time series/Time Series simplified.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style = \"whitegrid\")\n",
    "import statsmodels.api as sm\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading different version of date "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 1: Reading the file without any missing date value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
      "Data columns (total 1 columns):\n",
      "number_of_passengers    144 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.2 KB\n"
     ]
    }
   ],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')\n",
    "ts = pd.read_csv('../datasets/AirPassengers.csv', index_col='Month',date_parser=dateparse)\n",
    "ts.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 2: Reading the dataset with missing date value (1949-12 and 1954-02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
      "Data columns (total 1 columns):\n",
      "number_of_passengers    144 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.2 KB\n"
     ]
    }
   ],
   "source": [
    "ts = pd.read_csv('../datasets/AirPassengers_missing_dates.csv', index_col='Month')\n",
    "\n",
    "# Converting index to datetime\n",
    "ts.index = pd.to_datetime(ts.index, format='%Y-%m')\n",
    "ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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_of_passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaT</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_of_passengers\n",
       "Month                      \n",
       "NaT                     118\n",
       "NaT                     188"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.iloc[[11, 61],:] # NaT: Not a Timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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_of_passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number_of_passengers\n",
       "1949-12-01                   118\n",
       "1954-02-01                   188"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the index with the new date sequence\n",
    "ts.index = pd.date_range(min(ts.index), max(ts.index), freq='MS') # MS: Month starting date\n",
    "ts.iloc[[11, 61],:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Case 3: Reading the dataset with mixed dates (1949-Dec and 1954-02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.read_csv('../datasets/AirPassengers_mixed_dates.csv', index_col='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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_of_passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number_of_passengers\n",
       "1949-12-01                   118\n",
       "1954-02-01                   188"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# errors=\"coerce\" convert dates to NaT which doesn't match given format\n",
    "ts.index = pd.to_datetime(ts.index, format='%Y-%m', errors='coerce') \n",
    "ts.index = pd.date_range(min(ts.index), max(ts.index), freq='MS') # MS: Month starting date\n",
    "ts.iloc[[11, 61],:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
      "Data columns (total 1 columns):\n",
      "number_of_passengers    125 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.2 KB\n"
     ]
    }
   ],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')\n",
    "ts = pd.read_csv('../datasets/AirPassengers_missing_data.csv', index_col='Month',date_parser=dateparse)\n",
    "ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fa723a5a20>]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa71edb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nts = ts.interpolate(method=\"spline\", order=1, limit_direction='both')\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(nts.index, nts.number_of_passengers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling outliers (1950-12: 2000, 1954-08: 1, 1959-09: 6000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
      "Data columns (total 1 columns):\n",
      "number_of_passengers    144 non-null int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.2 KB\n"
     ]
    }
   ],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')\n",
    "ts = pd.read_csv('../datasets/AirPassengers_outliers.csv', index_col='Month',date_parser=dateparse)\n",
    "ts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fa7647b710>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa76483c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(ts.index, ts.number_of_passengers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2000, 6000], dtype=int64)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa77845860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the outliers\n",
    "fig, data = ts.boxplot(return_type='both')\n",
    "outliers = [flier.get_ydata() for flier in f[\"fliers\"]][0]\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing outliers with NaN and going for forward fill\n",
    "ts = ts.replace(outliers, np.nan).ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decomposition techniques\n",
    "* Additive\n",
    "* Multiplicative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')\n",
    "ts = pd.read_csv('AirPassengers.csv', index_col='Month',date_parser=dateparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa73b4fc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "res = seasonal_decompose(ts, model='additive')\n",
    "res.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa73d1e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = seasonal_decompose(ts, model='multiplicative')\n",
    "res.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series: checking stationarity using Dickey-Fuller Test\n",
    "Null Hypothesis $H_0$ = Series is not stationary  \n",
    "Alternate Hypothesis $H_{alt}$ = Series is stationary  \n",
    "If the ‘Test Statistic’ is less than the ‘Critical Value’, we can reject the null hypothesis and say that the series is stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.748092847695433,\n",
       " 0.4065192861056566,\n",
       " 0,\n",
       " 143,\n",
       " {'1%': -3.4769274060112707,\n",
       "  '10%': -2.577665408088415,\n",
       "  '5%': -2.8819726324025625},\n",
       " 1412.211518998197)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "adfuller(ts.number_of_passengers, maxlag=0) # p-value: 0.40. Hence, not-stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.631338431594319,\n",
       " 1.6091751019746681e-16,\n",
       " 0,\n",
       " 142,\n",
       " {'1%': -3.477261624048995,\n",
       "  '10%': -2.5777431104939494,\n",
       "  '5%': -2.8821181874544233},\n",
       " -234.90296075356036)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adfuller(np.diff(np.log(ts.number_of_passengers)), maxlag=0) # p-value: 1.6e-16. Stationary series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots: ACF and PACF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nts = np.diff(np.log(ts.number_of_passengers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29663dd80b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "plt.figure(figsize=(16,8))\n",
    "ax1 = plt.subplot(121)\n",
    "plot_acf(nts,lags=20, ax=ax1)\n",
    "plt.xlim(0,20)\n",
    "ax2 = plt.subplot(122)\n",
    "plot_pacf(nts,lags=20, ax=ax2)\n",
    "plt.xlim(0,20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x296637d42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seasonal differencing\n",
    "sts = pd.Series(nts)[12:] - pd.Series(nts).shift(periods=12)[12:]\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "ax1 = plt.subplot(121)\n",
    "plot_acf(sts, lags=20, ax=ax1)\n",
    "plt.xlim(0,20)\n",
    "ax2 = plt.subplot(122)\n",
    "plot_pacf(sts, lags=20, ax=ax2)\n",
    "plt.xlim(0,20)\n",
    "plt.show()\n",
    "\n",
    "# p = 1\n",
    "# q = 1\n",
    "# P = 1\n",
    "# Q = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sts1 = pd.Series(pd.Series(nts) - pd.Series(nts).shift(periods=12))\n",
    "sts1.index = ts.index[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting 0 for d and D, since we are using differenced data\n",
    "model = sm.tsa.statespace.SARIMAX(sts1.iloc[:131], \n",
    "                                  order=(1,0,1), \n",
    "                                  seasonal_order=(1,0,1, 12)).fit() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                  <td>y</td>               <th>  No. Observations:  </th>    <td>131</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 1)x(1, 0, 1, 12)</td> <th>  Log Likelihood     </th>  <td>223.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Fri, 14 Sep 2018</td>        <th>  AIC                </th> <td>-437.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>13:37:16</td>            <th>  BIC                </th> <td>-423.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>02-01-1949</td>           <th>  HQIC               </th> <td>-431.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 12-01-1959</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>    0.1921</td> <td>    0.245</td> <td>    0.785</td> <td> 0.432</td> <td>   -0.288</td> <td>    0.672</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.5368</td> <td>    0.219</td> <td>   -2.452</td> <td> 0.014</td> <td>   -0.966</td> <td>   -0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.0684</td> <td>    0.220</td> <td>   -0.312</td> <td> 0.755</td> <td>   -0.499</td> <td>    0.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.5186</td> <td>    0.230</td> <td>   -2.255</td> <td> 0.024</td> <td>   -0.969</td> <td>   -0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>    0.0013</td> <td>    0.000</td> <td>    7.818</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>           <td>nan</td> <th>  Jarque-Bera (JB):  </th> <td>2.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>nan</td> <th>  Prob(JB):          </th> <td>0.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.53</td> <th>  Skew:              </th> <td>-0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.05</td> <th>  Kurtosis:          </th> <td>3.67</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                                  y   No. Observations:                  131\n",
       "Model:             SARIMAX(1, 0, 1)x(1, 0, 1, 12)   Log Likelihood                 223.842\n",
       "Date:                            Fri, 14 Sep 2018   AIC                           -437.684\n",
       "Time:                                    13:37:16   BIC                           -423.308\n",
       "Sample:                                02-01-1949   HQIC                          -431.842\n",
       "                                     - 12-01-1959                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.1921      0.245      0.785      0.432      -0.288       0.672\n",
       "ma.L1         -0.5368      0.219     -2.452      0.014      -0.966      -0.108\n",
       "ar.S.L12      -0.0684      0.220     -0.312      0.755      -0.499       0.362\n",
       "ma.S.L12      -0.5186      0.230     -2.255      0.024      -0.969      -0.068\n",
       "sigma2         0.0013      0.000      7.818      0.000       0.001       0.002\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                         nan   Jarque-Bera (JB):                 2.26\n",
       "Prob(Q):                               nan   Prob(JB):                         0.32\n",
       "Heteroskedasticity (H):               0.53   Skew:                            -0.02\n",
       "Prob(H) (two-sided):                  0.05   Kurtosis:                         3.67\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1960-01-01    0.966480\n",
       "1960-02-01    0.998667\n",
       "1960-03-01    0.983753\n",
       "1960-04-01    0.997970\n",
       "1960-05-01    0.982166\n",
       "1960-06-01    1.031436\n",
       "1960-07-01    0.978854\n",
       "1960-08-01    0.993535\n",
       "1960-09-01    1.006876\n",
       "1960-10-01    0.998529\n",
       "1960-11-01    0.988062\n",
       "1960-12-01    0.992629\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = model.forecast(12)\n",
    "np.exp(est)"
   ]
  },
  {
   "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>number_of_passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            number_of_passengers\n",
       "Month                           \n",
       "1959-12-01                   405\n",
       "1960-01-01                   417\n",
       "1960-02-01                   391\n",
       "1960-03-01                   419\n",
       "1960-04-01                   461\n",
       "1960-05-01                   472\n",
       "1960-06-01                   535\n",
       "1960-07-01                   622\n",
       "1960-08-01                   606\n",
       "1960-09-01                   508\n",
       "1960-10-01                   461\n",
       "1960-11-01                   390\n",
       "1960-12-01                   432"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.iloc[131:]"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}