Holt'sWinterData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv('hourly_data.csv', index_col = ['date_time'], parse_dates = ['date_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/PugCAIBG+fMBZdIg4m50rwTVJiCI6EIAdwC4nJlP5P30GICriaiWiMYAmADg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\nKAGhUCgUClv+f/BFVaaXcz/mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7dbc5e9048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = data[:-100]\n",
    "test = data[-100:]\n",
    "plt.plot(train)\n",
    "plt.plot(test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'DecomposeResult'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-da9b7b78b2f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonal_decompose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m24\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstattools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madfuller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3238\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3239\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3240\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3241\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3242\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1708\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1709\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1710\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1437\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1439\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1757\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1779\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1780\u001b[0m         \"\"\"\n\u001b[0;32m-> 1781\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1782\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    949\u001b[0m         \"\"\"\n\u001b[1;32m    950\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 951\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    952\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    655\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    658\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2007\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2008\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \"\"\"\n\u001b[0;32m--> 531\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'DecomposeResult'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7dbc4abe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sm.tsa.seasonal_decompose(train, freq=24))\n",
    "result = sm.tsa.stattools.adfuller(train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jbb7zkFrDmHUIoS2Op+k719Pibdw6vV+za5dtPwj4ewfXwi8Zl09xZgIn6ibr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\n8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9WmzDkrB5OJh8mhNyaTZEwe+WMrCw3DMIxQYgrKMAzDCCWmoNKzsdQXEEJMJh4mh96Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7dbc4bdda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "daily_data = pd.read_csv('daily_data.csv', parse_dates = ['date_time'], index_col = ['date_time'])\n",
    "hourly_data = pd.read_csv('hourly_data.csv', index_col = ['date_time'], parse_dates = ['date_time'])\n",
    "#seasonility of a wee\n",
    "X = hourly_data['X']\n",
    "decomp = seasonal_decompose(X, freq= 168)\n",
    "\n",
    "trend = decomp.trend\n",
    "seasonal = decomp.seasonal\n",
    "residual = decomp.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(X, label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "    Autocorrelation Function (ACF): It is a measure of the correlation between the the TS with a lagged version of itself. For instance at lag 5, ACF would compare series at time instant ‘t1’…’t2’ with series at instant ‘t1-5’…’t2-5’ (t1-5 and t2 being end points).\n",
    "    Partial Autocorrelation Function (PACF): This measures the correlation between the TS with a lagged version of itself but after eliminating the variations already explained by the intervening comparisons. Eg at lag 5, it will check the correlation but remove the effects already explained by lags 1 to 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VOuCLwBvAOuB5VV0jIveLSMxPpngKpKdx9uAe1ijCtEazDYZU9R1VDV5xluCU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\nZmZWHDcnMzMrjpuTmZkV5xeqhVWMqho9VwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7db43073c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "lag_acf = acf(X, nlags=20)\n",
    "lag_pacf = pacf(X, nlags=20, method='ols')\n",
    "\n",
    "#plot ACF\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(X)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(X)),linestyle='--',color='gray')\n",
    "plt.title('Autocorrelation Function')\n",
    "\n",
    "#Plot PACF:\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(X)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(X)),linestyle='--',color='gray')\n",
    "plt.title('Partial Autocorrelation Function')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "value of p = 2 - point where the line crosses the upper confidence level the first time in the PACF graph\n",
    "value of q = 6 - point where the line crosses the upper confidence level the first time in the PACF graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7db35b1ef0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PdQKwynDO6uixJAghlxNCZhBCZmzaZD7BQwYzHcWNn7AqLRGa4HY5d+3OmD+2DGYvucpX\n3+hOKdPGjR+TBLdME0FeVmxi0nHjpeOzNOHZpyrwZyzfhie/XBYzrbS0RvDCN/L+9LKTfKywmuxj\nhdV8ERVaIpFoSIT0SHwvyiyD20HUJpfun7lCqgL/UQC9ABwEYB2A+6PHzVqPqTiilD5OKR1GKR1W\nV+e4Bq81phq+/w+P0sTJHT97+Euc+8Q06fRmGrPKR7+pNV5HGa3Gqvdg6qVjYtJRIS6IXSVXJtXr\nPPXVMtzxzrzYBL7npq7Ac1PlBT6//e0k3W5F9iq2V7fmESPNrRThAEmb2S1duNfw09MDyRQpeelQ\nSmOzmAghTwB4J7q7GkAXw6mdAajFFVDErL16YeN0goKmNPDiNCHKMb3hXJmOgdVEFDMN38yko0Ig\nTSad+IfFmy/L0s178PK3K5VNLBy3g7aq2q9bjxcjfCJgukw66cKtbdztM88VUnpDCCHGUHqnAuAe\nPG8BOIcQUkwI6QGgD4DpqVzLDW4Hz1SgNDU7nHnDVHEFNJbFOZ3xenYzcwGgrCjZLVOFdIVW4OYI\nr9w/L3p6Ov7+yRLpEAYibt0yMyFsmiPchp9fEt+twE/3IGq6kdbwCSEvATgKQC0hZDWAWwEcRQg5\nCExCLQdwBQBQSucSQl4FMA9AC4CrKKW+2lcy2V6dBKcdpp4hShq+UYA7o9KgK6ImHbfCgAt6v80F\nMQ1fcraoE/wWhVz2GFpySEtsaY2YhpTIddz2svIdFS+dc00OP2lz/l0A7nJTKDekz7EskfEPfI6t\nigGajJhp5SriIpJg0nFOqaJ9xiZepSix/dfwGV5P8HJbbbfT7DNBhLIPW54p+HlvmnFL3sy0zVSD\n5SFt3eKlhi8jZ1Q0fLPQCirEgqelaUTQIxN+DLe9+1yzCoSCJGMKk1/k0kc3neRNXy5XNRRT328F\nHd94pqqG73R6uYlbpgoBkrj1C/5hcWuCscLtdPlsxqy3Fk6jH3660Bq+Ofkj8HNUQzGL36MWsEut\nYVuFhzAjZbfM6DZdgbm8Num4jauSzYRMniULnpaBwmjSTv4I/BxtsAs37MLHCxKXbnMToZH9L+Ol\nI5+3ZyYdjyZGWV4nuvVq0JbjhdtjtmE2QBvOQ5OOxpy8Efi5yu1vz8PFz8zAOmN4BhWTjtGGL5HM\nqOE7nc4XaknVpMMFv29RM/kHxeP8vZjYlG2YRRTNRz98jTla4GcJ+xRDJJid67WXDse9SSYq6KOS\n36/IgPGFVrzNNx81fLOVrczMPJr8JG8Efq5PHDEKQ7c2fJl0biaWuDXFiNEyK0v8cQrza9A23ZET\n04FZCOlwMJDz749GjvwR+JkuQIpEFGbAGjEKeeWZtsL5VoLdfWgFtuXCJNV49bLX84ps1vDdymcz\nG34ojQugaDJL/gj8HG+xiSESVNKp2fDtTimxWDHKvZcOH7RNz8Qrr7XUbLbhV0VXORNpX1mMq8b0\nwv4dK01/LzKz4efhxCuNOfkj8DNdgBRJjHEjj6oN3+6U0iLzNWpcD9oGeProtV3lkjmyWcPvUFWc\nsM/vcUVJCNcftz/G9m9vms5ssfJ0LnEIANce0xenHWwaLV3jM/kj8HNcRZn0wQKc8OAX+ODH9Tjl\n719JpzOaf+R6BtYnlYTjAv+lyw6N/e/aLVMYtPVrHlOqceityGYbfs/aioR9Hveoe7tyANYfaTOz\nWigYSNtsaAAYsF+V5TrTGn/Jm9AKuc7ni9jiL/d9tFApnWq0zEgE2LhzH9pXlSSIfkLi9t3D+9Ri\nZK92sd9cm2QEG36usS+LNfy2FYkx9wfsV4XTD+6McQM6ArC+56aDtmlc4hBAWlfY0iSSPxp+pgvg\nEaqLYKja8F+ZsQoj7p6CTxZsTDheHArE/dkFbc+twI/PtOVHKN64chROPmg/V/lZYWWvTpV0hMq9\n8qheOO+QrsrpxMHXaUu34sxhXVBdxmz7Vgp7+8ripGPhUPoWMQeAcEB7BWWK/BH4edJ+VAcKVW34\nnKWb9yTsl4SDllEnvYqWSSkwpGsNOrUpTSk/kX4+Cfx0UF4cwt2nDkJp2Hz8xAozf3ojVpPQzJ5l\nKM0avtfzJTTy5NGtzw+Jr7rEmmo8fE4kQhM+FiWhYHxGrCAUUv2Y8ok9PBiZ1x/nklBcWJ7ice/B\nb/i96FTDPoJmXjRm7G60VwysbPLmwdPS66UTJNoNNFPkjcDPFw1fdfEM1Vg6VucWh+NNQfTkSFXD\n53ZjbiLx+nUPh+L5tSlzt55spuD3gs/DKLPwlBJxGlC2eh/MNP90h0cOBLQNP1Pkj8DPdAE8IiUN\nX0ngJ+6XhIIGm7tHNnw+Azb6AYkJfI8fVrqicfoB/5byD3CxpIbv5FVjdU/MTTrp1fADJHej2+Y6\neSPw8wXVgcIEDV/hWxGhNMEGVGLU8JMGbZWKlAQPeeDXIKixeLkm+7lgbo0JfDkNf3j3moT9Y/p3\nEPK1uJ5FPPx0htMhRGv4mSJv3DILtQFFXNrwRYrDQeyNmglEoZCqBs0HGHlZvX5URo+PXNP2eXH5\nx9ppMJbTtrwYx/TvgDOGdsL4A+qTfre6D2aDw6FgIK0at7bhZ478Efi6CanZ8AVtuzgUf+mTNfzU\n3DJ5mOVYpE4fhXKutQLRPVE2AFyAAP+8YJh0vr3qytG7fQXOH9kNT365LOG3MLOxpI0ASe/1cgVK\nqe/uqvkj8Au0AdkFQ7MjyYZv0PxEO2+qg7YhYaatn48q18I3iDZ82XbsbMNP3K+tKMZjE4eZrrDG\nNPz0QbQN35QIBTxewyeJvLHhF6rAT1zTVj5dhNKEsAwhg+eE126Z4uxOr5+VMb9cW4aWF53PTSiW\n9Md36nVZ/W52mM18TaOXTqG+rA6oKGxuyR8N3yONIRQgrhYJyRRGM05ji/ykLbsaimaFlDV8QW3x\nWrsz5ufXIit+wTX1xyYOxbSlW/HGzNVS6ZxW9xIfGT/dTLCHA+nV8APpndibM6RD7OSNhu9Vi01n\nECkvMMq3a1+ZrZDOunV55ZbJEcMA+Knh9+lQYX1iFsKL3q6iGBMG10O2ITs1UxWNPd2xbfSgrTnp\nUFbyR+B7RK4t9+a2GxihiTNtCUn2mzf+5gaeziwkryrvX3M4Hj53iPl1DP+fN6Ir7jvzwJSvlzaI\nu3vtJNDFj7SdiA2leSIUc8vMrfcsX8gjk443+LbQtk+47QbapfNawxdNRG5y619fhT2NLaa/GYtH\nCMHQbjWm52UjSaYXyXROZjYVvYUQkla7epqdgnIGreEr4JXGkGsmHbeNxCxZzI3S44lXXpl0pD1Y\ncugRipq3V3XM5oHRgJ54ZYq24SvgmYafS9IC7hsJFVbONQqeJC+dFO+uuJKW+4+zleeJtz2SdOK2\nqE6KiZivcf/wPrU4pEdb4fc0TrwKpDd2T66QDg0/f0w6QvsJBoirqfw5Ju/du3KZafgWbpniXCBC\n1NwfZSNAOmElk8TDudRLSzbpyA7aytnw+bMyPq/nLzkEAND9pncN100fhKT5gjlCOhzM8lbDdzv4\nmmuDSa5NOibHrAS+eHdFE40qKS6g5fhDLo3D+GbSiT4i3jx2NTbbl8PHWyaWVbYHdu0xfTGyZzvn\nE/OEdPjh54/AFxqR2VJuMojCItuVRdcmHbFxGerpNPFKNqKjMdkN4/vh+UtGRI97+zEW88v2Z5aA\njenFPpmDSUf4vW8H+0Vi/DSxiD08WRt+3w4VeOnyQ3FApyqfSpZdpMOGnzcmHRG3tngxXSgQkApZ\nrGrm8ArXFh2aLPStYumId7IoFARg7jFjxZVH9VYtYhJWT1QUHrll0nE3XuIkMJuFNmvW433l8kOx\ncmuDVH6pUBIOYl9zvDyyXjr8Az+oUxv8uGanT6XLHvRMWwXEBiQbdVBEtFeHgwQyy8wSZCaOi9tG\nYpfKMw1fYXq/XH5y5+XUoK3jAXc0CguwmylAh/Rsh0OiJhOZy7502aEAgHOfmKZUFrG9sBW2nK/I\nT0mHIMwGtIavgtB+ZKMOiogmHRbp0VniBwjJyLR+r9wyCWwGbYV7IjsIa/WiuvfRsTLpJJJTNnyX\nfvhOVWyKCvxwkKC5lTrmLCOAR/ZyZ08XY/wXhwNSPVPjWsiFgLbhKyAKAy60VHv3RuFWWRJCdWlY\n7voZkjFeuWUCcZGQ7JaZSMYGba28dEShmUOtOsmkI3lznEw/a7bvVSqHn+1X1PCLJNfQ5c0wuaXm\nJ+moZQ69GvaIDYibdEKKwsmYz3+uGo0LRnWXSwd3H5hU8UzDN1Rc1JBFISQ7PiI7yJoqSR/7HNbw\nvUo3cD820Hn1mD4AgGEOs4/9vGPG9ZIB+QVXYovDFIa8zy4/fELIUwB+BmAjpfSA6LG2AF4B0B3A\ncgBnUUq3EfamPwjgBAANAC6klH7vbdGF8gn7XCiFAwRNCvkYhVmvugrMWL5VOu3sW8eBEGDwbR8p\nXDE1PG0j0TfMyYafamwcP4OnAbllwxfxquSnDumEET3aonNNGc4Y1jkWftnyuj7eM27SuXBUd1wz\ntk/0es7peJn8Whoz28i2mbbPABgvHLsJwBRKaR8AU6L7AHA8gD7Rv8sBPJpaMZ1J1vBZ1fbIjLga\nSMVroro0jKoSOROQVxi1gk5tSnH8AR2l0tnZC53CGWdKoMpPvPK9KJ6RbNKRS+d0HiEEnWvKAMBR\n2AM+a/hRk05lSQg15UXS6fi9yfZw5V716rPKhk8p/RyAqO6eDODZ6P/PAjjFcPw5ypgGoA0hJHnh\nTR/hWmq3dmWu0sXw6AX0C6PAJ8R8zVIzKBJ7BwTxqjoJIXlfcW948JyDovnJ5ZhLGr7rQVuvzWI+\n3jI+yK/6XPjZrRFnt2gv4cH3Du3Z1uFMhuw750QuzLTtQCldBwDRbfvo8U4AVhnOWx095htWWmib\nsiIc1KUNuksK/mQN393100VSI5EshmnwtGhap+iWqQpUFfPBu78+DCcf1CmazjLDhN2csuEnzbSV\n71F6Wg4fb9nijbsBICHaqdygLTvpyqN6o31lsfT1Uq1LRXEIyydNQM86ubUVVMcJrcjlaJlmt9y0\nNoSQywkhMwghMzZt2uT+ghZaKAEbfD1zWBepfMRJO5l6AWUx9nZV2oud54PYfsV7kqpAde2WaZHQ\nanWnXMCtOcDrKvppw+ezfI2zfWUUJH5vDuhUjem/P0b6eqm2z88WMTkkm4vbOT8iuaDhb+Cmmuh2\nY/T4agBGCdsZwFqzDCilj1NKh1FKh9XV1bkuiFXXWPUe8mdXWaw2RSFTMsZqtqxzukShT4jRLdNe\nwx/eowbnj+yGo/q5e14q76OxPmZ1O3dEF1x6eE8h/9yR+GJRZe24Xs8mVsnt6QuH45SD9pM+n2uu\nxrEhqUfksopeRbyVbUZu5/yI5IKG/xaAC6L/XwDgTcPx8wnjUAA7uOnHPyw08+hNlB8MI7j1xAF4\n46rRJrnap8sEYiORLYb9mrb2huXvV2zHn04+APtJDAaaoXKnjPUxq9ufTxuMCsWPc3aRWCmnV/7C\nUd1x/XH90LO23NtSKLTfMfu3x8SR3aXP5142RtOHzNXcmg49E/iSLdWLFd2ALJtpSwh5CcBRAGoJ\nIasB3ApgEoBXCSGXAFgJ4Mzo6e+BuWQuBnPLvMjDMluUL3G/XdQboL6aCSXZxkMpxUWje1jma3l9\nudM8J9GkI99izG34rBZJZi2hdlOXboked8DKq8bli5zNentRUC7mkogom5weYX11Ca44spfydZxQ\nvbcqjzCm4RsqK9MGXAv8FJWvK45gPUbZbNwGakwmi/zwKaXnWvw01uRcCuAqt4Vyg/hsurUrxxPn\nD8OI6EIPXtuNHQuQJpI0fOmUNKF9Gb10koKnWQpu6Yu5Tuek4WcLz1w8HA2Nrbj0uRlK6UTBlykH\nRNV7q3I6d7JJEPgy13Br0klR4+5Zx3pPsrl4tQ52tvnh5xSEAMcO6BALjeBaOMn64bvLPiUISdQI\nKRRMOgpr2lo6x6S91tkr8WvKinDMgA7K6bKlRqrPUqWXxpUSoyasElpBlVS9yPgYlmwdvfLSWRL1\nZvKTvBH4Tv7M7mOwy51nNIM4TWP3iqAQsE3JS8fOLTNor3XWV5cknG+FbLAz2TyyWcN3WzZxvC9T\nkSH91PB5lVRt3W5Nf1v3qMytT0ZVY3frpSMGIXxlxiqLM70jfwR+0qAtbPe9v36c/7v0EHx2/VH+\nXhDsI5Mc9VJyrALiYG98nVHVMXwQAAAgAElEQVQnDekPEwbIF9IMhYeRYNJRvMzhfWoVU7jHtUKh\nOGjrF36+Hq0xG75Rw3e+Yqa+71x5k22mxkHib39/DO485QCpdGJQuVxwy8wakgW8hddOGigJB1Fb\nIT9RZHTvdlLT30WSNHxQJZOOVftyWgDl04UbTY875WOVnywqz3DRncfj0f8Z6vJK6rg244rpMiTx\nVd8PN4O2qpqwW9NMqh963m5lP+Jhw4esrrJYuodQIszQ1dEyFUg24djvW5E0cVV64lXieSrTrV+4\n9FB8ddPR0udzQoKGrzbxyoRoFZzc2jbtbmSnO9wbq8EzNT988/+dKAoFPBtMk8G1SSdp0Nb+IXIP\nKa9RN+ko2PCjo5Gq7pJuBX6qzz2oqOEnxZ6STCe7kJCX5LIDcwJJwscjk45ssqSFmtMgbELB5EVX\n3A7aGpM5RcuUjV4YttTw/R1PyQyscMEAUYrumKTgOyTd3mC/GLlbVG+tmobPtqoC3+3z5rbxYd1q\nsHl3I4pCASzaID8gyt06ZS+fvH6EXEpxXYmsCp6Wa3gV4VE+WfqlUSgYEEw68uWgoJZC30lD4umc\n7o04Yzd2HRUNP8GGnx4N0Q38UqqBtJJn2tqfLy5d6BV+mjz5B1A5eJrLInFvoAH7VeHT68coR7BV\n7u24jIDn1QQxFfJX4Kd70DYD2mc4QFzH0jFb7orXwWniFdegnASwNzFG3HvppPOZcGHG132VJXnQ\nNvHBiB9fcXFyr/DzXnGlRFXgu/1gc82ZX1dVb47F4ZLuLbubC+OVO6cKeSPwnd0yXeabgh/+YxOH\n4tYTU/RosSEUDCQ3NtlGapevg+bBZyI6YaXBuH0WqiEU0qrhR7eDOlfj9pMGSqdzCsPC7+GQrm0A\nAH07yEVwVMVfk05U4CtKm1Q1fP5qqJpKuMbueka4ZLoOVfKOHV6RPwLfwS3TbeuRj8GTfOy4gR0x\nqpe8x8CU3x6JR39+sPT54qAtQOUHp2nyqrb8Hjp1NdtVsLAVTvfG0kvHpUlH9GpwTKt0dmoYyym7\nLCZgouELsokLr3EDOuLe0wfjz6cOdltE+3L4OWjLbfjp0vBDXMN3lTx2XfdKojN3nHIAbhy/f8Kx\ndLhl5tGgrbCfZMN3ma/0eeZnqly3V10FOtfIu2eGg4GYj/PSTbvR3JrYYqpLw9ixtxlFoQCaBNtv\nskUnXlArgf+LI3thePca9G5fGU1jj6UN3+WrlC4N0Q1eDUSLz8XoMXLWcLkQ3+7K4Z99nXvpKF9D\n6ew48Rm97iQoSfrHnuT1oZ3TTDy0G5Zv3qNSLE/IIw1f2Hf4AEjnm+IAjHojlz8/HGIeIau3NeDo\n+z/Djr3NKDeYPR485yC8edXoWHgJI3Yzba3qUlYUxNj+HZLOt8JyZqWKhm/4X1VDTOfcC6+8wJI1\n/NS0TbflcDxfIcHIXu0AAG3KVAdP3dU6HGLpeAwf1zZ8ybvy5eLNpullr8Nxcsn1gvwR+E42fJdf\na1mstE/VnoVKGw9HvXR27WMrCf366N741dG9Y7+XF4dwYJc2pmnNqhkT+EmT1qzKal9YLyZeGa+R\nCa8GWVwL/KSEyTOgAf/r7qdicttJA/Hp745SmowIuO+V80FbLkBV3+mYSYeIx92Vxwonc54f5I3A\nT0J4Wu4fllxCK3ujn93YcCCA1kjcvXLAftWoNLig2eVFaaJbprGYVgs62Pnum+FVryfVdOnAfZ0S\n9926yqaKn7mHgwF0dxG/3+09jXvpmP/udCvjGr6YTq480o4eGWjOeSvwkzT8DJl01DV8+QSBAOu2\nck1GxRXVVMPng7bizEHpEjFqo4O6R/Z1v4KZ22tnCq8G+JJnerNt0GcXvmx0eXX7jQuHEr10RJx6\nS/w9EOso28tybdLRg7byOA6cuBTcsm3Oyr6sPNlE4dxggNj6Zdt+PGwal1VdkmyMJh8YSoGh3Wrw\n2MRh1uWyvnTO4lYDd/rAc+Hjv4afngFVFdx66cTdMs398Fm+yS8AnyVtZcMPBQgaTa53yWE9sHjj\nbnRScLgAkp99Omz4eSPwnZBtOskLYsulTPc6mgBruEaTjso3TnTKJIZrS2syoiss5AbI3LplZjPl\nLpdZTNbyEu8gfxSpruKkWg6vz08nRdEeauxOOsxVOaZ/B2zd04jmVoo5a3ZY2/At3otQgODZi0cY\n8pd9f9JP3pp0RO1AVltwH4LBQsP3UTMLBpJj6Rixa3h23UfrCVP2g7n8ek5dUyWBnyP9ATGkQklY\n7tVyMunw9pOO2Exq+F8et3WOLWBila/QAId0bYN/XzkalSWhaHqYprfqZbm1CqRzYmDsmmm/Yppw\nCgBmhexqTyJWjcFPG36QJAbqSgoJbZPWfsWrxP2yqPZaWpTYXFxazXJGiBu5ekxvDNyvyvJ3UThV\nSsZvSerWW5gmvQlTYU0mhI8TqfZqQqKm75Dv/WcdiCuO7IkhXaILGAnnWc0rEZ+1tuGnAfFeya7L\nKiI+U/l03tjwVQgEEk06IvzSZr+z4Gk04VxCCEIBkiSELhrdHS2tEccZpKnMSvbiXD/53XH98Lvj\n+qH7Te8mHP+/Sw7Bxl37ks7v16ESm3aZWXwTSVrEPOl3krD1i2w06ahOtONwTb19ZYnp71bODfXV\npbj5+P7x40I6UaYM716DI/vW4eLDuifml0I4Fr/JG4Ev4jZkaZKGLzvYa3Genw9VXAAlWeNWN+mY\nfbiKQ0FcfXSfpOPmk9vSMfSUPexfX4nDTBbc+Pt5B+PjhRtw65tzsTM6T8KMZF9s80BcfvvhV5lM\nzrMjHcLKrYY/YVA9dje24LSDO5nnKykbnLx0ikIB0/dCGoePvR/kjUlHFGBuBXeySSc127+fvuPc\nq8ALt0y+wKGKN4hj/KICIGzhLlldFsapQzorj2eIXlfp0vDLi1TDOqfBhu/yGoEAwbkjuqI4xOrk\nVjaI7dvth8KynBl4YfJWw09ehUZWcAsHUjRTmMnPqTcfjQgFRk/6WC5zCwJCeGQRO62QvwRnzPkv\nSlqa0DzsChCiplWpfGASz3N/jWxDXMRCxG5QHUiunxjziLc/vzV8LhxlScdj8Wqg2mz95oR9i3Ru\nZ9rKj2W5S5cKeaPhi7gdfPX6q2sm3OqrS12tYSsSJEh0y3TogibC0t333gO486NH4mkUBgdVTEh2\n6VSY/vux+M9Vo1PIQZ36anNbMBCPzGhFq4XAH9G9LYBkO3OThYbvd+h0Xg+/wi+7QfUj98jPD8a0\nm8dK5Ju473aczrJn4FLxSYdyk7cavlfaZ6rPwK7N9utQiQ0mA37SecdMOlbXlrfhEwJlkw6/qQHC\nprHbDRKbJJO8ROLJ7StLsHOvtU3ca2bfOs527VEnoRSxmBf3q7G9Mbhzm6TAds0t5jZ8v7v/wQDB\nnNvGYfW2vTj+wS+SfhfdTNMhnFRt+GVFQXS0+ThzZM22TqYga9wpPunwXstbge/WDz/56y/78NRt\n+B9ee0RK61gmD9ra2xyNuPlIWEGiU2y9jjXCzjW7nnRyVBSHcMbQznjm6+XyiYT0qZhTrDT8ICGm\nUUxFDX/nPraGbToCx1WWhBEKmCsgbse2UkE9HLY7wW3VnpLXi4ifb/fauh4vTMNHNG9MOuLDSZox\nK5mPW1OQdX72v6cy+BUbtLWYamunIZl9aLhbpizEYuuYLsWbqpL8x9uPw20KK1CJpCpnrRY0t3ru\nog1/w07m2jlnzY7UCiLJii0NScc6VpXg7lMHJRxLi3BSvIjVs3ITr94snewAerxX5nABl1aIVMgb\nDT/5obr7enr91fXTmyHgMNPWTkMyC9JFoDZQxqtGVCW+A6XhIIIBgt2NLab3P71x7v25ltVtbmxp\nBQBMOm0Q3v9xPT5btAlAfBERvzmkZ9ukY9P+19ku7gd+rZCV/I5b9QwEJZIvRuOQPzF8GOxnwgv7\n2qTjHtmHmpTOhUp3zvAuuGpMb+cTPYbPtOVNSiy5jJeOiBu3TO5/75WFk3WZbV4Uyev4yT2nD1L6\nGAyor8K8dTtj+1btrDGq4Y/t3wHnjOiKL37ahIlPTsfFh/VIrcCSVJaEccWRPdGmtAj3fLAgLde0\nQvVdlD1bNhRC8iS46Pmxrf0VA3xwywIxtWw4jlTIY4GfuC9v0hHTOaecdLrzOqPd25VhuUl3ORXi\nJh2L3+1MOibHCHGn4UPyBUhKJ1AUDKCpNZJwx83ufza4ap41rIuSwBdfZqvbzE06xdHzD+9Th+WT\nJrgrpEtuPr4/IhFqK/Cz4RmIWGrqwn5ygETz/KxMOuw6zoLc6VUyKqVH9K3Dn0/zZ71iI3ks8N1p\n+LKrPclSURzCpNMG4Yi+dRiVot+9COsyxvfFOtoJb3EBFN5M3djw49d3m5IRDhI0tSZ2hbNRsADq\npp7SpIlN5un5xCs7z6B04PThl6n/X88+EIM6ma+45gfWNnxxfE/u2YnmmFggO8dxObZ1MkkZf75p\n/P6oq1RbEcwNeTNoK5JsH5NN5+2gLQCcM6Ir9vPA714kGOCDglHhKP6uKJSKw0GUFqnrAMkmfLXJ\nRhy+cIVTsXMp+FqP6EpP4ipiVkJjQDRAm9OErkwj8wROHdIZvdunz69ffnIl19Sj6SzOszTpONSe\n5+s8uBv/PV3LdxaMhi/r4uV2pq0MU357pCfZlYaDKC8OMj9820FbNRv+TeP3R0NTq3Q5kuc6SJp0\nLI4HTbrMZudmq9ZvxqtXjMTqbQ2YMn9jbAAWsBYGz198CJZu3p3WgWk3+Fk8t+ZP6ZmwBoHcSqnl\nvU7S8A3zTgDrdswFudMwu/Gy6Yp+nTcC36nbJqsV2q0glSq96lLTdo4b2AEfzt2A9645HEFC8PK3\nKxFxOdOWCkugEAJ0aVumVB63vaHvVm6zyC++zZcAbHWVxairLMagTtU4bmBH/OaVmViyaY+lwK8p\nL8LQ8mRPmUxQGg6if32l6W9+9rJe/cVI7LIJOGeFkx9+u/IibNnThIpouO9Yz1RSxY+fb1/3dTvY\nXIbdjfZ1SBD4WsNX44VvVibsO2nqVgPoZw3vIiTLHk3roXOHYHtDMzpUsdmEwaiGbyUc7f3wvS+f\nrNa3ZONu0+PHDeyY9BzNbn+WK7+mhIIBDOpcHfeoyoE6fPy7I1EWTr+IqC4NW4Y2tsPpnt55ygHo\n06ECk95ng9F8qUNZk06SG7IFuxubnYrK8jGadNLUILLbUKhAiyC9RdNE8gBj4pGRPdthzm3jMKpX\nrXCeZ0VMmeJQMCbsAdZgKY0Lb/HjFF80I1m6J81b8KSEsl46ied1a1eGObeNw7kjugJI9Dk399JR\nL+2iO4/HwjvHK6fzi2xqV1bUV5eiusw8bLKf5XerZDnZzEuKgujdvjLe9h1UfKu5D049g+ZWlu5Q\nkzkNCfkY0qfLhp83Al9kWPeahH2nGbSEmK9SlM3vJW8kfDYnr+IVR/RE7/YVCMUWc05OS2HtzukW\n3mad8jXTZipLwgkrFNlOXXdRtqJQQCki5DnDu6C2osjFlezZGJ05m40rTKngZ+ndyj5LL52owmN1\nz620a7MQ4oCz+aWhiZlyKort1xgw5pKu5uCJwCeELCeEzCGEzCKEzIgea0sImUwI+Sm6rXHKxwt6\n1JZj+aQJ6NauXCijWGb7/fjx7H0xRYHPufmE/vjvdUfG9s1m+2XSpJO0qlh0G3MJTXA1dX8dGX49\ntg+OHdAh6fj/TuiPGX841rsLReF23XU79nqed1rx8bVwHQdfMuRB0pKHFlLQciU5YStyxtAuqCkL\n43SLBVg4xvLmopfOGErpZsP+TQCmUEonEUJuiu7f6OH1TLGaoZm0WAfiniBmv8eOZ6+8jzWYlmhI\nRllbpNkxN/WMCD0LWe8E8cWkwnGn9F7aO687ti8A4O+fLEZZURC3vz0PgP89u2waG3KDn+X3+p2z\ncmrgWH0orMIiOCmBPWrLMfOWcZiz2j7+UYJJJw9s+CcDeDb6/7MATvHxWjGsHp7TIG42C3YruGZi\nFaCLwwXz0xcOx9nDumBI1zaeuMFwl9C4OyUvl4MtNWxuWuG+6pQaVvEyOc9qlSkVbj9pIP55/rDY\n/lVjeuOi0T1i+3717DrXlEbz9yX7jNGvg7k3jxvc3ntZv3fZiVjWHwj+u8P1HKphTJ8uLx2vBD4F\n8BEh5DtCyOXRYx0opesAILpt79G1bLG8yaKAj26dhFM2v5dxDd88WiaHN++6ymLcc8ZglISCglOm\nO/iHhDdWHhag1EKgc64Za74OKF98xW72MBCfoJUKF4zqjmNMTDmx61ocf/6SEfjbeUNcX/ftqw/D\n1WN64/A+da7zyAbEx9K+yv9Zok5YzbVx8oyykgHWvRjvpUK6xnS8MumMppSuJYS0BzCZECIddSn6\ngbgcALp27ZpyQaztboJJJ7pbFAxgb8R6slE2a2Lc5h0zrVg0RH5PeMMmJNkl1U0X/eOFGwEg5jPN\nA385BYGqLDFvdsGYScf+UxRWWJXLLVbPPVVBXVNehN8d1y+lPLIB4+2585QDMP6Ajhh2538zVh7A\nrv3bD9paadeOIRScyqPQTNNl0vFE4FNK10a3GwkhbwAYAWADIaSeUrqOEFIPYKNF2scBPA4Aw4YN\nc610dmlbilVb92Jw52rT38X7yQVeKEiAZrvuWeYl/ttXH5a0Ri8QF+CiS6qI2OC/XrIFALBpVyN6\nWKZy5sc1OxP29zWzD6eVyYZjtSwgrw81rJ5ldvdV11818tovRqK2wlkbzXUbu98Y35f/ObSbo1kx\nHVgJ6Iig8DgFU/MKlTakutiLW1K+DCGknBBSyf8HMA7AjwDeAnBB9LQLALyZ6rXsmHLdUbjyqF64\n/6yDTH8Xv+58Ri23B1uK+yx47wd1rkb/+qqk41wzcQo0xhu82LCXb9kT+98uRIMs/MPjFPirrCiE\n1385Cn89+0AAyT0QPzyIOMO7t43Ft7EjG557NuMwJJYRnEIk8KGfWPuymL/C4auNJV9Htjxy5wHp\nM+l48V3pAOBLQshsANMBvEsp/QDAJADHEkJ+AnBsdN83ikIB3DB+f2t7nHCYP/SQwcyRa/BuYEur\nw6AtTbS1c75dtjX2/4viDFcJaiwm5cgwtFsNKqN+yrxYQYNa7+RZAcCTheA13pAN749TGbhQ5e8D\nNx1ayYwDu7BInzwAHD9/0y42l2LKAlOjhXR5jOTMxCtK6VJK6YHRv4GU0ruix7dQSsdSSvtEt1ud\n8soEMYFv8Xs2T5AJCH74Tm6ZYl1e+251Ste/+YT+CftDurIXZHTvWrPTLeGaGY8Bf7HRW8aiVrNu\nORaTrztC6TpqZfIt67wgG++P1btaHo0AK/YARKVP5LwRXTH15qMxINq7VrVa7WuWG9MCckvDz2li\ny5bJunNmEVwjbo2ZdOwHrbweGBLzO7hrDeb/aTyO6ifnkCXa6UvCQSy4YzxuHN8vbme1KHKbsiKU\nuQjlLIu24duT7ASR+ftl9a5yJwE+Azau4TOCFk4AhBDUV8d7kaJfvtPaEfXVLAzK2cO62J4H5JCG\nn6uY2cTNyGYNX37Qlm3TUZXkhT6siZXHUK6ScBCEkJjnUbpeBJEsfuzZQRbeH6uPNBf43JuMvw+8\nZyy76A9vk7wnO3A/exnSoaoEyydNwFVHOy9/moszbbMaq0GmHQ1sYGaPRSjTTAkcGQKiW6bloK0/\nwjPVsdX4mrjJ8F6LygpcXpK9Tz07yMYPolWZrj22LxZu2IVDerBgZqJTgOx70Sq6d0rehDqDV9iI\nHm0dQy74ScEI/Gargc3oM+veztxzIxMa/h0nD8Te5lbc/Z79dIbYoK2Dhl8SDqKhqdW2Llcc2VO5\nnOMP6Ii3Z69Fz7pyPP3VcnXvmmhxzMrF88pUDysbTBQaNaz86QfuV40vbjg6ti+aZpxMnfzsaAST\n+AdCssETQnDO8C6YMLg+4xPuCsak88Pq7Qn7fCCFzwq1noXna7FMmTiyOy4/opfjefHQCvaxdMr5\ngg827frEwfupFBEAW6/32YtHoGM0ZLPZXAEZ7MqlNfzshN+fdC7F+PSFw/Gnkwda/i77zEQxLdtu\n+YeCux3z90qGSacPzriwBwpIw28juBBOHNkNo3vXoro0jDvfnW8zaJt4vGNVCdbv3OdbOVXgZXNa\npCscFZp2q3k5TZayIz6nQU1Mxk341unSFWNERCv49nChaTWJzg/G7M+cAW55c67p77K9QTGWTlBy\n1hO3+Q/uXI1De7bDmcM6S6XLJgpGwz9zKAsatn9HFuSpNBzEb8f1Q7Gjhi96I/hazASevmg4PvyN\ntethPDxyVMO3KFufaGArcSFtIzKuY1Y0Rc1lqkHN+EfWrNxdFZdb9Bpt0pGDv0+cTPXIAPl3U7SA\nOpWZfyBiwQIDAVw1prerVbkyTcFo+IEAwRtXjsZfPlyABet3oY4/rNhAjEU6oRWlcxB3jIN7Y9wP\nnx8xL9vD5w3Bt8u2omO1dQP1RsNXFPh8a/Km/uuXI/HTBvOlEDWZp6okjKcvGo4h0clJAFuwvVNN\nKUZP+jgjZeLB+5ywWpzcMR33HMthZaBgNHzOtcf0xSuXH4qh3dh6LPFAkxYmHUHAZ1KDEYn54Ufs\nG3pVSRhj+1tHhgRSFPjRF82tPdfsjravLFGewKVJL2P6tUebsviqYCN6tHU1+/nz68fgixvGSJ8/\nsme7hP3zDmFBF4sle6niWKuTDT9pkDeHpWbBaPicUDCAQwwNhlJ7l0bxax7Koqct+uGnoniUpGCL\ndW3Dj57udrBXk51894djlGaldm3HzHc8AKITT180HOt27MOY+z4FwNY2uOSwHtImlmQbvn3746FL\neE80U+NKXpA90itDWIUd4IiHs0nDjw3aehBtLJUPWcyGr/jR4L2qbJ7roFGnXUUx6irV4+NPvvZI\nzLltnON5JeEgetSW49wRbAZrOBhAr7oK6evwt+W4gazX61RWMXSJNunkMNxscMKgetPfxUebTcIp\nNmjbmtgg0w23naoP2rJtNn1ENZmjJBxEZYl8QL67ThmExXcdr3wdbqK5akxv/HTX8ahyuGZMoYo2\n02ySAaoUvMDv26ESyydNwIjoLDwnAoSgpiyM67NgEYuYH75DLB2/4SYdp7DIIvEJi7n7AmkyRyBA\nXPVMjZP6ZJSUSw/ric41pejfsSqWzit61ZXj1xKhF7yi4AW+IybPduYt43DR6O5pL4pI3A8/s4tP\nXH10b/SsK8eRfdUmlrTmgdeDJvdQfV0O61OLL288Gm3L2QC1lxr+lN8ehevGpU951ALfAauveTZ0\n65IGbf26UCQC/Pij5c99O1Ti498eleCxIcPeJrZCVq0Le69G45Z2UcGtqqnzVz6XnQy0wHegXXkR\nfn1079jqTBy7SUzpIragg98a/n33AYMGAdOne5rtUf3a48JR3XHbiQM8zTcV6nZvRbs9251P1OQs\nf/zZAJw7oiv6dpAf6AWA647thwtGdsPpB+feDFtOwbllqkIIwXXj+mHh+l0AgA5VTBvNAgXfU7dM\nW779lm2XLwdGjPAs26JQALedZB0bJRN8+/fz2T8P/zyzBdH4Rr+OlfjzaYOU01WXhXH7yQf4UKL0\noQW+JP06VuL+Mw/EMQOYKxcfaMzkMntc4MfCI/tl1CkEG/uSJUAH+8lpGk2uowW+AqcPTezKvXTZ\nobH1LjMBN+k4hUfWSNC7NzBqVKZLodH4ihb4KTCyVzvnk3wkHjzNZ5NOofD115kuQUHxxQ1jssL5\noZDI/MijxjVBK7fMLVuAnTst05XbLUP4t78BgQDQYr4CWNqgFNjrPM1ekyU0NAAVFcCbb0on6dK2\nDPtl0CRaiGiBn8NwR6Ekk05tLdDeOtLmW786zDrTX/2KCdvGRg9KmAK33w6UlQE7dmS2HBo5li8H\n9uwBbrop0yXxD0rjy17lKFrg5zCxQVuzAHA2AttpKnlyZlE8iNmTwLRp7DrffZf82zPPsO3Wrd5e\nU+MPwWivsbUVePppYNEi5zSbNwO7DSGwr7wSuOcef8rnBf/8J6vn2rVsv6UFWLMms2VSRAv8bIYQ\n4De/sfxZdk1bEcvFTowC3fi/1eDAnDnAL34R13ooZS+8LG+/zbYffCCfRpN9bNoU7262tgIXXwwM\nGeKcrq4OOPBAYMMG1pN79FG5HsLSpcDChe7Le999tj3gJP75T6CoiH3IAGDxYra98Uagc2dW/xyh\ncAT+Rx8xH/LvvwcmTmQNc9my+O+UJmob2cKDDzItYnvyZKCAuOKVk1vmUUcBo0ZZx77fsEGtbBMm\nAI89BqxaxfZvvRUIheTNQV73GNLB1KnAjBn+5b97N7BunX/5e822bUx4/ulPbJ9/8Bsa5NIvXQp0\n7Aj0MqzhfMklTMha0asXsP/+wOmnsw/GjBlqSsP118sJ6alTgWOPZec3N7OxMSPvvMO227bZ59Pa\nCnz8MWvvX30lX04fKByBf8EFbALREUcA//d/wG9/C/TsGX95H3sMqKxkDVBk1y72Ve/QgT1kQoAf\nfmC/7dsHXHONqUD2jM6dgX7J8TZiGn6rg+DcupWV77PPgKlTrQNGzZsX/99MGIvH+P6cOczm/sQT\nbN/pZdq2DXjggXj6vXvZ/d+7F3jkEX/tpM3NLP99++LCSeXDM2oUMHy4++uff7699jtqFLCf+oLy\nGaG5Od7uX3yRbZua4r8TAjz8sFxeRmH61FPAZZcln/PvfwOvvJK4/8MP7HkcbxM1c+lSpujNnw+8\n9FL8+OWXx3smZkycCPz3v/E6Njez7Z49wDffxNuN2ANubWWyZsYMoE0b1msZOxY45RTgsMOAt96y\nvqbfUEqz5m/o0KE0ZX7zG0qB5OP19ex4ZSXbDhzItnfcQemZZ1J65JFs//33E9NNnsyOH3YY24ZC\nbHvLLez3xx5j+7/6lXWZdu2iNBJRrwtrUvE/zquvUjpzJt3e0ES73fgOPf2Rr2i3G9+h89buSEzH\nOeCApHwemLyIdrvxnfuQGGoAAB0HSURBVMRzH3oovr9rVzz92WezY88/n1i+Tp3Y8W7d2LakhG2X\nLbOv18SJ7LwRIxLL1a8f2774YjzPpUvV75sdQPz6J57IjrW2Wt/rjz6idNGixPRm7cuJ005LrK9d\n+QBK16+ndMYM9eukyp49lF57LXv+H3zA7o0Zq1axct58M9sGg2zbtm3ifaypsb6WeM+tnoHb8zlV\nVe7S9eyZeF7XrontvbSUbX/6KTHdffex4/vtlyhzysrY9r77KF2wwPq6LgAwg0rI2PzT8B94gG1F\nLTEei5dtucvfH/8IvPYa036BZG2PH//mG7bl7orLlrG8Xn6Z7Rs1GyOrV7Oew0MPsXNlewJmWu7z\nzwOzZgFnnQUMGRL3w6cOfvhi4LMvv8Q1WIE2ZcLgrVHDN/LFF2x7++2Jx/m9WrGCbbkG5KSh79nD\ntuKALLfLTp8ez9OqUq+/Lu/Bs28fy+epp9j+88+z7dtvs0G4SZOs044bB/Ttm3z8nnvsBxgvvpj1\nKufNA2bPZtqoSiyiwYOBYcOAuXOBP/whfeavRx4B/vpX4NBDgfHjmUlx+fLk6y9fzrb8nnLE90Bl\nTMcvbFyUAbB3ktvl7eB14W2TyxCxjc6Zw7Z8cFe8d88/z0xS3CSURvJP4HNEOzJ/KDFfxpbEfY74\ncPjv/DjPh5uCPvmEba0aNm8cd9wBnHsu6yZymprMX+SLLzbv9gnmAL5wiOXizaeeCgwdmnz88MOB\nY49NDgDHGyiQWC5uU168GNi40fx8IH4PnF5y8Z6K8I+2SFMTE36TJwNnnMH+J4TZWu3gNtZrr03+\nLRIBfv97+/QAMwMan8lNN9kPMD79NPDcc8DAgcBBBznnL8Lv89FHA3fd5a/JEADWrwfGjImP43DT\n5nPPAT16xAcsOcXRCKdc6PGPfCYF/t13M3u7KsOGAX36JB8X26dVXUSBLyqXHH6PZs+Ob6dNS+s9\nyj+Bz2+ylcDn8JvvFPWSp+MPn7ufiflbabX8fK7VLl8OvP8+e8GKi9lAp8jTTzNh7UBJOIjq0jDm\nrmUaTNKg7X/+w2yXFiStQWucbGWsj7Hhy8SbWbSIfbT27bM/T1VrffFFJvyuuort/+1vbPvgg/bp\nQtEJ5bIDiWZMnAicfHLy8ZtusvWksoRKejSlaz7E3/4GfPppsmCfO5dteS+PwwU+v6f8WWZS4P/+\n98wDR5UlS8yPpyrww+HEfHgPmP/+5ZfAyJGsTaeJ/BP4XICLDY8f54LMSuCLD9lK4IvnOQl8ns/K\nlcAJJzAPFwB4/HH76zuQZJZRICmut7EOd97pOl/88Y9McHjdZeWCW/SWCNrMHAbiL6ofs4fvucf5\ng2PG+PHx+tjB28PSpUqzWJURyyL2aMV7x393uqe5PFHJa4HP0/P2yj80/KOaBvJH4D/0ELvR/Kby\nrymHPwTeAHlDdRIWosC3MkeYNWxjbBax58Ht6mIjUhT4F4/ukXQJWcLix85YlvvuA265xT4DK4HF\n3VtVNXin3kNpdBq++DGXFfjZxEcfyZ3H7+GwYczLwy9bvngP+fvBn7Eo2GU/nrkk8Lt2TXyJZAW+\nlZJo9ULye20MHcLdNn0mfwT+H/+YuG/VIEVbo18a/qxZwOjRcfuwqGXyD5JYThXh9NBDGDzvG/nz\nBZI0fPHad9xhn4FVg+Z1svrdqmEXO6x8VVLCtqKZw8ws949/MK349ts9X7jFlNde82cCmWgWW7yY\nPRevhYP48RYVIrft1Erg+/khWLQo7jatAp9PwpEV+OvXJ7oZi+OFYj6iwH/1Vea2+Y9/qJdZkfwR\n+KIQsNLwRU8SWRs+hz8sscGK+5s3sy0XNvwDI/p+i41I5UW45hoMuewcBCKtWH7Pz9Dht7+STwuT\nVbtUX0Irge6lRj1pUlzocaEkllPUTnfvBn75SzbgedttwGmneVceK846y94X3AonwS2242OOYT0v\ns/kiZqxdy2aEOj0Tq94aPy6WQ0XDnzcvefzEz+B8/fqxCVmpIivwR41iDgH8I+Ok4fN7Kk70TINi\nkj8CX8RK4POGxoWGKCysNHwOP99scOr119l1J09mEzaA+IfI6sVOReBHOXXupwCA6heeBf7yF+l0\nSYO2qoLaScPnTJ4MdOniPGhqlt9jjwH33sv+N07fNyI+Qz5AnsqUd0KYa6LfWLnzWrFyJds6mbG2\nbWPxiC69lN2/L76wd0+0yi9VDR9gnkpnnJF4TEbgv/hixmemJuBUZydHEY6V44eMa2iK5I/AFwWq\nlcAXNWuzWXJce2puthb44sN65x3WqP/0J+a7zX20nbwsWluZBsTL5ULg1zYYpnbfcIN0uhHzpyWX\nRQY+aCpr0rn0UjYfwUmDscpv0SLmbmclJIzCasmSuH9/Kl45QHzuhQoPPQRcfbX8+QsX2npSWSK2\nb5FrrwUuuijuNvz440B1tXUMGq51ynqzqWro77+vnv7nP2czUykF3n1X7XoAm0nrxoNK9DziOL0f\nU6cyF1zRP1+8p1ahGAYPViunC/J3ARQrgc8xBvwy8sYbzCVy/Hhmky0vN89f1My4Vsk1MI5Tl72x\nkWlAo0axiUAuhEy7Pe5CCP/+EcFnWWzQRUXmCadOjdfXDDEffk9uv535enPhIvtCvfAC21rFmOH5\n7NrFVq7KJNdco3Y+Nz289JKzEDfC299PP7EPxtlnJ/7OTYrcHMYD1T3+OPD//h9w3XXMbnzCCew4\nfybiMzCOdc2Zwz4g11zDPmyq1NWxnu+BB6p9MMaOjX+4VOChPlTh77wY4sJJ4POPC/ezd/KiCAYT\n8/RwvWhLZKbjpvIHYDyAhQAWA7jJ7tyUQiuEw4nToHnohJoatu3dO/H3oiK2FaddE2I9Bds4dTwQ\nMP99/Hj79D787SoqTT2fFSvMj7N522p/1dVs+/rraul5+AurPzFEhFjOlSvTfu8z9vfLXybur1/P\nwoJs3Ejpjh2UHn20+TMR/959l927Rx5h+zx0CP/j71WfPt6V/cwzM3//ZP7Ky1NLz2UFD6ng9PfE\nE67FHyRDK/iq4RNCggD+DuBYAKsBfEsIeYtSajGHPwUoTdznvq28+yTax6w0fHFfhJsLrEwvGQj1\nW9HkwcpQPXuaHxdn08rAQx6sX8+2nTrJxQ13ihIphoiwum4h8OijifsdO9qfb7V62IQJrM1zjVvU\nvHmv46ef1MtoxWuveZeXn9j1YlVwkikc1fEcF/htwx8BYDGldCmltAnAywBMpiumSEODuk2Rn79r\nl+fFyUmsuqtPPsliAbnhqquAzz93572iSlUVMGiQ/9fJVeyECSGpxZfXmMOVQ9mlOlVMei7x24bf\nCYDRuXU1gEM8v4qT5qdxj9PkKyeOPNKbcjihP9yp8fe/Z7oEmjzQ8M1GLRL6N4SQywkhMwghMza5\ndaPr3Rs480x3aXMdPqDpF0cf7S4+CeeFF5i7qt84mTQ09uSKmSWfyQOBvxpAF8N+ZwAJRmFK6eOU\n0mGU0mF1dXXurtK2rTcTLdINDxXglosuAs47j4Wz9Yvnn2eTitxy3nnpmfi0YoVzGFwzcmWxEb8R\n/eS95MQT/cs7n0iDScdvgf8tgD6EkB6EkCIA5wDwZ7kXmUBUfvPhh2rnc9/mu+82/72iwj49n5Si\nGkRHFb/z94JwWG2swcrd1i1ulRUgLhCXLmXxcmSorwdmzmRulc89x3pRfG2G/v3t/c9V1nP1gvHj\nk49FIvH5Cp06Jf6WDe9yJsh1DZ9S2gLgagAfApgP4FVKqT+h4cxelMmT2YSoceN8uWQSnTuzhybG\n5BARJ2QYX8BXX41P2hIbgOgXzxehsIrW54RTrByOl3FPPvnEH42f15n7m8ue71UYiPJy5qHlJkLo\nyy8z3+0ePeTNX0VFbJLPu++y0M2nncZ88efMYRP57Hp9a9YAV17JAnYBLGiYDDzWi8z6AUA8WqSZ\nACck/tEtK0v8jTtUtGvH1rcVF1lJB1demZ7rGBWPY4/1/3oyvpvp+kvJD7+lJdmvlfPUU+78aK+/\nXu38JUvY9XbssD+P+zpXVLDts88mlvnee9n/xcVsW1tr7s/brx87/+GHE4/zpQad/qZMcT5nzRq2\nzKB4fNQouWtwrPad/rp3T6xr+/b212lsNP+dL2vI/7iPtVV+qn/duqnXTSy7Svo+fZzfCTHNxx+z\ntiV7vll6SindulWujLy9Pvpo/NiRR1K6YQPL59Zb2bH+/a3Tcz7+mNKxY9Xv7XvvuZsb09BA6b/+\nJX/+P/7BtmefTekpp8inmznTuh0ogIJb4tAutsiFFwILFqjnee+97FHIasw8mmNVlb1tmGvMXLsM\nh5n2JE4/51CauM81p86d2Vac9u4UdRJg8xT69088ZlbP8vJEDZ93z81Wj/ID1XGOoiJzf/6aGvPz\nxXvL4Saatm0TjzsF20sHBx7oLrLimDHuVoTi7YK375oa1n4OPtj8PP4uGmdU33ADeyc+/TTeo+X5\nWfUgjaExxoxhgfBkufhi4F//Yi7BKo4NF13EFiAvLQVOP10+Ha9DmzZstr4sVrPZfSILWm8aIIRF\n0JNl5MjEfSuhIGIUTmvWxAWyiBiTPxwGrrjC3NZpvL4YdpVjJfBFYWVkwADr6xiprk58Id9/n513\nxhlq5hDx4yILv4e/+511GUXMPHas1ji2EjaHH862F1/Mtvwjy4VUJpk1i3lPOVFfz7a9eqV2PT42\nYlQkBgyI3xOOaKo0xq+6557kyXf8XortaPhw83KI17OCEDZ/hAtsGQWI89RTbO0BVQ47jG1VHRxU\nyuYBhSHwVfn6azW7NRdK4qDh+efbp2vXjm1raxOPi5q2lZATNSoO1xqseiY83oxTz4X7ZlsJdhVt\nd+ZMd77ylZWs/pdean3O//5v8jHxfH4PZUNCd+nC0owZw/a5B4WokVmtj5ANLFjAZkrPnOluxrSI\nKJz4veOKhVWwMKt7wxUkHmCQC3R+XLTtywp88XrpEKqDBrHrynyIjcjWySO0wLdCxTPlzTdZF1cc\nnLrzTha8ik8+qqpiW+6V8NBDLPyvODlJFE5OHx8rDV9s+LW1LDgbD+5kx/Dh8YErp1C8MhQXO3sd\nycCfC7/X8+aZrwn6xBOJ9Rd7SXzf6kPAj4truYrnmw1InnFGXLt2QlVAqFBVxcpRWSlfHjN4ncV2\nwNslb2+qC97wjycP8MbzDwTYvABxEROej6qZL5u9ftJs0sniO5EGPvuMeTWohLM1EgiwRt+uHdCt\nW/LvhLCXQRQmN90EbNzIPCvshKkonKx+F1+0Bx8Efvaz5HSBgHxEPmOeffrIpTFy2WWpfSjuvttc\nc+eEQswkJnsNo6A29liMwszsw8o/0px+/ViYZ36+2apGr73GtFaj+YeQ5OexdKn5so5PP83s3ccd\nJ1c3r+H3VsRK4HOByu+Fag9VXBQoEDCfF8DPa9eOhdv2kldeife4vWDgQLm1arUNP40ccQSL9+IW\n7lLl1C3jLwJ3Dx0zhsXNdxJW/AUQBdH++7Ot1exi/jvvEk+cyLZu3TcBYMoUYNky+fMffzw5wJcK\n/CPjtIiEUx1Wr2ZusmJcd9GGb/UsuNC+5hq2Di13m7zjDjb4yGchi0KNv8h8PMFMy+zRI9lsATAn\nA95WgkE2EDhwoGUVPePnP2dbMTa+k4bPt6omHQ53P3YyRQ4bxnrNjz9un58bzjqLhWEWOfNMtfE/\nzqefJvcOzdAmHQ947z1/o1aKDdOpy8iFzKWXAhs2qL+8osDv3p0FZOJ2auP1778/Xq5wmNme//a3\n+PXdcvTR7LrpwkpIiKYZJ4HfqRMbY+HjK6KA5fd2yBDz63CB3aMH85Pu3Jnd0xtuYM/SahYpISz2\n+4wZzJTl5uM3YwabQbxtG+uJvvACm6fhF08/zeLon3MO2+ft/MkngaFD2biGkb//nQlhfg/4WBR/\nJrzXaxX2gt/jsWOZknLrrYnpRQIBNgeAP0vR84p/nEePNk8/bhxb7vGll8x/N+Pll81jdS1aZL9w\nTW1tfCDXjOnTWU/QC3OpCjK+m+n6S8kPn1JKzzqL0ssus3NWTfxzOm71++LFlH7xRTyW/tat9uWa\nPp3Snj2Zf74M3A+f+zGPG8e2PAb6Kacknv/88+z4Oeewfe43b/QN37GD0kgkMd2GDdb+wSNGyJXV\nKr0TX37J4n+L6YYMYdtXXmHbM85ITFdXx4737Mm2y5fLlXP3bkrvvJPSX/2KpbvlFna/Zs5kPvnz\n51O6alW8HNddx9JFIpS+9Ralra3m+TY1Udq3L6Vvv+1chkGDKH3wQUoff5zSb7+VK3cmaGpicfVl\n2/e+fZS+8w57/wBKS6PrM/zzn5S++WZyu+M8+SQ776KL2P4HH8Tbu1P5Lrgg0U/+wQcpnTeP0oUL\nKd25MznN3r2UNjfH94H4/BaZ9uokO5zSVVayLZ+Ds3Ej+904byQFkA3x8NPOK6+klv6JJ+TMFt27\nJ7q6OWmZw4ezpffc0rMnaxIffMA0RSuvHtFcYdSSRVu0X/Tpw8YnnBg9mv1ddlni8a+/ZvZvHqZC\nvLcXXMDMKB99xDRos7ETM8rLmXb49tvAww8zbf3229lvxsWk58xhHhd8Nikh9rFgwmH50MLiIGS2\nEg6zeQiyXljFxSyu/vPPJx4nBDjpJPnrWi2naFa+Z56Jr+oFAL/+tX0a0Z121SrW87KanyHSti1b\nb2HdusSYNwsWANu3y+UBMHPu5MlxU06aNfz8EvhuueACNkVd1uTBG+RZZwH//Kd/vtmisDvuODYg\ny33DndI52U79iJGzcKE7F0U+UFhSwv6s8rjnHhayubLSnX/5iSeyOOVWL/oBB2Sni2UmkPUS43BP\noKoqZnJ0uo98nIb73YtjAU7U1jKzmhtlxmqOjBVr17L6iO+6k33/3XeZPZ9PlHvuObYwUJs2bD/N\nE/ny04ZvxRNPMBe+449PDHb1zDOAXWhmPqPw6afZhBPeIB99lDU4rwX+L3/JBu54XJgJE9iWEKbJ\nOLk3ygp8PyBErREvXco09YULExeqFm31nEDA/YIsHFmtrtDhioXZwLIZd9/N7P18Nq9o8xc5/HDm\nVvuLX7B9q2duR/v2qb1/77zD7OlOFBe7u84JJ7AZ+7xOpaUsBhKHHz/E+2VCzCgsDd/toOXHH7Mv\nfP/+TBBzQiF/Ig9WVLCPC8DMG6quW5kQ+K+/7i4QWY8e7A8wX2YxFyJ15iv33MPMXrJ+76Wl7CMR\niTCt/YgjnNMYZ2APHcq2qXjOqcKVKb/p0YPNfzFThubMkQ9glyKFJfDdUl3N/jKBjLAXBbsXJh1Z\nQXvKKUz7SUfMe016CQTktXsxnYywF6mvz19z2ocfAl9+ad47PeCAtBVDC/x8pGNHZoYym4HqNSqB\nojSaQqVDB7VgbD6hBX4+IGrjoRDw3Xfq6QDWLd+7FzjZ+7XmlchXTU+jySCFNWircYYvFXnUURkt\nhqsBPI1GY4sW+JpEsk2z1gJfo/EMLfALGTNhmi2adbZ9eDSaPEALfE12k+kPj0aTR2iBX8hwYWpc\nSDlb0Bq+RuM5WuDnAyeeyOLS8NgwbvjqK7aQC4/PrrrIhNdki2lJo8kjtFtmPlBVxSZ1qGIUpqNG\nse3hh7NZs9xbR6PR5A1aw9ckUlOTWtx8r+DhFsQ49RqNxjVaw9dkJ4cdxhaY0D0NjcYztMAvZLLd\nPq61e43GU7RJR6PRaAoELfALmWzX8DUajadoga/Rgl+jKRC0wNfoSU4aTYGgBX4hozV7jaag0AJf\no9FoCgQt8DUajaZA0AJfo9FoCgQt8DUajaZA0AJfowdvNZoCQQt8jUajKRC0wNdoNJoCISWBTwi5\njRCyhhAyK/p3guG3mwkhiwkhCwkhx6VeVI1v6IlXGk1B4EW0zL9SSu8zHiCEDABwDoCBAPYD8F9C\nSF9KaasH19NoNBqNC/wy6ZwM4GVKaSOldBmAxQBG+HQtTaroQVuNpiDwQuBfTQj5gRDyFCGkJnqs\nE4BVhnNWR48lQQi5nBAygxAyY9OmTR4UR6PRaDRmOAp8Qsh/CSE/mvydDOBRAL0AHARgHYD7eTKT\nrEwNxZTSxymlwyilw+rq6lxWQ6PRaDROONrwKaXHyGRECHkCwDvR3dUAuhh+7gxgrXLpNBqNRuMZ\nqXrp1Bt2TwXwY/T/twCcQwgpJoT0ANAHwPRUrqXRaDSa1EjVS+deQshBYOaa5QCuAABK6VxCyKsA\n5gFoAXCV9tDJQvhgbVlZZsuh0WjSQkoCn1I60ea3uwDclUr+Gp+pqADuuQc4+eRMl0Sj0aQBL/zw\nNbnMDTdkugQajSZN6NAKGo1GUyBoga/RaDQFghb4Go1GUyBoga/RaDQFghb4Go1GUyBoga/RaDQF\nghb4Go1GUyBoga/RaDQFAqFZtNoRIWQTgBWZLkeUWgCbM12IDFFodS+0+hrRdc8PulFKHcMNZ5XA\nzyYIITMopcMyXY5MUGh1L7T6GtF1L6y6a5OORqPRFAha4Gs0Gk2BoAW+NY9nugAZpNDqXmj1NaLr\nXkBoG75Go9EUCFrD12g0mgIhbwQ+IaQLIeQTQsh8QshcQsg10eNtCSGTCSE/Rbc10eP7E0KmEkIa\nCSG/M+TTjxAyy/C3kxDyG4trPkUI2UgI+VE4bnrNAqn7mdEyRAghvnhAZFl9/0IIWUAI+YEQ8gYh\npI0fdfa67tHfro3m8SMh5CVCSInFNS+I5vsTIeQCw/G7CCGrCCG7/ayz4XpZUXdCSBkh5N3oc59L\nCJnkd909g1KaF38A6gEcHP2/EsAiAAMA3AvgpujxmwDcE/2/PYDhYKty/c4izyCA9WA+rma/HwHg\nYAA/CsdNr1kgde8PoB+ATwEMK4D6jgMQiv5/T648awCdACwDUBrdfxXAhSbXawtgaXRbE/2/Jvrb\nodHy7PazztlWdwBlAMZEzykC8AWA49NxD1L9yxsNn1K6jlL6ffT/XQDmgz3YkwE8Gz3tWQCnRM/Z\nSCn9FkCzTbZjASyhlJpOBqOUfg5gq8lPptf0i2yqO6V0PqV0odu6yJBl9f2IUtoS3Z0GoLN6jeTx\nuO4hAKWEkBCYEFtrcs5xACZTSrdSSrcBmAxgfDTvaZTSdZ5VzoFsqTultIFS+kn0Gk0AvofPz90r\n8kbgGyGEdAcwBMA3ADrwRhndtlfI6hwAL7koQirXTIksqHtaybL6Xgzg/RTzkCaVulNK1wC4D8BK\nAOsA7KCUfmRyaicAqwz7q6PHMkq21D1qwjsRwBQ39Ug3eSfwCSEVAF4H8BtK6c4U8ikCcBKA17wq\nm98UWt2zqb6EkN8DaAHwgts8FK+XUt2jdu6TAfQAsB+AckLI/5idanIso6592VL3aO/gJQAPUUqX\nqpYjE+SVwCeEhMEawguU0n9HD28ghNRHf68HsFEyu+MBfE8p3RBN28UwuPcLh7Rur+maLKp7Wsim\n+kYH834G4Oc0atj1E4/qfgyAZZTSTZTSZgD/BjCKEHKIoe4ngWm1XQzpOsPc/JEWsqzujwP4iVL6\nQOo1Sw95I/AJIQTAkwDmU0r/n+GntwBwz4ILALwpmeW5MHTxKaWrKKUHRf/+4ZDW7TVdkWV1951s\nqi8hZDyAGwGcRCltkK2DWzys+0oAh0Y9TgjYGMZ8Suk3hrq/BeBDAOMIITVRzXhc9Fjayaa6E0Lu\nBFANwNSrK2vxcgQ4k38ADgPrbv0AYFb07wQA7cDsaz9Ft22j53cE+4LvBLA9+n9V9LcyAFsAVDtc\n8yUwG2BzNP0l0eOm1yyQup8a3W8EsAHAh3le38Vgdl5ejn/k0LO+HcACAD8CeB5AscU1L47WczGA\niwzH743mF4lubyuEuoNp+hRs0JiX41I/6+7Vn55pq9FoNAVC3ph0NBqNRmOPFvgajUZTIGiBr9Fo\nNAWCFvgajUZTIGiBr9FoNAWCFvgajUZTIGiBr9FoNAWCFvgajUZTIPx/Xh6WV1/xk1YAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7db361ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "data_temp = X[:-100]\n",
    "model = ARIMA(data_temp, order=(3,2,3))  #(p,d,q)\n",
    "results_AR = model.fit()  \n",
    "plt.plot(data_temp)\n",
    "plt.plot(results_AR.fittedvalues, color='red')\n",
    "# plt.title('RSS: %.4f'% sum((results_AR.fittedvalues-hourly_data)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 107.63570641]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/base/model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/base/model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/base/model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/base/model.py:488: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/home/malay/anaconda3/lib/python3.6/site-packages/statsmodels-0.8.0-py3.6-linux-x86_64.egg/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "res = results_AR.fittedvalues.values\n",
    "predicted_values = results_AR.forecast(1)\n",
    "print(predicted_values[0])\n",
    "data_temp1 = data['y']\n",
    "y_act = data_temp[-100:]\n",
    "\n",
    "predict = []\n",
    "for i in range(100):\n",
    "    model_new = ARIMA(data_temp[:-100+i], order=(3,2,3)).fit()\n",
    "    predict_new = model_new.forecast(1)\n",
    "    predict.append(predict_new[0][0])\n",
    "plt.plot(predict)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P9u0A+BusxT0d86ksLxyf5n1X9SHWrgWHA/bvp/PGdwEwGUnqXui1da2vExKZHFeN7Gft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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7daa226b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(predict)\n",
    "# print(y_act.values)\n",
    "plt.plot(y_act.values, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.42829403565952\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rms = sqrt(mean_squared_error(predict, y_act.values))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aJrwgZAGzqof2nCtkmo0gQt0LkXFMOKacSwAQxWxjzTiWmEser8MspG1UPHaP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\n9AQBFwAAgJ4g4AIAANAT/n9KV0jB2kcfgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8aac342e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = hourly_data[:-100]\n",
    "test = hourly_data[-100:]\n",
    "y_hat_avg = test.copy()\n",
    "fit1 = ExponentialSmoothing(np.asarray(train['available_spots']) ,seasonal_periods=24 ,trend='add', seasonal='add',).fit()\n",
    "y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot( train['available_spots'], label='Train')\n",
    "plt.plot(test, label='Test')\n",
    "plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "567.262673527154\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rms = sqrt(mean_squared_error(test, y_hat_avg.Holt_Winter))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EUg9nVvu4aL5dqXfn7m8Vk0yR7Y5Du1vEgXj5AMllZ2E5XmnRAaPnOtgz20rJ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\nQ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yj3exeWPruALnEvVRiS3sSYkywDArWJjhQP31lLUKU4p7rGVMdXAJQ+6TZEkg+DbjFv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oHm7fSKbUKwhcT5TJknzv3NpS65Wj8TS0bg+kG2Jccbg9ic1A8v2bOla3iPsqdPR7Ncgk7lHs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\nImE0SBYRERERCaNBsoiIiIhImP8HOl0oFXzDVAkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8aab34b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#normal avergae method\n",
    "train = hourly_data[:1000]\n",
    "test = hourly_data[1000:1100]\n",
    "# print(hourly_data.head())\n",
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['avg_forecast'] = train['available_spots'].mean()\n",
    "# print(y_hat_avg['avg_forecast'])\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(train, label='Train')\n",
    "plt.plot(test, label='Test')\n",
    "plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.739524346756916\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test, y_hat_avg.avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dih8cOoO3fvZe3Heshz5n6xA3//gkvrFf4BLYhgOnS/jtz92Lr9z/hHgbNw5E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\niSHDtjWo/vcMV/0nZ24WxPfQWBQVZlfszzr6oGqsdvGN11JRLr5Dl/hqB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1sAACAASURB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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8aac3398d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#moving average \n",
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['moving_avg_forecast'] = train['available_spots'].rolling(60).mean().iloc[-1]\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train, label='Train')\n",
    "plt.plot(test, label='Test')\n",
    "plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.01847339470101\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test, y_hat_avg.moving_avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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