'k_seasonal_ar': 12,\n",
       " 'k_seasonal_ma': 12,\n",
       " 'k_ar_params': 0,\n",
       " 'k_ma_params': 1,\n",
       " 'trend': 'c',\n",
       " 'k_trend': 1,\n",
       " 'k_exog': 0,\n",
       " 'mle_regression': False,\n",
       " 'state_regression': False}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarima_results.specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create ARIMA mean forecast\n",
    "arima_pred = arima_results.get_forecast(25)\n",
    "arima_mean = arima_pred.predicted_mean\n",
    "\n",
    "# Create SARIMA mean forecast\n",
    "sarima_pred = sarima_results.get_forecast(25)\n",
    "sarima_mean = sarima_pred.predicted_mean\n",
    "\n",
    "# Plot mean ARIMA and SARIMA predictions and observed\n",
    "plt.plot(dates, sarima_mean, label='SARIMA');\n",
    "plt.plot(dates, arima_mean, label='ARIMA');\n",
    "plt.plot(wisconsin_test, label='observed');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automation and saving\n",
    "- pmdarima packages for Automated model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automated model selection\n",
    "The ```pmdarima``` package is a powerful tool to help you choose the model orders. You can use the information you already have from the identification step to narrow down the model orders which you choose by automation.\n",
    "\n",
    "Remember, although automation is powerful, it can sometimes make mistakes that you wouldn't. It is hard to guess how the input data could be imperfect and affect the test scores.\n",
    "\n",
    "In this exercise you will use the pmdarima package to automatically choose model orders for some time series datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA(2,0,2)x(1,1,1,7) [intercept=True]; AIC=1125.412, BIC=1144.763, Time=0.506 seconds\n",
      "Fit ARIMA(0,0,0)x(0,1,0,7) [intercept=True]; AIC=1124.433, BIC=1129.271, Time=0.009 seconds\n",
      "Fit ARIMA(1,0,0)x(1,1,0,7) [intercept=True]; AIC=1120.434, BIC=1130.109, Time=0.119 seconds\n",
      "Fit ARIMA(0,0,1)x(0,1,1,7) [intercept=True]; AIC=1122.218, BIC=1131.893, Time=0.075 seconds\n",
      "Fit ARIMA(0,0,0)x(0,1,0,7) [intercept=False]; AIC=1122.596, BIC=1125.015, Time=0.006 seconds\n",
      "Fit ARIMA(1,0,0)x(0,1,0,7) [intercept=True]; AIC=1125.048, BIC=1132.305, Time=0.016 seconds\n",
      "Fit ARIMA(1,0,0)x(2,1,0,7) [intercept=True]; AIC=1119.909, BIC=1132.004, Time=0.233 seconds\n",
      "Fit ARIMA(1,0,0)x(2,1,1,7) [intercept=True]; AIC=1121.791, BIC=1136.304, Time=0.209 seconds\n",
      "Fit ARIMA(1,0,0)x(1,1,1,7) [intercept=True]; AIC=1120.954, BIC=1133.049, Time=0.157 seconds\n",
      "Fit ARIMA(0,0,0)x(2,1,0,7) [intercept=True]; AIC=1119.066, BIC=1128.742, Time=0.166 seconds\n",
      "Fit ARIMA(0,0,0)x(1,1,0,7) [intercept=True]; AIC=1119.294, BIC=1126.550, Time=0.092 seconds\n",
      "Fit ARIMA(0,0,0)x(2,1,1,7) [intercept=True]; AIC=1120.813, BIC=1132.907, Time=0.173 seconds\n",
      "Fit ARIMA(0,0,0)x(1,1,1,7) [intercept=True]; AIC=1120.054, BIC=1129.729, Time=0.090 seconds\n",
      "Fit ARIMA(0,0,1)x(2,1,0,7) [intercept=True]; AIC=1119.937, BIC=1132.031, Time=0.126 seconds\n",
      "Fit ARIMA(1,0,1)x(2,1,0,7) [intercept=True]; AIC=1120.607, BIC=1135.120, Time=0.397 seconds\n",
      "Near non-invertible roots for order (1, 0, 1)(2, 1, 0, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
      "Total fit time: 2.380 seconds\n",
      "                                SARIMAX Results                                \n",
      "===============================================================================\n",
      "Dep. Variable:                       y   No. Observations:                   90\n",
      "Model:             SARIMAX(2, 1, 0, 7)   Log Likelihood                -555.533\n",
      "Date:                 Tue, 16 Jun 2020   AIC                           1119.066\n",
      "Time:                         15:34:55   BIC                           1128.742\n",
      "Sample:                              0   HQIC                          1122.953\n",
      "                                  - 90                                         \n",
      "Covariance Type:                   opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept     -5.3485     22.658     -0.236      0.813     -49.757      39.060\n",
      "ar.S.L7        0.2326      0.116      2.014      0.044       0.006       0.459\n",
      "ar.S.L14       0.1716      0.127      1.356      0.175      -0.077       0.420\n",
      "sigma2       3.79e+04   7066.970      5.363      0.000     2.4e+04    5.18e+04\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       33.73   Jarque-Bera (JB):                 1.88\n",
      "Prob(Q):                              0.75   Prob(JB):                         0.39\n",
      "Heteroskedasticity (H):               1.37   Skew:                            -0.24\n",
      "Prob(H) (two-sided):                  0.41   Kurtosis:                         2.44\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "import pmdarima as pm\n",
    "\n",
    "# Create auto_arima model\n",
    "model1 = pm.auto_arima(df1,\n",
    "                      seasonal=True, m=7,\n",
    "                      d=0, D=1,\n",
    "                      max_p=2, max_q=2,\n",
    "                      trace=True,\n",
    "                      error_action='ignore',\n",
    "                      suppress_warnings=True)\n",
    "\n",
    "# Print model summary\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA(2,1,2)x(0,0,0,0) [intercept=True]; AIC=1131.956, BIC=1146.173, Time=0.092 seconds\n",
      "Fit ARIMA(0,1,0)x(0,0,0,0) [intercept=True]; AIC=1153.963, BIC=1158.702, Time=0.005 seconds\n",
      "Fit ARIMA(1,1,0)x(0,0,0,0) [intercept=True]; AIC=1155.355, BIC=1162.464, Time=0.028 seconds\n",
      "Fit ARIMA(0,1,1)x(0,0,0,0) [intercept=True]; AIC=1155.747, BIC=1162.855, Time=0.011 seconds\n",
      "Fit ARIMA(0,1,0)x(0,0,0,0) [intercept=False]; AIC=1153.963, BIC=1158.702, Time=0.005 seconds\n",
      "Fit ARIMA(1,1,2)x(0,0,0,0) [intercept=True]; AIC=1136.103, BIC=1147.950, Time=0.114 seconds\n",
      "Fit ARIMA(2,1,1)x(0,0,0,0) [intercept=True]; AIC=1130.992, BIC=1142.840, Time=0.058 seconds\n",
      "Fit ARIMA(1,1,1)x(0,0,0,0) [intercept=True]; AIC=1149.020, BIC=1158.498, Time=0.087 seconds\n",
      "Fit ARIMA(2,1,0)x(0,0,0,0) [intercept=True]; AIC=1132.526, BIC=1142.003, Time=0.067 seconds\n",
      "Total fit time: 0.469 seconds\n",
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                   80\n",
      "Model:               SARIMAX(2, 1, 1)   Log Likelihood                -560.496\n",
      "Date:                Tue, 16 Jun 2020   AIC                           1130.992\n",
      "Time:                        15:34:55   BIC                           1142.840\n",
      "Sample:                             0   HQIC                          1135.739\n",
      "                                 - 80                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept     -9.5411     20.071     -0.475      0.635     -48.879      29.797\n",
      "ar.L1          0.2744      0.148      1.849      0.064      -0.016       0.565\n",
      "ar.L2          0.5383      0.098      5.503      0.000       0.347       0.730\n",
      "ma.L1         -0.4587      0.159     -2.886      0.004      -0.770      -0.147\n",
      "sigma2      8.558e+04   1.65e+04      5.197      0.000    5.33e+04    1.18e+05\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       30.31   Jarque-Bera (JB):                 0.85\n",
      "Prob(Q):                              0.87   Prob(JB):                         0.66\n",
      "Heteroskedasticity (H):               0.60   Skew:                            -0.09\n",
      "Prob(H) (two-sided):                  0.20   Kurtosis:                         2.53\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Create model\n",
    "model2 = pm.auto_arima(df2,\n",
    "                       seasonal=False,\n",
    "                       d=1,\n",
    "                       trend='c',\n",
    "                      max_p=2, max_q=2,\n",
    "                      trace=True,\n",
    "                      error_action='ignore',\n",
    "                      suppress_warnings=True)\n",
    "\n",
    "# Print model summary\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA(1,1,1)x(1,1,1,7) [intercept=True]; AIC=1566.379, BIC=1581.510, Time=0.427 seconds\n",
      "Fit ARIMA(0,1,0)x(0,1,0,7) [intercept=True]; AIC=1595.826, BIC=1600.870, Time=0.007 seconds\n",
      "Fit ARIMA(1,1,0)x(1,1,0,7) [intercept=True]; AIC=1585.203, BIC=1595.290, Time=0.093 seconds\n",
      "Fit ARIMA(0,1,1)x(0,1,1,7) [intercept=True]; AIC=1572.745, BIC=1582.832, Time=0.218 seconds\n",
      "Near non-invertible roots for order (0, 1, 1)(0, 1, 1, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.999)\n",
      "Fit ARIMA(0,1,0)x(0,1,0,7) [intercept=False]; AIC=1593.897, BIC=1596.419, Time=0.009 seconds\n",
      "Fit ARIMA(1,1,1)x(0,1,1,7) [intercept=True]; AIC=1558.168, BIC=1570.777, Time=0.298 seconds\n",
      "Near non-invertible roots for order (1, 1, 1)(0, 1, 1, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
      "Fit ARIMA(1,1,1)x(1,1,0,7) [intercept=True]; AIC=1565.768, BIC=1578.377, Time=0.259 seconds\n",
      "Near non-invertible roots for order (1, 1, 1)(1, 1, 0, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 1.000)\n",
      "Fit ARIMA(1,1,1)x(0,1,0,7) [intercept=True]; AIC=1579.028, BIC=1589.116, Time=0.107 seconds\n",
      "Near non-invertible roots for order (1, 1, 1)(0, 1, 0, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.994)\n",
      "Fit ARIMA(0,1,1)x(1,1,1,7) [intercept=True]; AIC=1571.169, BIC=1583.778, Time=0.323 seconds\n",
      "Fit ARIMA(1,1,0)x(1,1,1,7) [intercept=True]; AIC=1570.775, BIC=1583.384, Time=0.260 seconds\n",
      "Near non-invertible roots for order (1, 1, 0)(1, 1, 1, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.993)\n",
      "Fit ARIMA(0,1,0)x(1,1,1,7) [intercept=True]; AIC=1573.132, BIC=1583.219, Time=0.244 seconds\n",
      "Near non-invertible roots for order (0, 1, 0)(1, 1, 1, 7); setting score to inf (at least one inverse root too close to the border of the unit circle: 0.997)\n",
      "Total fit time: 2.248 seconds\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                                 y   No. Observations:                  100\n",
      "Model:             SARIMAX(1, 1, 1)x(0, 1, 1, 7)   Log Likelihood                -774.084\n",
      "Date:                           Tue, 16 Jun 2020   AIC                           1558.168\n",
      "Time:                                   15:34:58   BIC                           1570.777\n",
      "Sample:                                        0   HQIC                          1563.257\n",
      "                                           - 100                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "intercept      1.7464      2.134      0.819      0.413      -2.435       5.928\n",
      "ar.L1          0.4239      0.127      3.342      0.001       0.175       0.672\n",
      "ma.L1         -1.0000      0.250     -4.004      0.000      -1.489      -0.510\n",
      "ma.S.L7       -0.9382      0.231     -4.056      0.000      -1.392      -0.485\n",
      "sigma2      1.008e+06    2.6e-07   3.88e+12      0.000    1.01e+06    1.01e+06\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                      272.00   Jarque-Bera (JB):                11.30\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.07   Skew:                             0.85\n",
      "Prob(H) (two-sided):                  0.86   Kurtosis:                         2.84\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.73e+29. Standard errors may be unstable.\n"
     ]
    }
   ],
   "source": [
    "# Create model for SARIMAX(p,1,q)(P,1,Q)7\n",
    "model3 = pm.auto_arima(df3,\n",
    "                      seasonal=True, m=7,\n",
    "                      d=1, D=1,\n",
    "                      start_p=1, start_q=1,\n",
    "                      max_p=1, max_q=1,\n",
    "                      max_P=1, max_Q=1,\n",
    "                      trace=True,\n",
    "                      error_action='ignore',\n",
    "                      suppress_warnings=True)\n",
    "\n",
    "# Print model summary\n",
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and updating models\n",
    "Once you have gone through the steps of the Box-Jenkins method and arrived at a model you are happy with, you will want to be able to save that model and also to incorporate new measurements when they are available. This is key part of putting the model into production.\n",
    "\n",
    "In this exercise you will save a freshly trained model to disk, then reload it to update it with new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "# Set model name\n",
    "filename='candy_model.pkl'\n",
    "\n",
    "# Pickle it\n",
    "joblib.dump(model1, filename)\n",
    "\n",
    "# Load the model back in\n",
    "loaded_model = joblib.load(filename)\n",
    "\n",
    "# Update the model\n",
    "# loaded_model.update(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SARIMA and Box-Jenkins\n",
    "- Box-Jenkins with seasonal data\n",
    "    - Determine if time series is seasonal\n",
    "    - Find seasonal period\n",
    "    - Find transforms to make data stationary\n",
    "        - Seasonal and non-seasonal differencing\n",
    "        - Other transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SARIMA model diagnostics\n",
    "Usually the next step would be to find the order of differencing and other model orders. However, this time it's already been done for you. The time series is best fit by a $\\text{SARIMA}(1, 1, 1)(0, 1, 1)_{12}$ model with an added constant.\n",
    "\n",
    "In this exercise you will make sure that this is a good model by first fitting it using the ```SARIMAX``` class and going through the normal model diagnostics procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2_ppm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-01</th>\n",
       "      <td>315.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-01</th>\n",
       "      <td>317.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-05-01</th>\n",
       "      <td>317.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-06-01</th>\n",
       "      <td>317.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>315.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO2_ppm\n",
       "date               \n",
       "1958-03-01   315.71\n",
       "1958-04-01   317.45\n",
       "1958-05-01   317.50\n",
       "1958-06-01   317.10\n",
       "1958-07-01   315.86"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2 = pd.read_csv('./dataset/co2.csv', index_col='date', parse_dates=True)\n",
    "co2 = co2.asfreq('MS')\n",
    "co2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create model object\n",
    "model = SARIMAX(co2, order=(1, 1, 1), seasonal_order=(0, 1, 1, 12), trend='c')\n",
    "\n",
    "# Fit model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>               <td>CO2_ppm</td>            <th>  No. Observations:  </th>    <td>727</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, 1, 12)</td> <th>  Log Likelihood     </th> <td>-179.718</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Tue, 16 Jun 2020</td>        <th>  AIC                </th>  <td>369.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>15:35:01</td>            <th>  BIC                </th>  <td>392.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>03-01-1958</td>           <th>  HQIC               </th>  <td>378.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 09-01-2018</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>intercept</th> <td>    0.0017</td> <td>    0.001</td> <td>    2.809</td> <td> 0.005</td> <td>    0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.2411</td> <td>    0.078</td> <td>    3.072</td> <td> 0.002</td> <td>    0.087</td> <td>    0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -0.6079</td> <td>    0.065</td> <td>   -9.296</td> <td> 0.000</td> <td>   -0.736</td> <td>   -0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>   -0.8820</td> <td>    0.019</td> <td>  -45.520</td> <td> 0.000</td> <td>   -0.920</td> <td>   -0.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0944</td> <td>    0.005</td> <td>   20.750</td> <td> 0.000</td> <td>    0.085</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>47.00</td> <th>  Jarque-Bera (JB):  </th> <td>6.19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.21</td>  <th>  Prob(JB):          </th> <td>0.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.13</td>  <th>  Skew:              </th> <td>0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.33</td>  <th>  Kurtosis:          </th> <td>3.46</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:                            CO2_ppm   No. Observations:                  727\n",
       "Model:             SARIMAX(1, 1, 1)x(0, 1, 1, 12)   Log Likelihood                -179.718\n",
       "Date:                            Tue, 16 Jun 2020   AIC                            369.437\n",
       "Time:                                    15:35:01   BIC                            392.291\n",
       "Sample:                                03-01-1958   HQIC                           378.263\n",
       "                                     - 09-01-2018                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0017      0.001      2.809      0.005       0.001       0.003\n",
       "ar.L1          0.2411      0.078      3.072      0.002       0.087       0.395\n",
       "ma.L1         -0.6079      0.065     -9.296      0.000      -0.736      -0.480\n",
       "ma.S.L12      -0.8820      0.019    -45.520      0.000      -0.920      -0.844\n",
       "sigma2         0.0944      0.005     20.750      0.000       0.085       0.103\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       47.00   Jarque-Bera (JB):                 6.19\n",
       "Prob(Q):                              0.21   Prob(JB):                         0.05\n",
       "Heteroskedasticity (H):               1.13   Skew:                             0.00\n",
       "Prob(H) (two-sided):                  0.33   Kurtosis:                         3.46\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot common diagnostics\n",
    "results.plot_diagnostics(figsize=(20, 15));\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SARIMA forecast\n",
    "In the previous exercise you confirmed that a SARIMA (1,1,1) x (0,1,1)12 model was a good fit to the CO2 time series by using diagnostic checking.\n",
    "\n",
    "Now its time to put this model into practice to make future forecasts. Climate scientists tell us that we have until 2030 to drastically reduce our CO2 emissions or we will face major societal challenges.\n",
    "\n",
    "In this exercise, you will forecast the CO2 time series up to the year 2030 to find the CO2 levels if we continue emitting as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create forecast object\n",
    "forecast_object = results.get_forecast(136)\n",
    "\n",
    "# Extract predicted mean attribute\n",
    "mean = forecast_object.predicted_mean\n",
    "\n",
    "# Calculate the confidence intervals\n",
    "conf_int = forecast_object.conf_int()\n",
    "\n",
    "# Extract the forecast dates\n",
    "dates = mean.index\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Plot past CO2 levels\n",
    "plt.plot(co2.index, co2, label='past');\n",
    "\n",
    "# Plot the prediction means as line\n",
    "plt.plot(dates, mean, label='predicted');\n",
    "\n",
    "# Shade between the confidence intervals\n",
    "plt.fill_between(dates, conf_int.loc[:, 'lower CO2_ppm'], \n",
    "                 conf_int.loc[:, 'upper CO2_ppm'], alpha=0.4);\n",
    "\n",
    "# Plot legend\n",
    "plt.legend();\n",
    "plt.savefig('../images/co2_predicted.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438.2001450227593\n",
      "lower CO2_ppm    432.118155\n",
      "upper CO2_ppm    444.282135\n",
      "Name: 2030-01-01 00:00:00, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Print last predicted mean\n",
    "print(mean.iloc[-1])\n",
    "\n",
    "# Print last confidence interval\n",
    "print(conf_int.iloc[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The SARIMAX model\n",
    "\n",
    "**S** - seasonal\\\n",
    "**AR** - AutoRegressive\\\n",
    "**I** - Integrated\\\n",
    "**MA** - Moving Average\\\n",
    "**X** - Exogenous\n"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}