ARIMA-DEMO.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.graphics import tsaplots\n",
    "import statsmodels.api as sm  \n",
    "from statsmodels.tsa.stattools import acf  \n",
    "from statsmodels.tsa.stattools import pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 6 \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# fix random seed for reproducibility\n",
    "np.random.seed(10)\n",
    "#see current working directory\n",
    "cwd = os.getcwd()\n",
    "#change it to the current path\n",
    "os.chdir(\"C:/Users/mukul.virmani/\")\n",
    "# load the dataset\n",
    "path = './Desktop/CPI_US_Medical.csv'\n",
    "def getTansformedData(path):\n",
    "    frame = pd.read_csv(path,engine='python',skiprows=0)\n",
    "    return frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "series_id    0\n",
       "year         0\n",
       "period       0\n",
       "value        0\n",
       "Month        0\n",
       "Date         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=getTansformedData(path)  \n",
    "def num_missing(x):\n",
    "    return sum(x.isnull())\n",
    "data.apply(num_missing, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e14380f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e143bd1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Date',y='value')\n",
    "plt.show()\n",
    "plt.savefig('C:/Users/mukul.virmani/ARIMA_DEMO.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: the coerce=True keyword is deprecated, use errors='coerce' instead\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>year</th>\n",
       "      <th>period</th>\n",
       "      <th>value</th>\n",
       "      <th>Month</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M01</td>\n",
       "      <td>13.200</td>\n",
       "      <td>1</td>\n",
       "      <td>1947-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M02</td>\n",
       "      <td>13.300</td>\n",
       "      <td>2</td>\n",
       "      <td>1947-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M03</td>\n",
       "      <td>13.300</td>\n",
       "      <td>3</td>\n",
       "      <td>1947-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M04</td>\n",
       "      <td>13.400</td>\n",
       "      <td>4</td>\n",
       "      <td>1947-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M05</td>\n",
       "      <td>13.500</td>\n",
       "      <td>5</td>\n",
       "      <td>1947-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M06</td>\n",
       "      <td>13.500</td>\n",
       "      <td>6</td>\n",
       "      <td>1947-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M07</td>\n",
       "      <td>13.500</td>\n",
       "      <td>7</td>\n",
       "      <td>1947-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M08</td>\n",
       "      <td>13.600</td>\n",
       "      <td>8</td>\n",
       "      <td>1947-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M09</td>\n",
       "      <td>13.700</td>\n",
       "      <td>9</td>\n",
       "      <td>1947-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M10</td>\n",
       "      <td>13.800</td>\n",
       "      <td>10</td>\n",
       "      <td>1947-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M11</td>\n",
       "      <td>13.800</td>\n",
       "      <td>11</td>\n",
       "      <td>1947-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1947</td>\n",
       "      <td>M12</td>\n",
       "      <td>13.900</td>\n",
       "      <td>12</td>\n",
       "      <td>1947-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M01</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1948-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M02</td>\n",
       "      <td>14.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1948-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M03</td>\n",
       "      <td>14.100</td>\n",
       "      <td>3</td>\n",
       "      <td>1948-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M04</td>\n",
       "      <td>14.300</td>\n",
       "      <td>4</td>\n",
       "      <td>1948-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M05</td>\n",
       "      <td>14.300</td>\n",
       "      <td>5</td>\n",
       "      <td>1948-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M06</td>\n",
       "      <td>14.400</td>\n",
       "      <td>6</td>\n",
       "      <td>1948-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M07</td>\n",
       "      <td>14.500</td>\n",
       "      <td>7</td>\n",
       "      <td>1948-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M08</td>\n",
       "      <td>14.600</td>\n",
       "      <td>8</td>\n",
       "      <td>1948-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M09</td>\n",
       "      <td>14.500</td>\n",
       "      <td>9</td>\n",
       "      <td>1948-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M10</td>\n",
       "      <td>14.600</td>\n",
       "      <td>10</td>\n",
       "      <td>1948-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M11</td>\n",
       "      <td>14.700</td>\n",
       "      <td>11</td>\n",
       "      <td>1948-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1948</td>\n",
       "      <td>M12</td>\n",
       "      <td>14.700</td>\n",
       "      <td>12</td>\n",
       "      <td>1948-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M01</td>\n",
       "      <td>14.700</td>\n",
       "      <td>1</td>\n",
       "      <td>1949-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M02</td>\n",
       "      <td>14.800</td>\n",
       "      <td>2</td>\n",
       "      <td>1949-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M03</td>\n",
       "      <td>14.800</td>\n",
       "      <td>3</td>\n",
       "      <td>1949-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M04</td>\n",
       "      <td>14.800</td>\n",
       "      <td>4</td>\n",
       "      <td>1949-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M05</td>\n",
       "      <td>14.800</td>\n",
       "      <td>5</td>\n",
       "      <td>1949-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>1949</td>\n",
       "      <td>M06</td>\n",
       "      <td>14.800</td>\n",
       "      <td>6</td>\n",
       "      <td>1949-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M02</td>\n",
       "      <td>441.086</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M03</td>\n",
       "      <td>442.913</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M04</td>\n",
       "      <td>445.788</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M05</td>\n",
       "      <td>446.440</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M06</td>\n",
       "      <td>446.264</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M07</td>\n",
       "      <td>446.974</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M08</td>\n",
       "      <td>446.695</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M09</td>\n",
       "      <td>447.497</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M10</td>\n",
       "      <td>450.780</td>\n",
       "      <td>10</td>\n",
       "      <td>2015-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M11</td>\n",
       "      <td>452.247</td>\n",
       "      <td>11</td>\n",
       "      <td>2015-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2015</td>\n",
       "      <td>M12</td>\n",
       "      <td>452.897</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M01</td>\n",
       "      <td>454.705</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M02</td>\n",
       "      <td>456.546</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M03</td>\n",
       "      <td>457.511</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M04</td>\n",
       "      <td>459.103</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M05</td>\n",
       "      <td>460.352</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M06</td>\n",
       "      <td>462.084</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M07</td>\n",
       "      <td>464.416</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M08</td>\n",
       "      <td>468.525</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M09</td>\n",
       "      <td>469.359</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M10</td>\n",
       "      <td>470.001</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M11</td>\n",
       "      <td>470.244</td>\n",
       "      <td>11</td>\n",
       "      <td>2016-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2016</td>\n",
       "      <td>M12</td>\n",
       "      <td>471.337</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M01</td>\n",
       "      <td>472.251</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M02</td>\n",
       "      <td>472.738</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M03</td>\n",
       "      <td>473.415</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M04</td>\n",
       "      <td>472.664</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M05</td>\n",
       "      <td>472.614</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M06</td>\n",
       "      <td>474.368</td>\n",
       "      <td>6</td>\n",
       "      <td>2017-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>CUSR0000SAM</td>\n",
       "      <td>2017</td>\n",
       "      <td>M07</td>\n",
       "      <td>476.383</td>\n",
       "      <td>7</td>\n",
       "      <td>2017-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>847 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             series_id  year period    value  Month       Date\n",
       "0    CUSR0000SAM        1947    M01   13.200      1 1947-01-01\n",
       "1    CUSR0000SAM        1947    M02   13.300      2 1947-02-01\n",
       "2    CUSR0000SAM        1947    M03   13.300      3 1947-03-01\n",
       "3    CUSR0000SAM        1947    M04   13.400      4 1947-04-01\n",
       "4    CUSR0000SAM        1947    M05   13.500      5 1947-05-01\n",
       "5    CUSR0000SAM        1947    M06   13.500      6 1947-06-01\n",
       "6    CUSR0000SAM        1947    M07   13.500      7 1947-07-01\n",
       "7    CUSR0000SAM        1947    M08   13.600      8 1947-08-01\n",
       "8    CUSR0000SAM        1947    M09   13.700      9 1947-09-01\n",
       "9    CUSR0000SAM        1947    M10   13.800     10 1947-10-01\n",
       "10   CUSR0000SAM        1947    M11   13.800     11 1947-11-01\n",
       "11   CUSR0000SAM        1947    M12   13.900     12 1947-12-01\n",
       "12   CUSR0000SAM        1948    M01   14.000      1 1948-01-01\n",
       "13   CUSR0000SAM        1948    M02   14.000      2 1948-02-01\n",
       "14   CUSR0000SAM        1948    M03   14.100      3 1948-03-01\n",
       "15   CUSR0000SAM        1948    M04   14.300      4 1948-04-01\n",
       "16   CUSR0000SAM        1948    M05   14.300      5 1948-05-01\n",
       "17   CUSR0000SAM        1948    M06   14.400      6 1948-06-01\n",
       "18   CUSR0000SAM        1948    M07   14.500      7 1948-07-01\n",
       "19   CUSR0000SAM        1948    M08   14.600      8 1948-08-01\n",
       "20   CUSR0000SAM        1948    M09   14.500      9 1948-09-01\n",
       "21   CUSR0000SAM        1948    M10   14.600     10 1948-10-01\n",
       "22   CUSR0000SAM        1948    M11   14.700     11 1948-11-01\n",
       "23   CUSR0000SAM        1948    M12   14.700     12 1948-12-01\n",
       "24   CUSR0000SAM        1949    M01   14.700      1 1949-01-01\n",
       "25   CUSR0000SAM        1949    M02   14.800      2 1949-02-01\n",
       "26   CUSR0000SAM        1949    M03   14.800      3 1949-03-01\n",
       "27   CUSR0000SAM        1949    M04   14.800      4 1949-04-01\n",
       "28   CUSR0000SAM        1949    M05   14.800      5 1949-05-01\n",
       "29   CUSR0000SAM        1949    M06   14.800      6 1949-06-01\n",
       "..                 ...   ...    ...      ...    ...        ...\n",
       "817  CUSR0000SAM        2015    M02  441.086      2 2015-02-01\n",
       "818  CUSR0000SAM        2015    M03  442.913      3 2015-03-01\n",
       "819  CUSR0000SAM        2015    M04  445.788      4 2015-04-01\n",
       "820  CUSR0000SAM        2015    M05  446.440      5 2015-05-01\n",
       "821  CUSR0000SAM        2015    M06  446.264      6 2015-06-01\n",
       "822  CUSR0000SAM        2015    M07  446.974      7 2015-07-01\n",
       "823  CUSR0000SAM        2015    M08  446.695      8 2015-08-01\n",
       "824  CUSR0000SAM        2015    M09  447.497      9 2015-09-01\n",
       "825  CUSR0000SAM        2015    M10  450.780     10 2015-10-01\n",
       "826  CUSR0000SAM        2015    M11  452.247     11 2015-11-01\n",
       "827  CUSR0000SAM        2015    M12  452.897     12 2015-12-01\n",
       "828  CUSR0000SAM        2016    M01  454.705      1 2016-01-01\n",
       "829  CUSR0000SAM        2016    M02  456.546      2 2016-02-01\n",
       "830  CUSR0000SAM        2016    M03  457.511      3 2016-03-01\n",
       "831  CUSR0000SAM        2016    M04  459.103      4 2016-04-01\n",
       "832  CUSR0000SAM        2016    M05  460.352      5 2016-05-01\n",
       "833  CUSR0000SAM        2016    M06  462.084      6 2016-06-01\n",
       "834  CUSR0000SAM        2016    M07  464.416      7 2016-07-01\n",
       "835  CUSR0000SAM        2016    M08  468.525      8 2016-08-01\n",
       "836  CUSR0000SAM        2016    M09  469.359      9 2016-09-01\n",
       "837  CUSR0000SAM        2016    M10  470.001     10 2016-10-01\n",
       "838  CUSR0000SAM        2016    M11  470.244     11 2016-11-01\n",
       "839  CUSR0000SAM        2016    M12  471.337     12 2016-12-01\n",
       "840  CUSR0000SAM        2017    M01  472.251      1 2017-01-01\n",
       "841  CUSR0000SAM        2017    M02  472.738      2 2017-02-01\n",
       "842  CUSR0000SAM        2017    M03  473.415      3 2017-03-01\n",
       "843  CUSR0000SAM        2017    M04  472.664      4 2017-04-01\n",
       "844  CUSR0000SAM        2017    M05  472.614      5 2017-05-01\n",
       "845  CUSR0000SAM        2017    M06  474.368      6 2017-06-01\n",
       "846  CUSR0000SAM        2017    M07  476.383      7 2017-07-01\n",
       "\n",
       "[847 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Change_Dtypes(data):\n",
    "    data['Date']=pd.to_datetime(data['Date'],coerce=True)\n",
    "    return data\n",
    "Change_Dtypes(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test\n",
      "Test Statistic                   1.186506\n",
      "p-value                          0.995891\n",
      "#Lags Used                      21.000000\n",
      "Number of Observations Used    825.000000\n",
      "Critical Value (1%)             -3.438301\n",
      "Critical Value (5%)             -2.865050\n",
      "Critical Value (10%)            -2.568639\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def test_stationarity(timeseries):\n",
    "#     #Determing rolling statistics\n",
    "#     rolmean = pd.rolling_mean(timeseries, window=12)\n",
    "#     rolstd = pd.rolling_std(timeseries, window=12)\n",
    "\n",
    "#     #Plot rolling statistics:\n",
    "#     fig = plt.figure(figsize=(12, 8))\n",
    "#     orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "#     mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "#     std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "#     plt.legend(loc='best')\n",
    "#     plt.title('Rolling Mean & Standard Deviation')\n",
    "#     plt.show()\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print ('Results of Dickey-Fuller Test')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)\n",
    "test_stationarity(data.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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geuBVdx8MvBr2Y2bDgQnA4cBpwJ/MLD2J+USklYrFnWumz+Wj9cXcdeFYRvTKjzpSSkpa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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e1440d9c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Plot_rolling_stats(value):\n",
    "    #test_stationarity(data.value)\n",
    "    var_index = value.rolling(12).std()\n",
    "    mean_index = value.rolling(12).mean()\n",
    "    mean_index.plot()\n",
    "    plt.title(\"Rolling Mean of Index\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Index\")\n",
    "    var_index.plot()\n",
    "    plt.title(\"Rolling Std of Index\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Index\")\n",
    "Plot_rolling_stats(data.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ULZraemjvGeCi+vIx85bOKWFfRy99g0MZSFn6WEAyJkdEhg5SnZm5pO2HOmlu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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e1452882e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Plot_ACF_PACF(value):\n",
    "    lag_acf = acf(value, nlags=30)\n",
    "    lag_pacf = pacf(value, nlags=30, method='ols')\n",
    "    plt.subplot(121) \n",
    "    plt.plot(lag_acf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(value)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(value,)),linestyle='--',color='gray')\n",
    "    plt.title('Autocorrelation Function')\n",
    "    plt.subplot(122)\n",
    "    plt.plot(lag_pacf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-1.96/np.sqrt(len(value)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=1.96/np.sqrt(len(value)),linestyle='--',color='gray')\n",
    "    plt.title('Partial Autocorrelation Function')\n",
    "    plt.tight_layout()\n",
    "Plot_ACF_PACF(data.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:466: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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91JKtq9ax7Yjh9FqXxcqOPdjx/LP0O3qY32WJVMkBvxjrnHNAtYfUzrnxzrl0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e1454b6ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def build_ARIMA_model(value):\n",
    "    model = ARIMA(np.array(value), order=(2,0,0))\n",
    "    results_AR = model.fit(disp=-1)\n",
    "    plt.plot(value)\n",
    "    plt.plot(results_AR.fittedvalues, color='red')\n",
    "    plt.title('RSS: %.4f'% sum((results_AR.fittedvalues-value)**2))\n",
    "    return results_AR.fittedvalues,value\n",
    "pred,true=build_ARIMA_model(data.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 4.74 RMSE\n",
      "Train Score: 0.38 MAE\n"
     ]
    }
   ],
   "source": [
    "def Find_RMSE_MAE(x,x1,i):\n",
    "        if i==1:\n",
    "            return math.sqrt(mean_squared_error(x, x1))\n",
    "        else:\n",
    "            return mean_absolute_error(x, x1)\n",
    "# calculate root mean squared error\n",
    "trerr= Find_RMSE_MAE(true,pred,1)\n",
    "print('Train Score: %.2f RMSE' %(trerr))\n",
    "# calculate mean absloute error\n",
    "meanerrtr = Find_RMSE_MAE(true,pred,0)\n",
    "print ('Train Score: %.2f MAE' %(meanerrtr))"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}