Time_Series_ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference : https://medium.com/@josemarcialportilla/using-python-and-auto-arima-to-forecast-seasonal-time-series-90877adff03c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time Series have several key features such as trend, seasonality, and noise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARIMA model, which stands for AutoRegressive Integrated Moving Average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are labeled p,d,and q.\n",
    "\n",
    "p is the parameter associated with the auto-regressive aspect of the model, which incorporates past values. For example, forecasting that if it rained a lot over the past few days, you state its likely that it will rain tomorrow as well.\n",
    "\n",
    "d is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series. You can imagine an example of this as forecasting that the amount of rain tomorrow will be similar to the amount of rain today, if the daily amounts of rain have been similar over the past few days.\n",
    "\n",
    "q is the parameter associated with the moving average part of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If our model has a seasonal component, we use a seasonal ARIMA model (SARIMA). In that case we have another set of parameters: P,D, and Q which describe the same associations as p,d, and q, but correspond with the seasonal components of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPG2211A2N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>72.5052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-02-01</td>\n",
       "      <td>70.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-03-01</td>\n",
       "      <td>62.4502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-04-01</td>\n",
       "      <td>57.4714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-05-01</td>\n",
       "      <td>55.3151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-06-01</td>\n",
       "      <td>58.0904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-07-01</td>\n",
       "      <td>62.6202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-08-01</td>\n",
       "      <td>63.2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-09-01</td>\n",
       "      <td>60.5846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-10-01</td>\n",
       "      <td>56.3154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-11-01</td>\n",
       "      <td>58.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1985-12-01</td>\n",
       "      <td>68.7145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-01-01</td>\n",
       "      <td>73.3057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-02-01</td>\n",
       "      <td>67.9869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-03-01</td>\n",
       "      <td>62.2221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-04-01</td>\n",
       "      <td>57.0329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-05-01</td>\n",
       "      <td>55.8137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-06-01</td>\n",
       "      <td>59.9005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-07-01</td>\n",
       "      <td>65.7655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1986-08-01</td>\n",
       "      <td>64.4816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            IPG2211A2N\n",
       "DATE                  \n",
       "1985-01-01     72.5052\n",
       "1985-02-01     70.6720\n",
       "1985-03-01     62.4502\n",
       "1985-04-01     57.4714\n",
       "1985-05-01     55.3151\n",
       "1985-06-01     58.0904\n",
       "1985-07-01     62.6202\n",
       "1985-08-01     63.2485\n",
       "1985-09-01     60.5846\n",
       "1985-10-01     56.3154\n",
       "1985-11-01     58.0005\n",
       "1985-12-01     68.7145\n",
       "1986-01-01     73.3057\n",
       "1986-02-01     67.9869\n",
       "1986-03-01     62.2221\n",
       "1986-04-01     57.0329\n",
       "1986-05-01     55.8137\n",
       "1986-06-01     59.9005\n",
       "1986-07-01     65.7655\n",
       "1986-08-01     64.4816"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/manishanker/AutoArima-Time-Series-Blog/master/Electric_Production.csv\",\n",
    "                   index_col=0)\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IPG2211A2N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>104.0375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>114.5397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>115.5159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>102.7637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>91.4867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>92.8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>112.7694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>114.8505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>99.4901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>101.0396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>88.3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>92.0805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>102.1532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>112.1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>108.9312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>98.6154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>93.6137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>97.3359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>114.7212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>129.4048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            IPG2211A2N\n",
       "DATE                  \n",
       "2016-06-01    104.0375\n",
       "2016-07-01    114.5397\n",
       "2016-08-01    115.5159\n",
       "2016-09-01    102.7637\n",
       "2016-10-01     91.4867\n",
       "2016-11-01     92.8900\n",
       "2016-12-01    112.7694\n",
       "2017-01-01    114.8505\n",
       "2017-02-01     99.4901\n",
       "2017-03-01    101.0396\n",
       "2017-04-01     88.3530\n",
       "2017-05-01     92.0805\n",
       "2017-06-01    102.1532\n",
       "2017-07-01    112.1538\n",
       "2017-08-01    108.9312\n",
       "2017-09-01     98.6154\n",
       "2017-10-01     93.6137\n",
       "2017-11-01     97.3359\n",
       "2017-12-01    114.7212\n",
       "2018-01-01    129.4048"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(397, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index = pd.to_datetime(data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['Energy Production']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e41f1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.figure(figsize=(12, 8))  \n",
    "result = seasonal_decompose(data, model='multiplicative')\n",
    "fig = result.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above we can clearly see the seasonal component of the data, and we can also see the separated upward trend of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One simple approach is to perform a grid search over multiple values of p,d,q,P,D,and Q using some sort of performance criteria. The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1782.527, BIC=1802.280, Fit time=3.264 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 1, 0, 12); AIC=2012.117, BIC=2020.018, Fit time=0.090 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 0, 12); AIC=1942.041, BIC=1957.844, Fit time=0.552 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 1, 1, 12); AIC=1837.289, BIC=1853.091, Fit time=0.971 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 1, 12); AIC=1783.889, BIC=1807.593, Fit time=3.163 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 0, 12); AIC=1920.884, BIC=1936.687, Fit time=0.855 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 1, 2, 12); AIC=1783.389, BIC=1807.093, Fit time=9.263 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1782.021, BIC=1809.675, Fit time=7.014 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1837.164, BIC=1860.868, Fit time=3.102 seconds\n",
      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(1, 1, 2, 12); AIC=1782.825, BIC=1814.430, Fit time=10.632 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=1852.588, BIC=1876.291, Fit time=2.656 seconds\n",
      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=1782.073, BIC=1813.678, Fit time=9.234 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 1, 2, 12); AIC=1864.184, BIC=1883.938, Fit time=2.725 seconds\n",
      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(1, 1, 2, 12); AIC=1784.157, BIC=1819.713, Fit time=10.437 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1770.229, BIC=1801.834, Fit time=11.147 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(2, 1, 1, 12); AIC=1771.287, BIC=1798.941, Fit time=6.129 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1816.629, BIC=1844.283, Fit time=4.591 seconds\n",
      "Fit ARIMA: order=(2, 1, 1) seasonal_order=(2, 1, 2, 12); AIC=1771.990, BIC=1807.546, Fit time=11.551 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(2, 1, 2, 12); AIC=1837.279, BIC=1864.934, Fit time=3.736 seconds\n",
      "Fit ARIMA: order=(1, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=1771.105, BIC=1806.661, Fit time=9.538 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(2, 1, 2, 12); AIC=1854.602, BIC=1878.306, Fit time=3.148 seconds\n",
      "Fit ARIMA: order=(2, 1, 2) seasonal_order=(2, 1, 2, 12); AIC=1773.399, BIC=1812.906, Fit time=11.253 seconds\n",
      "Total fit time: 125.119 seconds\n",
      "1770.2288399625086\n"
     ]
    }
   ],
   "source": [
    "from pyramid.arima import auto_arima\n",
    "stepwise_model = auto_arima(data, start_p=1\n",
    "                            , start_q=1,\n",
    "                           max_p=3, \n",
    "                            max_q=3,\n",
    "                            m=12,\n",
    "                           start_P=0,\n",
    "                            seasonal=True,\n",
    "                           d=1, D=1, trace=True,\n",
    "                           error_action='ignore',  \n",
    "                           suppress_warnings=True, \n",
    "                           stepwise=True)\n",
    "print(stepwise_model.aic())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyramid-arima\n",
      "Requirement already satisfied: pandas>=0.19 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (0.24.2)\n",
      "Requirement already satisfied: statsmodels>=0.9.0 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (0.10.1)\n",
      "Requirement already satisfied: Cython>=0.23 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (0.29.12)\n",
      "Requirement already satisfied: numpy>=1.10 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (1.16.3)\n",
      "Requirement already satisfied: scikit-learn>=0.17 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (0.21.1)\n",
      "Requirement already satisfied: scipy>=0.9 in /anaconda3/lib/python3.6/site-packages (from pyramid-arima) (1.3.0)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /anaconda3/lib/python3.6/site-packages (from pandas>=0.19->pyramid-arima) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in /anaconda3/lib/python3.6/site-packages (from pandas>=0.19->pyramid-arima) (2019.1)\n",
      "Requirement already satisfied: patsy>=0.4.0 in /anaconda3/lib/python3.6/site-packages (from statsmodels>=0.9.0->pyramid-arima) (0.5.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /anaconda3/lib/python3.6/site-packages (from scikit-learn>=0.17->pyramid-arima) (0.13.2)\n",
      "Requirement already satisfied: six>=1.5 in /anaconda3/lib/python3.6/site-packages (from python-dateutil>=2.5.0->pandas>=0.19->pyramid-arima) (1.12.0)\n",
      "Installing collected packages: pyramid-arima\n",
      "Successfully installed pyramid-arima-0.8.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pyramid-arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data.loc['1985-01-01':'2016-12-01']\n",
    "test = data.loc['2017-01-01':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Energy Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>72.5052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-02-01</th>\n",
       "      <td>70.6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-03-01</th>\n",
       "      <td>62.4502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-04-01</th>\n",
       "      <td>57.4714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-05-01</th>\n",
       "      <td>55.3151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-06-01</th>\n",
       "      <td>58.0904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-07-01</th>\n",
       "      <td>62.6202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-08-01</th>\n",
       "      <td>63.2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-09-01</th>\n",
       "      <td>60.5846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-10-01</th>\n",
       "      <td>56.3154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Energy Production\n",
       "DATE                         \n",
       "1985-01-01            72.5052\n",
       "1985-02-01            70.6720\n",
       "1985-03-01            62.4502\n",
       "1985-04-01            57.4714\n",
       "1985-05-01            55.3151\n",
       "1985-06-01            58.0904\n",
       "1985-07-01            62.6202\n",
       "1985-08-01            63.2485\n",
       "1985-09-01            60.5846\n",
       "1985-10-01            56.3154"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Energy Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>114.8505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>99.4901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>101.0396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>88.3530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>92.0805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>102.1532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>112.1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>108.9312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>98.6154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>93.6137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Energy Production\n",
       "DATE                         \n",
       "2017-01-01           114.8505\n",
       "2017-02-01            99.4901\n",
       "2017-03-01           101.0396\n",
       "2017-04-01            88.3530\n",
       "2017-05-01            92.0805\n",
       "2017-06-01           102.1532\n",
       "2017-07-01           112.1538\n",
       "2017-08-01           108.9312\n",
       "2017-09-01            98.6154\n",
       "2017-10-01            93.6137"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(callback=None, disp=0, maxiter=50, method=None, order=(1, 1, 1),\n",
       "      out_of_sample_size=0, scoring='mse', scoring_args={},\n",
       "      seasonal_order=(2, 1, 2, 12), solver='lbfgs', start_params=None,\n",
       "      suppress_warnings=True, transparams=True, trend='c')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_model.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[120.95585223 109.87981733 100.38254332  90.56716717  92.17592454\n",
      " 103.28594092 112.97779707 112.23087913 100.95879342  91.92545297\n",
      "  95.7296424  111.20604258 120.32106996]\n"
     ]
    }
   ],
   "source": [
    "future_forecast = stepwise_model.predict(n_periods=13)\n",
    "# This returns an array of predictions:>>>\n",
    "print(future_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b3b9b00>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "future_forecast = pd.DataFrame(future_forecast, index = test.index, columns=['Prediction'])\n",
    "pd.concat([test,future_forecast],axis=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1651b2438>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))  \n",
    "pd.concat([data,future_forecast],axis=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}