16.ARIMAPOL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('AirPassengers.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  #Passengers\n",
       "0  1949-01          112\n",
       "1  1949-02          118\n",
       "2  1949-03          132\n",
       "3  1949-04          129\n",
       "4  1949-05          121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month          object\n",
       "#Passengers     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Month'] = pd.to_datetime(dataset['Month'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1949-01-01\n",
       "1     1949-02-01\n",
       "2     1949-03-01\n",
       "3     1949-04-01\n",
       "4     1949-05-01\n",
       "5     1949-06-01\n",
       "6     1949-07-01\n",
       "7     1949-08-01\n",
       "8     1949-09-01\n",
       "9     1949-10-01\n",
       "10    1949-11-01\n",
       "11    1949-12-01\n",
       "12    1950-01-01\n",
       "13    1950-02-01\n",
       "14    1950-03-01\n",
       "15    1950-04-01\n",
       "16    1950-05-01\n",
       "17    1950-06-01\n",
       "18    1950-07-01\n",
       "19    1950-08-01\n",
       "20    1950-09-01\n",
       "21    1950-10-01\n",
       "22    1950-11-01\n",
       "23    1950-12-01\n",
       "24    1951-01-01\n",
       "25    1951-02-01\n",
       "26    1951-03-01\n",
       "27    1951-04-01\n",
       "28    1951-05-01\n",
       "29    1951-06-01\n",
       "         ...    \n",
       "114   1958-07-01\n",
       "115   1958-08-01\n",
       "116   1958-09-01\n",
       "117   1958-10-01\n",
       "118   1958-11-01\n",
       "119   1958-12-01\n",
       "120   1959-01-01\n",
       "121   1959-02-01\n",
       "122   1959-03-01\n",
       "123   1959-04-01\n",
       "124   1959-05-01\n",
       "125   1959-06-01\n",
       "126   1959-07-01\n",
       "127   1959-08-01\n",
       "128   1959-09-01\n",
       "129   1959-10-01\n",
       "130   1959-11-01\n",
       "131   1959-12-01\n",
       "132   1960-01-01\n",
       "133   1960-02-01\n",
       "134   1960-03-01\n",
       "135   1960-04-01\n",
       "136   1960-05-01\n",
       "137   1960-06-01\n",
       "138   1960-07-01\n",
       "139   1960-08-01\n",
       "140   1960-09-01\n",
       "141   1960-10-01\n",
       "142   1960-11-01\n",
       "143   1960-12-01\n",
       "Name: Month, Length: 144, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Month'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexedDataset = dataset.set_index(['Month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
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       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>135</td>\n",
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       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>148</td>\n",
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       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-02-01</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-03-01</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-04-01</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-05-01</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-06-01</th>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          112\n",
       "1949-02-01          118\n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121\n",
       "1949-06-01          135\n",
       "1949-07-01          148\n",
       "1949-08-01          148\n",
       "1949-09-01          136\n",
       "1949-10-01          119\n",
       "1949-11-01          104\n",
       "1949-12-01          118\n",
       "1950-01-01          115\n",
       "1950-02-01          126\n",
       "1950-03-01          141\n",
       "1950-04-01          135\n",
       "1950-05-01          125\n",
       "1950-06-01          149\n",
       "1950-07-01          170\n",
       "1950-08-01          170\n",
       "1950-09-01          158\n",
       "1950-10-01          133\n",
       "1950-11-01          114\n",
       "1950-12-01          140\n",
       "1951-01-01          145\n",
       "1951-02-01          150\n",
       "1951-03-01          178\n",
       "1951-04-01          163\n",
       "1951-05-01          172\n",
       "1951-06-01          178\n",
       "...                 ...\n",
       "1958-07-01          491\n",
       "1958-08-01          505\n",
       "1958-09-01          404\n",
       "1958-10-01          359\n",
       "1958-11-01          310\n",
       "1958-12-01          337\n",
       "1959-01-01          360\n",
       "1959-02-01          342\n",
       "1959-03-01          406\n",
       "1959-04-01          396\n",
       "1959-05-01          420\n",
       "1959-06-01          472\n",
       "1959-07-01          548\n",
       "1959-08-01          559\n",
       "1959-09-01          463\n",
       "1959-10-01          407\n",
       "1959-11-01          362\n",
       "1959-12-01          405\n",
       "1960-01-01          417\n",
       "1960-02-01          391\n",
       "1960-03-01          419\n",
       "1960-04-01          461\n",
       "1960-05-01          472\n",
       "1960-06-01          535\n",
       "1960-07-01          622\n",
       "1960-08-01          606\n",
       "1960-09-01          508\n",
       "1960-10-01          461\n",
       "1960-11-01          390\n",
       "1960-12-01          432\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-03-01          132"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset['1949-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
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       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
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       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
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       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121\n",
       "1949-06-01          135"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset['1949-03':'1949-06']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          112\n",
       "1949-02-01          118\n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121\n",
       "1949-06-01          135\n",
       "1949-07-01          148\n",
       "1949-08-01          148\n",
       "1949-09-01          136\n",
       "1949-10-01          119\n",
       "1949-11-01          104\n",
       "1949-12-01          118"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexedDataset['1949']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f235a581650>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f235a5acbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Number of air passengers\")\n",
    "plt.plot(indexedDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolmean = indexedDataset.rolling(window=12).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>126.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>126.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>127.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>128.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>128.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>129.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>130.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>132.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>135.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>137.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>139.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>142.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-02-01</th>\n",
       "      <td>144.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-03-01</th>\n",
       "      <td>147.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-04-01</th>\n",
       "      <td>149.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-05-01</th>\n",
       "      <td>153.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-06-01</th>\n",
       "      <td>155.916667</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>376.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>379.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>379.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>380.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>380.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>381.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>382.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>384.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>388.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>392.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>397.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>400.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>404.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>409.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>414.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>418.333333</td>\n",
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       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>422.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>428.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>433.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>437.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>438.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>443.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>448.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>453.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>459.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>463.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>467.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>471.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>473.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>476.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "1949-06-01          NaN\n",
       "1949-07-01          NaN\n",
       "1949-08-01          NaN\n",
       "1949-09-01          NaN\n",
       "1949-10-01          NaN\n",
       "1949-11-01          NaN\n",
       "1949-12-01   126.666667\n",
       "1950-01-01   126.916667\n",
       "1950-02-01   127.583333\n",
       "1950-03-01   128.333333\n",
       "1950-04-01   128.833333\n",
       "1950-05-01   129.166667\n",
       "1950-06-01   130.333333\n",
       "1950-07-01   132.166667\n",
       "1950-08-01   134.000000\n",
       "1950-09-01   135.833333\n",
       "1950-10-01   137.000000\n",
       "1950-11-01   137.833333\n",
       "1950-12-01   139.666667\n",
       "1951-01-01   142.166667\n",
       "1951-02-01   144.166667\n",
       "1951-03-01   147.250000\n",
       "1951-04-01   149.583333\n",
       "1951-05-01   153.500000\n",
       "1951-06-01   155.916667\n",
       "...                 ...\n",
       "1958-07-01   376.333333\n",
       "1958-08-01   379.500000\n",
       "1958-09-01   379.500000\n",
       "1958-10-01   380.500000\n",
       "1958-11-01   380.916667\n",
       "1958-12-01   381.000000\n",
       "1959-01-01   382.666667\n",
       "1959-02-01   384.666667\n",
       "1959-03-01   388.333333\n",
       "1959-04-01   392.333333\n",
       "1959-05-01   397.083333\n",
       "1959-06-01   400.166667\n",
       "1959-07-01   404.916667\n",
       "1959-08-01   409.416667\n",
       "1959-09-01   414.333333\n",
       "1959-10-01   418.333333\n",
       "1959-11-01   422.666667\n",
       "1959-12-01   428.333333\n",
       "1960-01-01   433.083333\n",
       "1960-02-01   437.166667\n",
       "1960-03-01   438.250000\n",
       "1960-04-01   443.666667\n",
       "1960-05-01   448.000000\n",
       "1960-06-01   453.250000\n",
       "1960-07-01   459.416667\n",
       "1960-08-01   463.333333\n",
       "1960-09-01   467.083333\n",
       "1960-10-01   471.583333\n",
       "1960-11-01   473.916667\n",
       "1960-12-01   476.166667\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolstd = indexedDataset.rolling(window=12).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1949-06-01</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>1949-07-01</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>1949-08-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>1949-11-01</th>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>13.720147</td>\n",
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       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>13.453342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>13.166475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>13.686977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>13.822467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>13.663710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>14.760718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>18.135016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>20.797727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>21.928949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>21.315807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>20.067311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>19.070841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>17.439940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-02-01</th>\n",
       "      <td>16.781122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-03-01</th>\n",
       "      <td>19.349066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-04-01</th>\n",
       "      <td>19.425655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-05-01</th>\n",
       "      <td>18.744696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-06-01</th>\n",
       "      <td>19.942911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>59.590013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>65.557054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>65.557054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>65.106207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>64.593074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>64.530472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>63.627229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>61.759553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>61.597422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>60.284678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>60.008270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>63.009138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>71.987951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>80.049369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>81.485451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>79.680422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>74.498729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>69.830097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>66.624399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>61.866180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>61.382741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>60.171472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>60.184565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>65.021849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>77.194510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>83.630500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>84.617276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>82.541954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>79.502382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>77.737125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "1949-06-01          NaN\n",
       "1949-07-01          NaN\n",
       "1949-08-01          NaN\n",
       "1949-09-01          NaN\n",
       "1949-10-01          NaN\n",
       "1949-11-01          NaN\n",
       "1949-12-01    13.720147\n",
       "1950-01-01    13.453342\n",
       "1950-02-01    13.166475\n",
       "1950-03-01    13.686977\n",
       "1950-04-01    13.822467\n",
       "1950-05-01    13.663710\n",
       "1950-06-01    14.760718\n",
       "1950-07-01    18.135016\n",
       "1950-08-01    20.797727\n",
       "1950-09-01    21.928949\n",
       "1950-10-01    21.315807\n",
       "1950-11-01    20.067311\n",
       "1950-12-01    19.070841\n",
       "1951-01-01    17.439940\n",
       "1951-02-01    16.781122\n",
       "1951-03-01    19.349066\n",
       "1951-04-01    19.425655\n",
       "1951-05-01    18.744696\n",
       "1951-06-01    19.942911\n",
       "...                 ...\n",
       "1958-07-01    59.590013\n",
       "1958-08-01    65.557054\n",
       "1958-09-01    65.557054\n",
       "1958-10-01    65.106207\n",
       "1958-11-01    64.593074\n",
       "1958-12-01    64.530472\n",
       "1959-01-01    63.627229\n",
       "1959-02-01    61.759553\n",
       "1959-03-01    61.597422\n",
       "1959-04-01    60.284678\n",
       "1959-05-01    60.008270\n",
       "1959-06-01    63.009138\n",
       "1959-07-01    71.987951\n",
       "1959-08-01    80.049369\n",
       "1959-09-01    81.485451\n",
       "1959-10-01    79.680422\n",
       "1959-11-01    74.498729\n",
       "1959-12-01    69.830097\n",
       "1960-01-01    66.624399\n",
       "1960-02-01    61.866180\n",
       "1960-03-01    61.382741\n",
       "1960-04-01    60.171472\n",
       "1960-05-01    60.184565\n",
       "1960-06-01    65.021849\n",
       "1960-07-01    77.194510\n",
       "1960-08-01    83.630500\n",
       "1960-09-01    84.617276\n",
       "1960-10-01    82.541954\n",
       "1960-11-01    79.502382\n",
       "1960-12-01    77.737125\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolstd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(            #Passengers\n",
      "Month                  \n",
      "1949-01-01          NaN\n",
      "1949-02-01          NaN\n",
      "1949-03-01          NaN\n",
      "1949-04-01          NaN\n",
      "1949-05-01          NaN\n",
      "1949-06-01          NaN\n",
      "1949-07-01          NaN\n",
      "1949-08-01          NaN\n",
      "1949-09-01          NaN\n",
      "1949-10-01          NaN\n",
      "1949-11-01          NaN\n",
      "1949-12-01   126.666667\n",
      "1950-01-01   126.916667\n",
      "1950-02-01   127.583333\n",
      "1950-03-01   128.333333\n",
      "1950-04-01   128.833333\n",
      "1950-05-01   129.166667\n",
      "1950-06-01   130.333333\n",
      "1950-07-01   132.166667\n",
      "1950-08-01   134.000000\n",
      "1950-09-01   135.833333\n",
      "1950-10-01   137.000000\n",
      "1950-11-01   137.833333\n",
      "1950-12-01   139.666667\n",
      "1951-01-01   142.166667\n",
      "1951-02-01   144.166667\n",
      "1951-03-01   147.250000\n",
      "1951-04-01   149.583333\n",
      "1951-05-01   153.500000\n",
      "1951-06-01   155.916667\n",
      "...                 ...\n",
      "1958-07-01   376.333333\n",
      "1958-08-01   379.500000\n",
      "1958-09-01   379.500000\n",
      "1958-10-01   380.500000\n",
      "1958-11-01   380.916667\n",
      "1958-12-01   381.000000\n",
      "1959-01-01   382.666667\n",
      "1959-02-01   384.666667\n",
      "1959-03-01   388.333333\n",
      "1959-04-01   392.333333\n",
      "1959-05-01   397.083333\n",
      "1959-06-01   400.166667\n",
      "1959-07-01   404.916667\n",
      "1959-08-01   409.416667\n",
      "1959-09-01   414.333333\n",
      "1959-10-01   418.333333\n",
      "1959-11-01   422.666667\n",
      "1959-12-01   428.333333\n",
      "1960-01-01   433.083333\n",
      "1960-02-01   437.166667\n",
      "1960-03-01   438.250000\n",
      "1960-04-01   443.666667\n",
      "1960-05-01   448.000000\n",
      "1960-06-01   453.250000\n",
      "1960-07-01   459.416667\n",
      "1960-08-01   463.333333\n",
      "1960-09-01   467.083333\n",
      "1960-10-01   471.583333\n",
      "1960-11-01   473.916667\n",
      "1960-12-01   476.166667\n",
      "\n",
      "[144 rows x 1 columns],             #Passengers\n",
      "Month                  \n",
      "1949-01-01          NaN\n",
      "1949-02-01          NaN\n",
      "1949-03-01          NaN\n",
      "1949-04-01          NaN\n",
      "1949-05-01          NaN\n",
      "1949-06-01          NaN\n",
      "1949-07-01          NaN\n",
      "1949-08-01          NaN\n",
      "1949-09-01          NaN\n",
      "1949-10-01          NaN\n",
      "1949-11-01          NaN\n",
      "1949-12-01    13.720147\n",
      "1950-01-01    13.453342\n",
      "1950-02-01    13.166475\n",
      "1950-03-01    13.686977\n",
      "1950-04-01    13.822467\n",
      "1950-05-01    13.663710\n",
      "1950-06-01    14.760718\n",
      "1950-07-01    18.135016\n",
      "1950-08-01    20.797727\n",
      "1950-09-01    21.928949\n",
      "1950-10-01    21.315807\n",
      "1950-11-01    20.067311\n",
      "1950-12-01    19.070841\n",
      "1951-01-01    17.439940\n",
      "1951-02-01    16.781122\n",
      "1951-03-01    19.349066\n",
      "1951-04-01    19.425655\n",
      "1951-05-01    18.744696\n",
      "1951-06-01    19.942911\n",
      "...                 ...\n",
      "1958-07-01    59.590013\n",
      "1958-08-01    65.557054\n",
      "1958-09-01    65.557054\n",
      "1958-10-01    65.106207\n",
      "1958-11-01    64.593074\n",
      "1958-12-01    64.530472\n",
      "1959-01-01    63.627229\n",
      "1959-02-01    61.759553\n",
      "1959-03-01    61.597422\n",
      "1959-04-01    60.284678\n",
      "1959-05-01    60.008270\n",
      "1959-06-01    63.009138\n",
      "1959-07-01    71.987951\n",
      "1959-08-01    80.049369\n",
      "1959-09-01    81.485451\n",
      "1959-10-01    79.680422\n",
      "1959-11-01    74.498729\n",
      "1959-12-01    69.830097\n",
      "1960-01-01    66.624399\n",
      "1960-02-01    61.866180\n",
      "1960-03-01    61.382741\n",
      "1960-04-01    60.171472\n",
      "1960-05-01    60.184565\n",
      "1960-06-01    65.021849\n",
      "1960-07-01    77.194510\n",
      "1960-08-01    83.630500\n",
      "1960-09-01    84.617276\n",
      "1960-10-01    82.541954\n",
      "1960-11-01    79.502382\n",
      "1960-12-01    77.737125\n",
      "\n",
      "[144 rows x 1 columns])\n"
     ]
    }
   ],
   "source": [
    "print(rolmean, rolstd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f235a520c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "orig = plt.plot(indexedDataset, color='blue',label='Original')\n",
    "mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Rolling Mean & Standard Deviation')\n",
    "plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftest = adfuller(indexedDataset['#Passengers'], autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                   0.815369\n",
       "p-value                          0.991880\n",
       "#Lags Used                      13.000000\n",
       "Number of Observations Used    130.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value (%s)'%key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test Statistic                   0.815369\n",
       "p-value                          0.991880\n",
       "#Lags Used                      13.000000\n",
       "Number of Observations Used    130.000000\n",
       "Critical Value (5%)             -2.884042\n",
       "Critical Value (1%)             -3.481682\n",
       "Critical Value (10%)            -2.578770\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexedDataset_logScale = np.log(indexedDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234ee4f910>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234f0cfd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(indexedDataset_logScale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "movingAverage = indexedDataset_logScale.rolling(window=12).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>4.836178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>4.838381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>4.843848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>4.849344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>4.853133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>4.855843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>4.864066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>4.875614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>4.887163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>4.899658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>4.908927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>4.916578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>4.930824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>4.950141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-02-01</th>\n",
       "      <td>4.964670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-03-01</th>\n",
       "      <td>4.984089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-04-01</th>\n",
       "      <td>4.999795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-05-01</th>\n",
       "      <td>5.026394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-06-01</th>\n",
       "      <td>5.041214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>5.919627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>5.926146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>5.926146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>5.928980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>5.930335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>5.930582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>5.935346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>5.941409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>5.950968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>5.961736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>5.973890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>5.980693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>5.989845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>5.998311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>6.009670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>6.020128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>6.033051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>6.048368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>6.060616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>6.071774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>6.074401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>6.087066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>6.096793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>6.107234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>6.117789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>6.124517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>6.132246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>6.142628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>6.148837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>6.154215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "1949-06-01          NaN\n",
       "1949-07-01          NaN\n",
       "1949-08-01          NaN\n",
       "1949-09-01          NaN\n",
       "1949-10-01          NaN\n",
       "1949-11-01          NaN\n",
       "1949-12-01     4.836178\n",
       "1950-01-01     4.838381\n",
       "1950-02-01     4.843848\n",
       "1950-03-01     4.849344\n",
       "1950-04-01     4.853133\n",
       "1950-05-01     4.855843\n",
       "1950-06-01     4.864066\n",
       "1950-07-01     4.875614\n",
       "1950-08-01     4.887163\n",
       "1950-09-01     4.899658\n",
       "1950-10-01     4.908927\n",
       "1950-11-01     4.916578\n",
       "1950-12-01     4.930824\n",
       "1951-01-01     4.950141\n",
       "1951-02-01     4.964670\n",
       "1951-03-01     4.984089\n",
       "1951-04-01     4.999795\n",
       "1951-05-01     5.026394\n",
       "1951-06-01     5.041214\n",
       "...                 ...\n",
       "1958-07-01     5.919627\n",
       "1958-08-01     5.926146\n",
       "1958-09-01     5.926146\n",
       "1958-10-01     5.928980\n",
       "1958-11-01     5.930335\n",
       "1958-12-01     5.930582\n",
       "1959-01-01     5.935346\n",
       "1959-02-01     5.941409\n",
       "1959-03-01     5.950968\n",
       "1959-04-01     5.961736\n",
       "1959-05-01     5.973890\n",
       "1959-06-01     5.980693\n",
       "1959-07-01     5.989845\n",
       "1959-08-01     5.998311\n",
       "1959-09-01     6.009670\n",
       "1959-10-01     6.020128\n",
       "1959-11-01     6.033051\n",
       "1959-12-01     6.048368\n",
       "1960-01-01     6.060616\n",
       "1960-02-01     6.071774\n",
       "1960-03-01     6.074401\n",
       "1960-04-01     6.087066\n",
       "1960-05-01     6.096793\n",
       "1960-06-01     6.107234\n",
       "1960-07-01     6.117789\n",
       "1960-08-01     6.124517\n",
       "1960-09-01     6.132246\n",
       "1960-10-01     6.142628\n",
       "1960-11-01     6.148837\n",
       "1960-12-01     6.154215\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movingAverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "movingSTD = indexedDataset_logScale.rolling(window=12).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>0.108425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>0.106059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>0.103922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>0.107840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>0.109033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>0.107876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>0.115479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>0.135244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>0.151510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>0.159762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>0.155297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>0.142842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-12-01</th>\n",
       "      <td>0.135295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-01-01</th>\n",
       "      <td>0.122261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-02-01</th>\n",
       "      <td>0.117778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-03-01</th>\n",
       "      <td>0.133126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-04-01</th>\n",
       "      <td>0.134097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-05-01</th>\n",
       "      <td>0.128530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951-06-01</th>\n",
       "      <td>0.135756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>0.151704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>0.163550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>0.163550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>0.162389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>0.160547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>0.160358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>0.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>0.151919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>0.152112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>0.149027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>0.148984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>0.155748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>0.171962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>0.186458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>0.190767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>0.186605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>0.169796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>0.156616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>0.148299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>0.134125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>0.133032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>0.130586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>0.131129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>0.140974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>0.160607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>0.171453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>0.174172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>0.169804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-11-01</th>\n",
       "      <td>0.160923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-12-01</th>\n",
       "      <td>0.156660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "1949-06-01          NaN\n",
       "1949-07-01          NaN\n",
       "1949-08-01          NaN\n",
       "1949-09-01          NaN\n",
       "1949-10-01          NaN\n",
       "1949-11-01          NaN\n",
       "1949-12-01     0.108425\n",
       "1950-01-01     0.106059\n",
       "1950-02-01     0.103922\n",
       "1950-03-01     0.107840\n",
       "1950-04-01     0.109033\n",
       "1950-05-01     0.107876\n",
       "1950-06-01     0.115479\n",
       "1950-07-01     0.135244\n",
       "1950-08-01     0.151510\n",
       "1950-09-01     0.159762\n",
       "1950-10-01     0.155297\n",
       "1950-11-01     0.142842\n",
       "1950-12-01     0.135295\n",
       "1951-01-01     0.122261\n",
       "1951-02-01     0.117778\n",
       "1951-03-01     0.133126\n",
       "1951-04-01     0.134097\n",
       "1951-05-01     0.128530\n",
       "1951-06-01     0.135756\n",
       "...                 ...\n",
       "1958-07-01     0.151704\n",
       "1958-08-01     0.163550\n",
       "1958-09-01     0.163550\n",
       "1958-10-01     0.162389\n",
       "1958-11-01     0.160547\n",
       "1958-12-01     0.160358\n",
       "1959-01-01     0.157895\n",
       "1959-02-01     0.151919\n",
       "1959-03-01     0.152112\n",
       "1959-04-01     0.149027\n",
       "1959-05-01     0.148984\n",
       "1959-06-01     0.155748\n",
       "1959-07-01     0.171962\n",
       "1959-08-01     0.186458\n",
       "1959-09-01     0.190767\n",
       "1959-10-01     0.186605\n",
       "1959-11-01     0.169796\n",
       "1959-12-01     0.156616\n",
       "1960-01-01     0.148299\n",
       "1960-02-01     0.134125\n",
       "1960-03-01     0.133032\n",
       "1960-04-01     0.130586\n",
       "1960-05-01     0.131129\n",
       "1960-06-01     0.140974\n",
       "1960-07-01     0.160607\n",
       "1960-08-01     0.171453\n",
       "1960-09-01     0.174172\n",
       "1960-10-01     0.169804\n",
       "1960-11-01     0.160923\n",
       "1960-12-01     0.156660\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movingSTD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234ed78e10>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234edf4d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(indexedDataset_logScale)\n",
    "plt.plot(movingAverage, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetLogScaleMinusMovingAverage = indexedDataset_logScale - movingAverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234e212410>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234ea44d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(indexedDataset_logScale)\n",
    "plt.plot(movingAverage, color='red')\n",
    "plt.plot(datasetLogScaleMinusMovingAverage, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-06-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-07-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-08-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-09-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-10-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-11-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>-0.065494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "1949-06-01          NaN\n",
       "1949-07-01          NaN\n",
       "1949-08-01          NaN\n",
       "1949-09-01          NaN\n",
       "1949-10-01          NaN\n",
       "1949-11-01          NaN\n",
       "1949-12-01    -0.065494"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetLogScaleMinusMovingAverage.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-12-01</th>\n",
       "      <td>-0.065494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-01-01</th>\n",
       "      <td>-0.093449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-02-01</th>\n",
       "      <td>-0.007566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-03-01</th>\n",
       "      <td>0.099416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-04-01</th>\n",
       "      <td>0.052142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-05-01</th>\n",
       "      <td>-0.027529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-06-01</th>\n",
       "      <td>0.139881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-07-01</th>\n",
       "      <td>0.260184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-08-01</th>\n",
       "      <td>0.248635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-09-01</th>\n",
       "      <td>0.162937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-10-01</th>\n",
       "      <td>-0.018578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950-11-01</th>\n",
       "      <td>-0.180379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-12-01    -0.065494\n",
       "1950-01-01    -0.093449\n",
       "1950-02-01    -0.007566\n",
       "1950-03-01     0.099416\n",
       "1950-04-01     0.052142\n",
       "1950-05-01    -0.027529\n",
       "1950-06-01     0.139881\n",
       "1950-07-01     0.260184\n",
       "1950-08-01     0.248635\n",
       "1950-09-01     0.162937\n",
       "1950-10-01    -0.018578\n",
       "1950-11-01    -0.180379"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetLogScaleMinusMovingAverage.dropna(inplace=True)\n",
    "datasetLogScaleMinusMovingAverage.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234c98bb10>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234ea63b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(indexedDataset_logScale)\n",
    "plt.plot(movingAverage, color='red')\n",
    "plt.plot(datasetLogScaleMinusMovingAverage, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Determing rolling statistics\n",
    "    movingAverage = timeseries.rolling(window=12).mean()\n",
    "    movingSTD = timeseries.rolling(window=12).std()\n",
    "\n",
    "    #Plot rolling statistics:\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(movingAverage, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(movingSTD, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries['#Passengers'], autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234ed0df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -3.162908\n",
      "p-value                          0.022235\n",
      "#Lags Used                      13.000000\n",
      "Number of Observations Used    119.000000\n",
      "Critical Value (5%)             -2.886151\n",
      "Critical Value (1%)             -3.486535\n",
      "Critical Value (10%)            -2.579896\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(datasetLogScaleMinusMovingAverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234c8a8210>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c8c9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exponentialDecayWeightedAverage = indexedDataset_logScale.ewm(halflife=12, min_periods=0, adjust=True).mean()\n",
    "plt.plot(indexedDataset_logScale)\n",
    "plt.plot(exponentialDecayWeightedAverage, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c88d250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -3.601262\n",
      "p-value                          0.005737\n",
      "#Lags Used                      13.000000\n",
      "Number of Observations Used    130.000000\n",
      "Critical Value (5%)             -2.884042\n",
      "Critical Value (1%)             -3.481682\n",
      "Critical Value (10%)            -2.578770\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogScaleMinusMovingExponentialDecayAverage = indexedDataset_logScale - exponentialDecayWeightedAverage\n",
    "test_stationarity(datasetLogScaleMinusMovingExponentialDecayAverage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f234c7afe10>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c7f5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetLogDiffShifting = indexedDataset_logScale - indexedDataset_logScale.shift()\n",
    "plt.plot(datasetLogDiffShifting)\n",
    "plt.plot(indexedDataset_logScale,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c8fb410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -2.717131\n",
      "p-value                          0.071121\n",
      "#Lags Used                      14.000000\n",
      "Number of Observations Used    128.000000\n",
      "Critical Value (5%)             -2.884398\n",
      "Critical Value (1%)             -3.482501\n",
      "Critical Value (10%)            -2.578960\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datasetLogDiffShifting.dropna(inplace=True)\n",
    "test_stationarity(datasetLogDiffShifting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposition = seasonal_decompose(indexedDataset_logScale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c6805d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(411)\n",
    "plt.plot(indexedDataset_logScale, 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": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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urn5B5VsYtmDYeANrqNO2MEpHuQUDQER+BH502fac5XMm2opwd+xyoKvLtnSgkzfKVhHk5EDr1jooe7Nr/zAfkZgIUVFQrZr+7qvWvzWgXlkWhu2SsvEmtmCUHXuktxdIS4N9+2Dz5oo7Z0JCYQvDF1jzrWwL48wZHa+xsSkPVsGwXVKlwxYML2DedBXZAq4oC8OMX/jyHCURa3FWpqRUThlsLhxMwahSxbYwSostGF7ADKCdPFl8Om9hziP1v2BhZGRAXBzExOjvdhzDpryYglG/vi0YpcUWDC9Q0RZGcrLuGhgZWXExjODgyrEwTOuikxHRsgXDprwkJkJICNSsabukSostGF6gogXDbCFVpIURHl45rTFXwbAD3zblJSlJPztVq9oWRmmxBcMLmC6piqrMTD9+eHjFxTDCwyvHwjAD3p0763fbwrApL2aX9MBAWzBKiy0YXqCiLYzUVP0eGlpxFkbNmpXzcB04ANWr2zEMG++RmAi1a2vBsF1SpcMWDC9gtTAqYlH5tDT9HhpacTGMyrQwmjXT5wfbJWVTfkwLw3ZJlR5bMLyA2UrJzvb9zLHgEIywsIqNYWRnV4wgWomN1dZFaCj4+dkWhk35sV1SZccWDC9gNWsromttZVkYULEPWF4eHDyoBcPPT7vFbMGwKQ/p6fqeNgXDdkmVDlswvIB1ptiKcJlUZAzDDHrXrKnfK1Iwjh3TD3SzZo4y2C4pm/Jg7WFou6RKj1fmkvpfx9pKqYgKLS1Nt7iDgkAp8Pe/MC2Mgwf1u1UwbAvDpjxYBcN2SZUe28LwAhVtYaSlaevCXIMwKMi3MQx/f8dMuBUZ+DbFIcJYmzE83BYMm/LhKhgV5ZISgSNHSk53rmMLhheoaAsjNdWxDgboOIYvLYygIP1wQcW2yEzXmylWtkvKprxUlktqwQJtKZsrZZ6v2ILhBSrDJWVd+8KXFsbZs86CUZEWhjVWA7ZLyqb8VJZLav16va7M+S4YdgzDC1SWS8rEUwtj+/btTJw4kRYtWtChQwfat29PTEwMfn5FtxsyMvQ8UlWq5AIBlWph2C4pm/KSmKgti7AwLRi5uZCfr2OCvmTXLv2enu7b8/garwiGUqoP8BZ6dbwZIvKKy/5AYDZ6UaRkYLCIHFJKNQH+AvYYSdeJyCjjmE7AJ0AQenGmR81lXc81Ktsl5YmFcebMGQYNGsSxY8fIzs4mz1hYIiQkhHvvvZc333wTZQZFLGRkQGbmdIYNewpYR2ZmCy9eSfGkpek4TUiI/l6zpi5PVpbD4rGxKQ0JCXpZAKW0cIB+fs3u6b7iQhGMcuuqUsofmAb0BVoBdyilWrkkuxc4JSLNgSnAJMu+AyLSwXiNsmx/DxgJtDBefcpbVl9hCkZERMWNw7C6pEqyMESEkSNHcvDgQZYsWUJaWhobN27ko48+ol+/frz99tt8/PHHbo+Ni/uDhISHSU09CcypcAvDGtw3u/baVoZNWTl+HOrV058rKi6Xng6HDjk+n894wxC7HNgvIrEikg3MA1wXKr0ZmGV8XgD0Uu6aswZKqXpAdRFZa1gVs4EBXiirTzBdUlFRleOSsloYWVmwdq1z+unTp/PFF1/w0ksv0b17d6pVq0anTp245557mDt3Lr169eKRRx7hr7/+cjouKSmJjRtvo2rVBrRu3QWYR0ZGxRl5rpaU2bXXFgybshIXp9fBAIdg+Lqn1O7djs+2YEADwNph7KixzW0aEckFUgCjsyRNlVJ/KqVWKKV6WNIfLSFPAJRSI5VSG5VSGxOtay9WIOYNV6dO5fWSOns2i/z8fD77DK68Uk/hDPDnn38yZswY+vbty9ixYwvl5efnx+zZswkODqZ///7ceOONdO7cmQYNGlC3bl2ysxNo3/4rbrnlXmAvu3f/6fsLNEhNdbakTAvD7illU1asFobpkvK1hWG6o8AWDAB3loJrM7SoNMeBRiLSEXgc+FwpVd3DPPVGkQ9EpLOIdI6MjCxFsb2HaWFERlZsL6nMzEw6derEsmXBbNhQjW7dunHkSB4iOs2ZM2e4/fbbqV27NrNnzy4yuF2/fn0+++wz/P39OXbsGFFRUfTp04fx48cTE/MLdepcyg033AIEsHz5PN9foMt1mtguKZvykJ2tG1IV7ZK6kATDG0Hvo0BDy/doIK6INEeVUgFADeCk4W7KAhCRTUqpA8BFRvroEvI8Z8jO1q2V8HDfC0Z+vr7pQkNh+fLlbN68mSZNhnHqVBDr139A9eqzgHvIyhJGjBjBwYMHWb58ObVr1y423+uuu449e/YU2r5ggXZ51a1bC7ietWvncfLkeH766Seio6O5+uqrfXOhFLYwbJeUTXk4cUK/V7RLatcuPQYjNvb8FwxvWBgbgBZKqaZKqarAEGChS5qFwHDj863AryIiSqlII2iOUqoZOrgdKyLHgVSlVFcj1nE38J0XyuoTsrP1gvK1avl+ivOMDJ1/aCj88MMPBAcH06PHB4SFvU/Xrl1ZvfopIJWJEx9j/vz5vPzyy3Tv3r3M53Meh3EHJ08eISoqirvuuouePXsyfvx4cnNzvXV5Tri63myXlE15OH5cv5fGwhARyts5c9cuuPRS3XnDnJvtfKXcgmHEJEYDi9FdZOeLyE6l1ItKqZuMZB8BEUqp/WjX03hj+1XANqXUVnQwfJSImP2MHgBmAPuBA8BP5S2rr8jJcVgYOTm+vSkcg9mERYsW0atXL0JDq5GVpXjzzTfJzIwHujJnzluMGTPGbdyiNJjjMHS3wwG0bTuQxx57jN9//51Ro0YxadIkrrvuOtJ90HSyLQwbbxJn+Cg8jWEcP36ctm3b4u/vT3BwMOHh4dSrV4+WLVvyww8/eHTOjAxtWbRurbuHn+8WhlfGYYjIj+ixEtZtz1k+ZwK3uTnuK+CrIvLcCLTxRvl8jdUlBboFbI4d8Dbm1OZnzvzFoUOHGD9+PHv36huzS5cuRETcRXLyHAYOHMUbb7zhdmxFaXCeGiSEO+/8mvGG3Hfr1o2uXbvyr3/9i2HDhrFgwYJiBwGWFnfdhwMDbQvDpmyYFkb9+pCcnMzrrz8MNGTx4s4EBrYgKiqKqKgoAgICSElJoW/fvgXPWG5uLpmZmWRmZvL7778zYMAAPvnkE4YOHVrsOffu1W7kVq1swbAxyMnRLilTME6ehOjo4o/xhNzcXPz9/Z0qfVMw/vpLt3BuuOEG/v7bMQ4jLGwqycn9GTPm1nKLhUjJc0kNHz6cU6dO8dhjjzFhwgQmTZpUOKMy4mphgG+mB9m0aRMrV67kscce827GNucUcXF6RHdkJEyaNJ3ffpsLVOX557N5/nmdJigoiC5dupCamsrOnTtZtGgR119/vVM+Z86cYcCAAdx11134+flxxx13FHlOM+BtC4ZNAe4sDHfMmzePjh07cvHFF5eY5y+//MLAgQNJT0+nevXqNGvWjI4dOxIR0Q0YwJYtP9C+fXsaNmxIUJCe4iA3F06frgHcjjfCCtnZunUUFKQftCpV3Jvvjz76KHv37uXVV19l6tSp+Pn58fDDD/Pyyy+X+dx5edq1Z41hgG+mB3nhhRdYtGgRrVq1KlQ52Fw4HD8OdeuCUvl8+OGHdO58DRs3/siUKdtp1OhvEhIS2L17N6tWreLgwYPMnDnT7f1QvXp1fvzxR3r37s39999P165dadq0qdtz7tqlZ3tu0aJ8gpGfn8/zzz+Pn58fzZs3p379+oSFheHn50dCQgLx8fF07dqVli1blu0EHmILhhcwg97FCUZ6ejpPPPEEmZmZLFq0iG7duhWZ36FDhxg8eDDR0dEMGjSI06dPs2fPHr799luSkz8CRrFjRz7jDd+QOa1BerqjMvVGzw9zMGBwsH4PDHQ/olwpxdtvv02LFi2Ii4tj69atvPLKK9xxxx20bdu2TOe2LkNrxdsz1qanp7Ns2TIAHn/8cbZu3UpAwLn1WBw8CEXURzalIC5Oxy+WLFnCoUOHeOCBSWzcGEjjxp0ZOLCzU1oRKdZCr1atGnPmzKFdu3YMGzaM5cuXu71vdu2C5s31s1Mewdi5cycTJ04sNs0777xjC8b5gDXoDe4rtJCQEFauXEnfvn255ppr+Pzzzxk4cGChdBkZGQwaNIjc3Fy+++47WrRwzN0kIkyevIVx4+bSqtVyhg0bBjhW3TN9tGaZyospGGb+1aoVHSAMCAgocOmcPHmSZs2aMWHCBBYtWlSmcxcnGOagRG+wZMkSMjMzGT16NFOnTmXGjBmMGjWq5AMriC1boGNH2LgROnWq7NKc3xw/Dg0b6pkPoqKiuP76AYwb5/6e9sSd27hxY959913uuusuhg4dSrt27ahTpw633norNY0ufcePO9zT5RGM/fv3A7B69WoiIiKIj48nLS2N3NxcoqKiqFu3LvXMaL4PsQXDC3jqkoqJiWHNmjX079+fQYMG8fDDDzNp0iTS0tL45ptvWLZsGStXriQ+Pp7vv//eSSxA38S1a3cEOrJoETRurLebFsZRy9h4b1oYpmAUZWG4UqtWLSZMmMD48eNZuXIlPXr0IDY2luzsbKpVq0ZgYCDVqlUjNDSUqmZXFRdcZ6o1CQ8H49nxCt9++y3h4eG88cYbbN++nWeffZbhw4cTZF50JWPOQRQXZwtGeTl+HNq0iWPevO/597//TWiovvfK86zceeed/P7770yfPp358+cD2kV79913M27cOM6caVLQKyskpOyNnQMHDgDQqlUrwsPDueSSS8pe6HJgr4fhBcygd/Xquq91cS6TyMhIli9fzpgxY3jnnXeIiYmhXr163H///axbt47evXuzcOFCbrzxRrfHmy1v17mkQK+BbeINwTC7B3tiYbjyyCOP0KBBA+666y6io6Np3rw5rVq1olmzZjRo0ICIiAiCgoJo3749I0aMYMGCBaSZF0fhtTBMwsIc+8pLbm4uixYtol+/flSpUoXx48eTlJTEypUrvXMCL2DeS+d7sLSyycnRM9Xu3z+ZvLw8RowY4ZWR3koppk2bRm5uLllZWWzevJkhQ4Ywc+ZMWrZsyZEjLxISolte5bEwDhw4QHh4OOFmq7SSsAXDC5gWhp+fbgGXNGNttWrVmDJlCj///DOtW7dm7NixbNmyhcOHD/Ppp5/Sv3//Io91JxjuLAxvuqRKimG4IygoiNdeew2AHj16MH36dObNm8cnn3zC+++/z1tvvcVTTz1F/fr1WbBgAbfddhu1a9fmv//9L1DYwpg7dy6LFi0qmGhx7ty5tG3blhkzZpR5YNWaNWs4efIkAwboeS2vuuoqqlatytKlS8uUny+wBcM7xMcDvMEff7zJyJEjiYmJ8frUIFWrVqVjx4589NFH7Nu3j5tuuokzZ55nxQo9F2t5BSMmJsY7BS0HtkvKC2RnO1rhdes6piAoieuvv77UvXJSU7U1Y10PwlcWRmliGO4YMmQIQ4YMKTFdbm4ua9as4fHHH2fmzJlMmDDBKYZx4sQJhg0bRl5eHs2b9yc1NYI77/yEiIiIAuvk1ltvJTs7m0aNGtGvXz+PfNDfffcdgYGBBf9BcHAw3bt3twXjPCc/P5+lS5cyd+5cli9fTkxMDEFBDYFZXHnlbUybNg1wXg/D2zRs2JC5c79g/vzO/P33k6xatYqQkB7lEozLL7/cu4UsA7aF4QXMoDfoQUHWitvbuE5tDudeDKO0BAQEcPXVV3PzzTezf/9+0tLSnCyMzz77jLy8PP79739z+PAviMxiwoSniYuLY+rUqaxatYoRI0bw0EMP0b9/f6655hp27txJbm4u2dnZZGRkkJ6eztGjR1m6dClvvPEGAwcOZPr06cZIeccP2rt3b7Zt20a8bpJWOheqYDz8MFjHvOXkwLhx3olPPfvss/Tp04dvvvmGTp06kZKSwuLFnwODmDx5TkFvJl9PPqjv4YcIDY3kpZdeKrAwSmsQ5+TkcPjw4XPCwrAFwwuYLinQghHnw2kS3QmGOwvDGy4p1xiGr9dA7tChAyLC9u3bCwQjJET45JNP6NKlC5MnT2bs2L3AZp56aiJVq1bloYce4vjx4/z999+cOHGC999/n61bt9KmTRuqVKlCYGAgwcHBhIaG0rBhQ6677jqeeOIJtm/fzu1mqcOPAAAgAElEQVS3385bb73lVIZrr70WoKCrbWVjujcvNMH44w/4/HM9Ehpg/nx49VWYOrV8+aanpzNt2jRuvvlm4uPj+eqrr9i4cSNvvpkBfEXjxo5OFr6e3jwlBSCY6657nMWLF3Pq1Eby8krfmDt8+DB5eXnnhGDYLikvYAa9ARo00L0xfLVOsOt0GeBsYQQH64reF+MwqlWD5OTy51sU7du3B2Dr1q2kpupxKrGxf7Jjxw7ee+89AOrWbQA0ICPDIZzVq1enevXqANx///3ccsstzJw5k8zMTPz8/PD398fPz4+wsDBatmxJq1atiIqKcluGjh07UqtWLZYuXVritA8VwYVqYejKFN59F6ZM0WIB8OOP8OabZc93zpw5pKSkMHbsWKpZ1l2Nj/dHKb3ImYmfHwQE+G62WvMab7rpQX79dRIrVvwf8Abbtjkm0kxLSyM+Ph6lVJHuabOHVPPmzX1T0FJgC4YXcLUwcnN197ki6qRy4TqDKzgsgIQEiImBAwd855LypYXRqFEjatasyZYtW6hTR2+bP/8TAgMDGTx4sFNZilvDvHbt2mWedNHf359evXqxdOnSEgdvVQQXqmCYA0w/+QS6d4dt26BbN71a5L59emR0aRERpk6dSseOHbniiiuc9h0/rp9H17F1vrynz5zR7/XqVeeRRx7hxRdfBBZRVChi6dKlBRauFVMwzgULw3ZJeQFzpDc45tr3lVuquBgG6KA7+G7gni9iGCZKKdq3b29YGBASks3cuZ8zYMCAgu6EZll8WY7evXsTFxdXaMnayuBCFYyUFC0UKSnwz3/qwW0zZ+p9P5VxXuoVK1awY8cORo8eXUjorUuzWvGlYJgWRvXqMH78eB5++AtgDpMnz2HOHP366quvWLlyJXXq1OGNN95wm8+BAwcICgqqkIF5JWELhhdwDXqD7wLf7lxS1jFmERHeM7MrOoYBOo6xbds2UlLyqFLlJ5KTkxk+fHjBfk8sjPLSu3dvAGbNmlVCSt9zIQpGZqa+j/r21aPY09Phscfg4ovhkku0W6osTJ06lVq1armdDNC6NKuVqlV975KqUUN3M+/V63ZgKNdcM5ShQ/Vr0KBB9OjRg4ceeoiffvrJbSNl//79xMTEVLq1C7ZgeAWrS6qBsfK4rywMdy4pq4UREeG9h6CiLQzQcYyzZ89y4sQBcnM/p3bt2gUVuLUsvhSMJk2a8M9//pPJkyezZMkS352oBEQqTjCSk8HNgos+wVqRPvusXlxoxAi97YYbYPny0l/viRMn+Pbbb7n33nvdjtJPSHDvIq4Il1SNGvrdXPLA3bWNGjWKatWq8aabAM65MgYDbMHwClaXlOkSqkiXlKuFUaWK91xSVavq2Tah4iwMgKNHV5Oe/j233Xab06RuFSEYANOmTaN169YMHTqUo9b+yhVIejoFsw77WjCef15PPZKY6NvzgEMwataEgQNh0yaH1dy3r77HfvutdHnOmjWrYAS3O9xZ5lAxLilPBCMyMpK7776b2bNnE2epPESE2NjYC0swlFJ9lFJ7lFL7lVLj3ewPVEp9Yez/QynVxNjeWym1SSm13Xi/xnLMciPPLcbLByFk72B1SVWpolsyvhQM1xs/IMDRI8vbFoZVjEo7cK8stGrVioCAAPbtewWRjELuhYoSjODgYBYsWEBmZiYxMTG0adOGwYMH895777F7924OHz7Mrl27WL9+Pb/99htr1qwp91KerlinmPG1YBw5os8xebJvzwOFK1IrPXroirU0bikRYcaMGVx11VWF5l8zSU93v6iZr11S/v6OXobFCQbAmDFjyM7OpmHDhnTo0IEJEyawefNmMjIyzokeUuAFwTDW5J4G9AVaAXcopVq5JLsXOCUizYEpgLnKThLQX0Taotf8/tTluKEi0sF4JZS3rL7C6pIC343FKGqNCKUcFak3BcNcz9vEHLjnyzXLAwMDueSSS8jI2Ee1ag248sornfab7jdfCwbAxRdfzK+//sqYMWOIiYlh7dq1PPjgg7Rs2ZImTZrQunVrunTpwjXXXEP37t3p2rUrK1asICsri/z8/IJ8kpOTmT59OgMHDmTTpk0en98UjMBA3wtGgvF0TZ1qTqPhO8weUu4EIzAQevbUbilPWblyJfv37+e+++5zuz87W1tq7gTD1y4pc345KFkwWrZsybp163juueeIjIzk1Vdf5bLLLgPOjR5S4J1utZcD+0UkFkApNQ+4GdhlSXMz8ILxeQEwVSmlRORPS5qdQDWlVKCI+Lgd6z3y8vSYC9MlBTqO4Yugt3mjuQoG6Io0Pd37LimzdWSeQ0Q/fNbr9TYdOnRgx44dNG48uNCSrxVlYZhcdtllBQ+tiLBkyQH69FnN4MH5DBoUSkhICCEhIezfv58XXniBnj17Fhzr5+dHlSpVyMnJKRCQmJgYOnk47aw5aC862veCkZgIl10GmzfrMRGvv+67c1ldUu7o2BF+/lk3TswGwuLF0LWre5GZMWMGNWrU4JZbbnGbn/nbVbRgpKQ4l7ckwQDn++3AgQO89tprrFy5kksvvdQ3hSwl3hCMBsARy/ejQJei0ohIrlIqBYhAWxgmtwB/uojFTKVUHnrd74nixuZXSo0ERoLux1/RmBWzq4WxcWPp8jlyRAtBcZNRFrVGBHjfwkhKgr//LmxhgH6QfSkYl156KXPmzKFly8K9XSpaMKwopQgKag4055JL4PbbHft69uzJ0KFD+eyzz0hISCAnJ4fs7GxycnIIDg5mwIAB3HfffWzbts3j85kWRnS0rsh9SUIC3HijXkr03XdhwgSoXds35yrOJQXQrp1uiO3apQPihw9Dnz7w2mvwxBPOabdu3cqXX37JPffcQ7C1dWPB7O1XGS4pYzyp0/k9Ff+YmJiCAavnCt4QDHd9vVwr9mLTKKVao91U11n2DxWRY0qpMLRgDANmF8pE5APgA4DOnTv70FniHvNms1ag9evrB9A6ArwkbrhB+2/ffbfoNEVN+Q2OlpgpGGW1MA4c0D1Wli/X1sSttzr2WefecSda3mLEiBFMmHAxzZp1LrSvMgUDHK4bs9KzEhQUVKRbBKBdu3b8WArnvCkYDRvCqlX6//BFz8rMTH1vRUbCzTfDrFl6IN0115R8bFkoziUF0LBhIpDCli0xXHqp4vff9fbDh53THTt2jH79+lG7dm2eeeaZIs9nVtDu9CQw0HsrOCYnw4IFMHKk/p/OnHG+RvP853MXaW8IxlGgoeV7NODqwTfTHFVKBQA1gJMASqlo4BvgbhE5YB4gIseM91Sl1Odo11chwahsirIwRLQv2FxtqySOHy95llt3U5ubmBVprVpapMrSavr5Z7jjDn2zP/ecbnFaLWFTlHzdtTY4OJSsrBuKvU5fl6EoihOMkmjbti0zZ84kISGhyKlJrFgtjPx8LdTWLtTewuwZFRUFppF+5EjR6ctLSoq+x1wbHcnJyUyaNIl33nkHyOTRR6P56qt2rFt3GIhl1qwWKNWTli1bEhwczJQpU0hJSWH16tXUdzcqz6CiXFJffgkPPAC9eullWVNSHN3sQQfATdfx+Yo3BGMD0EIp1RQ4BgwB7nRJsxAd1F4L3Ar8KiKilKoJ/ABMEJE1ZmJDVGqKSJJSqgpwI3BuzAbnglkxWwXDOhbDU8FIS3MIQnFpwDMLo7SCsXattnLatYOvv4ZmzQqn8fXsniaeuN48tTBiY3XL2VsWkSkYZiu5NLRr1w6A7du306tXrxLTnzqle7+ZXbXT030vGOa962vBqF7dea61nTt3ctVVV3Hq1CmGDRvGihVdOXv2V44c2UNeXgzQC9jJhx9+SIbx5wcEBPD9998XzEFWFMUJhjddUmYj4uhRh2C4LrFdnjUxXMnP13n50tp3pdy9pEQkFxgNLAb+AuaLyE6l1ItKqZuMZB8BEUqp/cDjgNn1djTQHHjWpftsILBYKbUN2IIWog/LW1ZfYFoYri4p8DzwnZOjK2FPBaOoijQkRFfqZXFJbdqkraIffnAvFuBdC2P3bhgzxjHOwEpx1+nnp6/PU8G48kr4v/8rezldMXsQldXCAC0YnnDqlA4Mmw0EX7VMTRGMjNT/cWSkbwXj9GlnV01CQgI33ngjVatWZevWrcyaNYvevR8gP/9L1q3bRnr6d8BbhIQs4/Tp08TFxbF//36OHTtGnz59SjxfRVkY5kA9c9iOq0sKtFvKW//jBx/4roNNUXhl8kER+RH40WXbc5bPmcBtbo6bCEwsItvzYgVjdxZGaeeTMivIkgSjpBhGRIT+XKVK6R8Cc61hc9I/d3jTwvj8c3jrLRgyRPd+sVLUet4m5qp7JZGRod18O3eWr6xWyuOSioqKok6dOh4Hvk+d0p0gShssLS1WCwN0zMTXFoZZkZ49e5YBAwYQHx/PypUrC0S1XTuYMQMWLtSNik6dzMB/1VLPqVRRgmHet0eP6saXay8pswze+h9//VWfc/Lk8s3wWxrskd7lxF3QOzJS+yu9LRjFuaSaNnWYv2UxsxMTdeXkOpunFW9aGOY0FO5G9BYnjOC5YJjWQGxs6ctXFOURDNBWxrkmGFYLAypGMPz9NzFy5Ejq16/P2rVrmTNnDp07Ozo5mF6m99/X77feqivh48dLf76KcklZLYzMTG3lW3tJmWXw1v9o9sScPt33Y2dM7OnNy4m7oLefn57ozFeC4a7lPXWq9mmaZSmtSyopyVFhFIU3LQyrYEyY4LzPWxaG+RAdOqS7aZpTnJSHsgpGXh7ceSdERrZj9ep3ycvLw7+EAp06pTsxnDixDYhn6dJMsrJq0aJFCyIjIwsmo9u5cyfz5s1jz549JCYmEhAQQLdu3bjiiito2LAhkZGRTuldiY8X/P0/5c03D/Hcc8/SsKEq1cA5V8ze70op8vPzOXnyJKdPn0ZESE5O5s8/J5GS8i379oUwaNAgRowYQY8ePZzyMAwNVqzQU50bM8Zw9Cg0bly68lSGS8p1HimTkBBHN9/ykJQEBw/CvffqWX5ff92xpogvsQWjnLhzSYH2LXoqGGYFWVLLw7EKXeF9VsugLL2kEhNL7nfvLQsjP9+x0trq1fqBta5RXpwwmuUojWBkZ+v/omHD4tN7gjXoXZpurnFxelW5m25qS2ZmJvv37+fiiy8u9piTJyEnZxojR44G4KmnHPtCQkKIiooyplHZh5+fX4GQpKSk8NJLLzmNNm/YsCFDhgzhH//4B/7+/gQEBNCsWTMiIiL4+usHycubwwsvQECAPw0bPk1Kir7fShtQTUlJoWfPnmzZsoVq1aqRnZ3tVA4AparTrt2LrFr1aMHCV66Eh+seW3//DVdc4eg8UpZpvSrDJVXUWJOQkMLzdaWlwRdfwD33eH4/mRMGDB2qn4V334Unn/Td2BkTWzDKibugN+g4hlkploRZQZ49W3xLODVV3+Alje0oi5mdlFR0sNvEWxbG0aP6Jr/+ej2Cd/16PQbFxNsWBujxJeUVjKwsLRSmWyEz03lgY3GYXab9/Bw9pUoSjMTEzRw48DhXXtmHNWue5r//rUa7dgns27ePQ4cOkZiYSGpqKqNHj2bw4MHUsQSgUlNT2bx5MydOnCA+Pp6lS5cyZcoUJrtMFqWUQgTq1n2BXr328cwzz/DYY62BARw5ogfyeYqIcN9997F9+3aefPJJRIQqVapQp04dwsPDC1Y/fOCB3vToEVHIXeNKu3ZaMLp1K59geDJwzxtjXKwWRnGCceiQ87bZs+Ghh/T1GoO8S2TDBv1+6aXw9NMwdy589JFeF92X2IJRToqyMOrX93w+HKsr6uzZoivKVav0egElURaXVGIiRa4EZuItC8N0R40cCUuWaLeUVTDMh82bghEbq+coKi1Ll+qK5NprHS3DFi1gyxZdztIKRmZmS/z8/Ni2bRtNmjTh119/5ezZs+Tm5tKxY0d69uxJWFgYR44c5fTp2wkLi+Ltt+fQqVMEdevqrs/FYc43FhYWxtVXX12w/ZFHHiE5OZndu3ejlCIrK4t9+/axf/9+vv++H40aXc2HH2awd+9e3ntvKPAce/c+RKtWOpB06tQppkyZwnfffcctt9zCww8/XLColcm7777LggULePXVV4tc8VAE7rqr6EF7Vtq1g0WLtGDUqKEr27JaGFWruo/PBQZ6b7obUzDi4x2dSDyJYZjWwo4dngvGxo16/ZAaNfTrt990j0BfYwtGOXEX9AZd2Xka3LIKRlHTMO/fD+vWeeanLK1LSqR0MQxvCUbXrto3/dtveqCgyeHD+hqK6rEVFOTZbxsfrwPnGRllD3yPG6cr4a1bHe4oq2CYYyRKwhSMhIQgLrroIl5//XX+85//FOz38/Mr5LoBf4YNW0F0tO7+5sk1v/SSDhTv3194ZHNERITTZI7/+Mc/AD3gLCpKj1T/7rvvGDLkX6xcOZ5hwybTpUsHRIRNmzaRkpJCx44def7553nttddo3rw5qampZGVloZQiLi6Ofv368YTr/B0W0tP17+mJYNxzj74P2rTRot2wYdkFw511Ac5Wc3kFIzXVMY+buQ6SJ72k/jRm1Nuxw/NzbdgAxt8HgKVt4FNswSgn7oLe5ndPTV3TBQNFB74/+0zn42YxsUKU1iWVkqJbWCX5P701HmDPHi2K9erpm37aNOeJ5mJjoUmTol1zQUGOFlxxRG1bxu85E9gd1JZlf70KlN7Be/CgbrHn5joE46KL9HtpAt+mtXP8ONxxxw18/fXXPPzwwwwfPpzw8HDy8vLYsGEDK1asICcnh8DAeowf35mOHTuWqpfU3r36HHPn6oCoJyQmOhoL9erVY9myn6la9Q8aNnyNjIw4lFLccMMNjBs3rmAJ3SlTppCUlET16tUJDAxERKhZsybPPvtsoQkjrZQ08aCVmBh44QXH9+josgtGEdNMFTy33ugpdeaMvjd27nR05S5JMLKyHELhqWDExemXp9aIN7EFowzceqtu7UyZUrRLymy5WNfKKApXC8MVEZgzR7tUPBk5XlqXlFn5lmRhmJaPaXqXlT17tDmtlBaMN97QI83NFlNsbDHxlORkmmfHszejGOd6fDw8+CDPrvyahKrRtEzfwnULv4OPJ8O//uWxs/r0aceI7gMHCgtGaUZ7mxZGfDy8+urrvO4yHayfnx9XXHEFV1xxBaAtmvHjdfDXdHt5IhjJyfr97bc9C6KePavztc5UUqUK1KvXha5dv+Tjjwsf0759ez755JOSC+OGkuaRKo7oaFhWhvkePLUwykN+vm74tWzpLBjuXFKZmY5Y5c6d+lmtUcNzwTC701p6IVcY9jiMUrJlC3z1lWP20KKC3qZIeHIjWkXCXaWwYYN2Mdx1l2dlLK1LyvTNF2lhxMXBBx9QrUoeAQHOFlFZMAUDdA8YcPhxoQTBuPdeJi9pxzVJ893v/+037ef68Uem1H6JMf32MfGWLfxFK93kvvpqPQ2qBxw86Pi8c6ezSwpKZ2GYgpGf78inOMx5pMLDdTdtT0cIJyVpX/22bTrmVRLmf+/aWPDVWIySZqotjuhoiD+WS+6R43qqAA/9jBUhGOZ/Y3YSMG8xd4IBjkC8WY8MHqwfM3NK++LYuFHfEx07lq/MZcEWjFLy9tv63azkS7IwPKm4S3JJzZmj8ytiun9njhzhxtXjCcvywGdjUKyFsXOnDjbcfz/qh0WEhZVPMM6e1T1fTMGoVUvHKkyf7+nTurJ0KxjJyfDDD+T5V+Wd5Dvgvfdg0iQ9yqtpU+3svvZaXRutX8+LuU9Rq341gjq35orcFWROnaGbce3b6yHmq1cXuxqUq2DEx+vWvjnfUlkEAzwbfGYKRq1a+t3TAV/JyXDTTfo4814tDlO8XOdCPOcE4+RJ+m9/mUPSiIBG9XVTPiZG9wIwuwwVgSeCUV6XlGl1R0dr121qqj6na6Dd1b34559aVG4yJlHyZFaCzZu1MBXlZvMltmCUgsREPaUFOCrNkgSjtBaGO8H49lvo18/Dh+yDD7hq7STWS2fyNv5ZcnqKaGWmpemLvfJKbUbVqgVz51K9evlcUvv26Xdrj9JWrRwtMrOSdisYX34JublMH7SE1f5Xw4MPar9NWBhcdZXO9NFHYeNGsi5qy+nTOigdEwOCH3u636tbpqNH66l5e/SAl18usqxmWWrVclgYUVGO/6G0gtG0qf7siWCYLU2zI5KngpGUpCv7++7T901JlX5JFoa3V1c0XVKexDAK2LgRYmK4/Lun2UEbDv57mr43J06EP/7Q3fvMIeFuKE4wSuMJKA7zmQgLc7iN3XUbdhWMzZu1pWDMS+mRW+rUqcICX1HYglEKpk/XN1b37o6KvSSXlCctl7Q0R4DXnWAkJzsqmxJZt46Umo3wJw+/HlfofqslYFoYtWujfUMDB+ovQ4fq5vS6ddpmXriQqOC0clkYZg8pd4Ih4vAyuL3ezz+Hli05EXMlN8oiPfva3r3aUpg1S/sK33gDQkMLWs516jjEJzYW/aRNmaJnbOvZEz7+uKBWzMx09FgBLRg1amgDa9cuh2CEhenYQGmD3qYLoTQWRmkEIztbN2Rq14b779d+8i+/LP6Y4iyMs2fLNitvcZTawti+XQ/YCQ9nzxdbuJ4lbOryoO798fTTelDDP/6hGw6uI+IMKsIlZT4T1as7BMPdNVrXxMjN1bGqSy/Vx1Sv7hCMQ4ccVrfrDJ2exEV9hS0YHnL6tB5Nef31uneCpy4pTy0Mswupq2CIOPcgKpa8PPjjDw627EdnNpLfNAbuvrvELkWJiRAVmELIvYN1JG3VKhg1Ss9utnWrnothyBDIyKBvzsJyWRimYJhxANCCceaM9uGaglHIwjh8WJdr6FCqBSnS8oPJ+ecI54wsmL2SCgmGSUiInqsjNrbgKf3iCz3JnblQz8GDWrhat9bljovTFaufn364PRUMc+p6c3oLT2YAOHlSNyLMjgaeCIYZ8K5dW19z69Z69uHiKM7CAO+7pUolGHv2aBdjUBD88guR1+oJppx6SoWF6Xlx0tKc+2ZbOHu24lxSJQmG1cLYs0d3+b70Ut0AadNG34pZWfD8FUs52GOY7i4YGurkHy3NwmzexhYMD8jI0D7GpCR9T4aG6vtTxDtB79RUR39+V8HIydGBUo8GiP31F6SmkhjTlQTqkPreZ7rmefDBYn0LSUkwNvAt1Pz58MwzukvQm2/qlpvphO3eHaKj6Zsyt9wWRqNGzv5Xa6AwNla7gAo9bHPn6vc77/RoESWrYNSsqVvqhWKk/fvrJ/W77wDdqheBNcbKLFbByM7WD7Mp7DVqeN76NsvSqJGuzF0tjOPH9UBP6zKssbFap81eTp4IhtkuMGctvvFGWLmyeBdiQoJujIRKqtMQ5Oho6M4qcuZ9pUcvWgM65SAlRd9SJfrfDx7UKxGJ6K5RTZsW9Bgr1LW2VSt9j3/wgW7guHCuuqRMa9ZcpKxNGzi4PY3dPUcx6/h1XH5qsY63ZWUV3KNgC8Y5TW6uIz766ae6V09oqL6PMzK8E/ROS9MtPKUKC4ZZKXpkYaxbB0BSi24AZFzUXi8G8eWXjgrXDYkJwi1Zc3Uc4D//cd808vODwYPpnLQYdcqDrhxFsH9/YaPAVTDcxi/mztVDfps29WgRJTPIbFbwzZppHXSibl3tb/r2W8BRafz+u/5/Dx1yCAZo4TZdNzVqeG5hWMtSr15hwdixQ2+zzty7e7fzqH5rL6lPP9W/masHxmphgI575eYW75VMTISeNf5EdWivgz2PPAI7d9LhxUGs4io6/fdWuO46vSLQQw/pHgsffqjdeePHu1/QpBjMtTCK7e577JgWi7NntVgYP4RSujJ2a/W88IJuGTzxRKHGkVvByMyEjz8mKFe3firKJWUVjM2btQCa7tkOLdJZfOoy2q77gFcZS/vwI1ooLrkELEv72oJxjpKaqt35Cxdqq3fwYL3dHMCWllb0SO/SdqsNC9P5urYizUrRIwtj7VqIiCCjQXPAKNvYsbqifeABXVu7oebf22iatVsrY3HceScBksMN8TM9KIx7EhIKj46OjNSt4iIF49gx3U904EDAs1X3rBYG6Dzd9sK8+WYdtzlypOC/XLtWH5+R4TxtPJRPMOrWdS8YpovK9FmbkzNaBcNqYaxYodPed59z3ehqYXTrpi2rArdUVpbzAtZnz9J54/t8k3CFroWGD9c3eps2VFvxMxPUK/RvtJXXbl5FbN8HdRCvcWM9p8vhw7qHWu/eRcYO3OFujQgndu3SnRGSknTHBDMabNCmjfZMFtKpWrW0+f/LL04KmZ9fhEtq4kS4914ufuluFPlkZ+t0ZZk+HUrvkoqP1+t/X3aZI37Z6/DHtGQ3g/y+Zc1Nr5KUarQ6b7hB/+nGDZCd7UYw8vO930PBDV4RDKVUH6XUHqXUfqXUeDf7A5VSXxj7/1BKNbHsm2Bs36OUut7TPH3N339rL8xPP+mRyA8+6NhnCkZqqn7O/PwKj0ourYURGupwdVkptYXRtStVA3XzLScHbf/PnasLeOutbmvZ7kfmkqeM/cXRsSO7G13Hw2de8qzDuBuSkx0VmolSusW8Y4euhwoJxi+/6PfevQHPBSMszJG2ZUttYRRyzwwYoN8XLiwQ961bHd0bmzbVD7kZhDcFo2ZNzwXDFC9PBePIEX1tRQnGoUO6wvh+YT6fv/J3QUWRnAzX8zNNPxgPy5cToPIY2uNvgr6egwweok2PWrV0k7ZfP4iKYvTOB9gd3k2L5scf6x5JTzyB2rGD6i+N42R0O178tTvtlr+DbNmqK+U1a7T6zpql77mLL9YNEw/GRaSkFNNDaskSrXKmZeFmcrO779a/3+LFbo5/4AF984wdq+N5OO4RJ8HYs0fPsdOsGTWXf8s4JpGVBc8+W/bR0wUuqVDxyCX1wgvatfbKK8aOnBxivn2N1XSnwaib6NJF1x1ZWUDfvvrLr7+aSROAyfkAACAASURBVB2CIQLffKMDZGUZ1VhaRKRcL8AfOAA0A6oCW4FWLmkeBN43Pg8BvjA+tzLSBwJNjXz8PcnT3atTp07iLQYNEgkLE1mypPC+r74SAZEtW0SefFKkWrXCadau1Wl++qnkc4WHi4weLdKihciQIc77/vpL5/P55yVkcuqUTvif/8i8efrjzp2W/YsW6Y333ed8XH6+HFKNZWfjPiUXVETeGblNcvGT/EceLTnxvHkijRuLPP+8SFyc5Mz6TDbQSc4GhYtER4tcdpnI+++LpKfL/feLBAToIk6f7pLPsGEikZEieXkiIvLNNzrd5s1Fn3rwYJHmzR3flyzRx/z8s5vEF18scu21MnasTgMiI0fq9x07dJIbb9Tfzfth6FCRZs1K/glERJ59VsTPTyQ3V2T8eJHb/b6UvP++UnA9o0frvMPDRfLzdRlBZOVKRx6jR4vUrKk/D2qwTn5rMlxOVYkUAcno2lPkzz9lZc9nHRcAIkFBBZ+zw6NERowQeeklkZtu0tc8cqQMjvpVhg/LK7b8b7+tszlxws3OLVtEbrtNxN9fv+bNKzavK68U+cc/3Oz46Sd9A7RtK3L4cJHHZ2eLREXp59Mt5s0/c6aIiMTH66/vvGPsz88X6dVLpEYNkRMn5Ey/IZKHkjUjZsoVl+cIiCQnF3sJbhk3TmR4wByR2rXl9De/Coi88ELhdMnJjr/nkUcsO2bPFgE58Pb3kpWlywsiiYkikpkpEhIi8sADInl58lbweNlf5wqRAQNEOnTQCS+6SGTx4tIX3ADYKJ7U954kKjYD6AYstnyfAExwSbMY6GZ8DgCSAOWa1kznSZ7uXt4UjG7dRHr3dr9v8WL9y61eLTJmjBYWVzZt0mm++67kc1WpIjJhgkjHjrpisvLnnzqfr78uIROzUMuWyddf649//umSZsIEvWPWrIJNWb+tEQH5dtAs8YRXXxV5n5GSHxAgsnt30QmzskQaNdK1nKUS20lL2dbjQZF//tNxs0dEyHf3LSxItnSpJZ/8fJF69ZyU1KxQ16wp+vQ9e+rKySQ1VddnTz/tJvHTT4v4+cl/7t4rVao4Km8QSUvTScaN09+3btXfH3xQpFatos//9dcin36qP48YoSs5EZGpb2RJPLqil3vuEcnNlfuvPyi3Ml9COSMnToi8+abenZAgWmXWrJH37lwpV/uvkrzBQ0RAzgbWlFM33CnP8n+SGVqr4PedHfAvkaQkkfnzRR58UNL++7Z0VH/Kc0/nOpUvN1fklVe0kD3zTNHXISLy/fc6+7Vri0l07JhIjx76Zl62rMhkbdroes6JDRt0hdihg0hKSvGFEZF//1trS3y8m535+bohEhUlsn69xMbqsn/8sbH/o4/0hmnTRETk77/SZAOdRED+pqGM4l3ZsKHEIhRizL1nJF7VKRDqrx/5TQ4cKJwuM1MnadxY35MiohsOrVvrH8doRMyapdMV5HHTTfqgBx8UATlY53KdvkMHnTgnp/SFtlCRgnErMMPyfRgw1SXNDiDa8v0Aeia4qcBdlu0fGfmVmKdl30hgI7CxUaNG5frRrLRtKzJwoPt9v/8uBa3Vhx4SiYgonGbHDp1m/vziz5OVpdO99JJ+3nr2dH+uYi2VlBSRRx8VUUokJaXAmFi/3iVdTo7I1Vfrluf27SJZWXK2/+1ylmoy442SH1QRbRBEcULyQsNEbr/dad/KlSIZGcaX995z/Ei7dok8/bQcfuc7UeTJ3LlGmvx8kRUrRC69VPL8A+Q2vnB+SEQcP+SMGQWbVqwo0MYiadlS5JZbnLddfrn+jQtx4oRIUJCsvWiYREWJXHKJzt+s5EX0uaKjHfXZhAlagPLzC2eXlyfSoIG+L3JzRfr3F2nXTu9bO0a3gFOu7q9PEh1dUNlvo438/lmsjBolEl4zX/K//kZXJBbBzasWJC/yjHz05hnJyNB/+aRxySLjxsmHXWdIk8aFC9S1q7N4njmjv4NuqZ88WfTvKKItVY+t3DZtdAvqmWdEHn5Y39jbtxf8UA0b6raC5OeLxMaKzJmjf+gmTUTi4ko4gXN5Xn+9iAQ7dujKtWpVOfrs+1KVTPliXr7Iyy/rA6+6Sv8xInL8uIgfufJ+329lBT1EQFY98Y1H5bDybeundN7ffy/SqpVIcLAWpQJVcPDccyJ//GHZ8OGH+tjZsws2FWr0vf9+wT3wRpUn5bExbm68clCRgnGbm8r9HZc0O90IRgQwzY1g3OJJnu5e3rQwmjTRnhB3bNumf7kFC3TrsV69wmn27tVpzFZmUSQliQSTJpuvflReb/OxdO7svP/XX3U+v/3m5uAzZ0Q6dXJUKMb1Wy2gQsTFidSpo/1fRmX0X8bJggXFl9Pks8903ieHjtbCk54uIvrBU8rwBGRk6IrwiiucatSVK8XJrVNASopkXt5dcvGTO9Rcyc627DOb24cOFWxav97xbBZFrVq6MWbliSdEqla1iJqVf/9b8pSfXFVnt/zrXzr/Ll2Kzv+VV8TJArGyerXjL1m/Xjd4r7tO7zvdsaccoKn8/GOeyFtvifTqJS/VfFVebD1PTlJTzobWljUR/eVUQITO4OKLRWbPli/vXyr9+F6WfHLMybUWHS0yfLj+fMMNBbeAE2PHOl+32cj+8EP3gudKerqjUVMiR4/qewtEqld3/BCNG4sMGCBvVH1StsYM0Pegua9+/eKtVTd07arr5SLLn5SkXQQgWVSRtLrN9LnuuEPk7NmCZCdP6s2XXipShSzZyKWSHhyhLSZPOXRIsvwCZVH4UP39xAntogDt+nr55aILunChbnlcc41Yb/xly/ThK1YYG44c0UI8ZowEVs2XJ5/0vHie4KlgeCPofRSwrmUWDbgOTSpIo5QKAGoAJ4s51pM8fUpR61KAcy+porq4eRr0zty+j3V0peOKt3h8xz1ce2yW0/5ie0lNm6aDlc89pwNfRte7YkeZ16sH8+bpCHBKClsmfs8EXvF4aUczkBffbYAunNEjJTEROskGqq/6QZfn6FF48UWn/pNmt0/XoDfVq1P1l5/Z7H8Zb/g9QRW/PMe+Zct0l07LQs4lBb1zcnRM3nU9jauu0r/J+vVuDho7lmy/ajye/mLBhIjFja43A7fuAt9ffKH/f6X0zxMfb/QM272bGn8uZzr3czzeDx55hPwly3ghfSzpNw6mV8gfJAQ3pc7pPWxvepMe2b5jBwwbRnLHa/mBG9l8oj6gx3OBc3fhpCT3E0j26KGv25xy6eef9biPe+/1bOLe4GD9W3o011+DBrpPcE6O/nGOHdNzfl12GbJ7Nw9nv06DUzt0V91339WDEQ4fdh767wGDB+sOVdY5upyIiICffmL7/33N6zxBZuOLYfJkvU6A5WEyn9OtWyEgqCoP1fwc/+wMHV0vtD6JG0TgiScQFDObG9PM1KmjOwb8/rvufvzUU3ogbF6e7t61cqVer/edd+D22/UUAN9+61SRmM9ZwZin6GjdxXDKFLJzVKV1q/WGhREAxKKD1maAurVLmodwDnrPNz63xjnoHYsOeJeYp7uXNy2MatWkSBVPSNDqP3WqyJ13OgdXTU6c0GnefbeYk3zzjeSGVpdEImTVuO9le/3ekoufjqobWAPsTqSlidSuLdKncLB6jQ5LFB8D++svkTNnZO5cndYpQF4My5fr9L8uztbxCaN5++f76xwtRtCuL5dWlWl5FxXTfKHtAp3gxx/1huxskdBQkVGjnNLt21fIgnfi6FG9//33nbebAceJE90f981FT0oeSvYsOSSg3U6FSE0V6dlTjrXvI6/wpBx5/Qun1mhurkjdutrV06mTSPfuunX/5JP/396Zx0lVnPv7eWdhhmEbFlFggoyACyIMCqIBRUXQKKCiRIn4g6jXNVeIuRoTTTDXaBC50ai44L4iqBjEhKuAYkS4hkVEEANRUSdOYBx2Bpitfn/UqenTPad7et+o5/Pp6e7TZ6kz55z61vu+VW8ppaZMUQ35+eowtja21s299OCD2hIZOFB/v/de/8O+8IJefsUV+t00kn/6U91AV0oH4X/yk6ZF/v57vc0992ivZHGxDp9Ewqmn6kZwML75RjfeQ7m3duxQSqgP7kqKAHPfNmeYmPhLE/esQ22t75Y95RR9njOOmRWee0CpRpP7oa73qPPP9/i9oUGpXzvuqkGDtJ/S/Zz06eNEtv0J1tmlrk4v9wqoxwLJsjCUUnXAz9AB642OGGwQkf8WEScHI08BHUXkn8DNwG3OthuAucBnwP8CNyql6oPtM9ayhktdne7OaiyJQNzdaj37RNNMK7+uTg96uugi9v/gaE5kDXuGjeKZ0W+wJvdkmDSpsZ9eUAvjscd8Q88DMOUJad0ceyy0aeOfRyoMTMtnV3W+7pr51ltQV0fXx6dSSScembBcN+EXLGjSfA1qYTjctGg0DR07wTPP6AVLlmgzbvhwv/WaszDMgOVAC6NDBzjhBN3A8+KtH1xPDopef3+JW24JMlnVnDmwdCltt3/NFB6g5BeX6lZ1377w+ecsW6ZbvT/+sW5Ef/ihvg5HFX4Hzz6LXHwxtcWdG7vWmi61XbvqrsVmroPAqXhNd8wNG7S1Yv4HRx2l97F/v/7/el3Hjh31vpct05dm506d4iYSSkv9B3uPGQPuKT3MvNIffRR8H2vWgCIn5rnVIfy5WUxX5GAjvfPydLd40COujzoKHqq+Sv/D7rtPV+vBKC/XgxlPPZWZRbd4z1EuoqdBvP9+bWqOHKlznm3YoP+ha9d6XjSzr8DzCzZhW7KIyzgMpdRflVJHK6V6KqXudpb9Vin1pvP5gFJqnFKql1LqZKXUl65t73a2O0YptTDUPpOFGQsRTDAKC/VNZlxSXhcvaC4ppfQkPvfeC9dey0czlvEt3WndGlq0b8V/8pBWImeCGr9xGNOn61Fuw4bBH/6gK9JTT21y7EgSH1ZW6nvapNFuDvOg7tmDHsNQVQX33Ufnj9/mPm5hY/GpujO7hz+vqkr/X4KlhWh/eAtyJlyuR7du2wa33qpdUeef77deKMFQSvenLy72nuP4tNO0p8BrgHJ5Xg8+bnMaOS++wPR7FSec4FHIJ5+E445j3ewNtGYvyx/4u645v/8ezjyT9x77By1b6rQc55yjy9OaPYx7fpS+WX79a7+xGG7BcA8QDCYYGzf63FGgB2eDHui3a1dwMR46VIvXwoX63j37bO/1glFaqscm1dXpMi9YAL/6lS7Phg06BT+Enuvj8cf1QMJRoyI7thfBKtRAmhMM8D2rAwbo/+c35TnUTfkvPVg02NiGhgb9HNfUwPPPs2NPXlAXNgBTpmjX28svw9ixWpB69Ag6ZLuJS8ohWCqiZGFHentgBCPYDSDiG2RXU+MtGEFHej/8sH66fvc7eOwx9tQUNB6rdWv4qH4gDaecqtdraGisFIv2bNUxgS5d9FNbUKBHq3pgjh3OrHvff68f4sC8/cHwa9mdc44ux+23U936MGZyo99I9f37/dM1m0F7If3m5iEcPVpnKp0xo4l5FUowXnxRp9iYNs17fo/TT9fXzZ23yXDwICw+fIL2wXut8NlneqDaVVfRrlioI59vjxgEN98M776Lamjgurln8lzpnbR67hF++N2rnFu4lDlcSvE363SKlhNOoEsXn1B4CUZ+ftP4ianw9u/3FwwzyNHEJ4JZikOHakF57DEYPDj8BoL7OPX1elDhsmW+5ddeq60Lc02CCcbWrTBvnjaew8pa0AzxFAzzvBgLQyn46tSf6Gftvvu8N3rkES0m//M/0KsXu3d7D9SLllat9HMSzMKwgpFGNGdhmN9CBb2NqevXyl++XFcuo0frJH8Bx2qcjevK/9QTR7z9dqOF0fahu7W58frruqn4r3/pPEgehOWScnDP5xwOfi2fNm10U1Uplg25jWpa+QnGs8/qh9Bko/Aa5d2E/v31Rn//uw4YeswaZUa9BwrG9u06ldApp8B//If37s00sF4Nx5oaWN5tnK5BTJPZzVNP6Qt7xRVNg959+vDnm95jd0Nrxn32O7jxRvJ/8mMWHjiT81jIv3/7qB6xi27Ffv65rpiMYBxxhE8wevZsek+5KzwvwTCB/FCCAbqBEKk7CnwC9tVXWjBatdIx2w8+0AbhbbfptoNbMK6+Wt8eO3fqQeR1dTqrSDyIt4WRn69zhhmL7YvyAp1Xa9Ei/5z3oEeK33ornHsuXHstdXX6XoynYIjox8sKRgZgzMBwBCOYhQF6eaOFUV2tHdtHHgnPP9/oOHUfyxxvx1kX6xrkoYd0i5KvyH/6Md2tJUg678DjQvMWRm2tbjAHnQ7Vg8JCnWWk8Ua+4QY46ywW97oO8M+F9e9/62MY33dYggG6R0mLFvCnP3maIyK6HIGCcf/9+hiPPebzSwfSubN2PXgl5Dt4EGpaOT6T2bP9/VaO64ELLoDOnZtMolRXB7c804fLBmxCHazRPqd163jn1sWcWfQRbW72KZgR0a+/1oLRqZOutEpL9WkHuqMguGB06qQrFiMYwf6/PXpoKwZ0PRcpbsH44AOfKA8bpm/VKVP0/9YtGMuX6zDUsGHaHXXGGd7nFg2RCIa5X4JRUKDFoqDA9yx88QX6PmzbVvs2r7hCZ3184gmdFr+wUDcgRBqf4ZAuqSho27apSypY7rpkYQXDg3AsjDZtmheMggJXK7+oSDfJ5s3zS6bjPlZjd92aFvpmXbiQC2ZfyiuMR3Jzg+b7DyTcGMbcudrF4M6T1RwiATfyeefBkiVU7tOBCbdgmHVMEDpswbj6al3hBiSec9OyZVPB+PJLXbH17x969yNG6MosMG9XTY3jz54wQftQFizw/fjoo7p5ftVVgK7Ac3N9gvHSS7qSufNOkBb5uhY94QRGTBvOW9tO9qtMzERKH3+sBcNU5Hl52svodT2CCYaIruSM6y+YhSGiK+zDDtNTnkRKSYk+37VrtWt/6FAtym+/rT2HbdpowXDnIdy6VYezvvhCi+N110V+3GBEEvQuKgrtBj3++Ebjjy5dtBZ8+SX6OV2+XCdlXLBAd7W95hp9wrNmNV44d+LBeJKOFkaYnutDi+ZiGNC8Swp05eMXw3CyrQYeS0Tf1O7xHfzsZ/Dxx3RduppC/q0jjGYy6WYIxyWllHbPHnusrvMjwWtebzM3hFswzP/RbWGE1RsrjCi8l2Ds2BHe1J8jR+r+A++/7x9PP3jQEdvzzoOjj9bCMW+ertWnTNHLR45sLGLbtvq86+p0VvgBA7S3MfBUAt0h/frpynfNGn/BAJ03z4tgggHajWKmgQglyH/6k29ipkjJy9PG8dy5Ot5rXFwFBb6gsdvCMGNhbrpJDxd67TXP2z9qWrTQFXs4ghHKHQW6I4DBCHBjKvzjj9eNhT/+Ubd82rTRQT/XTt2pzeOJ13TIqe4lZQXDg3BjGBUVoadLbNGi+Vb+3r363svJCRCMjh3hz3/mtzfoWGlleMZF43EhtEtq8WJdyTz5ZHD3TTC8bmQTpwhmYSgVgYURBsEEw0xpGoohQ3Rl8847TQWjoAD95/33dbNz1Cgd7TXdIV21bbt2+pg336wrmPnzwxsI17KlFmpjYYQwpBpxV3rdu/v/5nYphvr/duoUfvdpL0pLtYspN1cHzgPp3NmX5dc9Re6gQdFngQ1FOPPLh5ptLxg9e3oMUmzZ0r8bmwv35EnxxMslZS2MNCSSGEZOTgQWRpBjmeP4CYbD/v2R9yoJxyV1333aazJhQmT7hvAtDLdg7Nql6914CkbgjHs7dxJWH//CQu1XX7TIf3mjSwr0P2fpUu2vzsvTo+MDHOHFxXpYRn09TJ7c1LoIxYAB+viVlf4WRjDy8vR17dCh6f1gArVFRfHpgRQME8coK/OuHI2FoZS/YCQKr/swkHAsjECOOkpnElfK1wBYvlz3vrv99qbrJ9IlFTidb6oFw8YwPIikl1TYQe8Qx3LP2+w+PkQwn7eL5lxSNTW6spo0yVVBRoBXyy6US2rLluYH7UWKV9A7XAsDdBxj40b/6T4bXVKGdu307EPz53vWxB06aPfMAw/oVzjWheHEE7WPv6EhPMEAfX8EuqPAZ2HEYj2EgzmOcUcF0rmzvl/37m06gVUiCMfCiEYwevbU27kD+C+9BFOneo/jS4VLygpGGhEvwfALerv45BPtCzbHireFYSZ0CuaSMvvv0iWy/RpCWRjV1b5lZp2vvoq/YAS6pJSKTDCcUISfleFnYYTBtGm6JTp5cvjbGEzgG8IXjPbtfdaEG7MsXv/bYBgL47TTvH83k0tt25bZguHXU8ph925tSbpzh61ZowUykS6pwPOzvaTSkL17dYUUKjjoFoxgFy+YhTF9uu5wUVPjLRjuVno0FoY5djALIxyXWygCb2TzIOXkaMEwOdvMcfbt013XIXGCYeZXDyfoDTqTR3GxfyLCJhZGMwwapHseRUNZme9zuIIxdy7cc0/T5d2763s10RbGeefpvhfBOkmY8TxuwTAikggSJRimb4k7saE5jkmls3evjuP84heJdUnt2eNv1VgLIw1xxxWC0bq1rhh3747cwvjmG105ffyx/7G8XFLRWBigb6hgghGOBRWKQAvDPDBmrm5Tke/d66vEVq/W74kSDBN0D9fCENGCYcRZKf0wRuOii4biYl9LNlzBOOmkpgFv0Nf62GP9EvomhLZttWAFux8DLYyWLaO/x8ItTyIEw4yxce/bfDaW8rZtunfck0/6Av2JsDDMnOSGVPeSsoLhgbvVHwxzcxw4EHnQ+5tv9LsZC2D21aKFfsUawzD7as4lFatgmJaPcUeZlpmphPfsoTEfU6IFw5QhXMEAHSQ2D6MR12Q+iAMGaOGKh9vmnXeCZ7FIFoGCcfjhkcV1IiVRguE1KNA0kIxgmPeaGp3RoKgouu7KkZbDWhhpSKi5MAzuyjaSbrV1dTqrB8CKFU3Fybi6DNFaGKFcUrEKRtu2WiyMMJjWvVswGhr0cYxgfPyxdlmF6zJqjlgtDPAWjGRZGKDzMP3Xf4WfxysUXbvG738bLcYlVVnpE4xEkijB8BoUGOiSMoJx4onaJRtvd5S7HG5r3gpGGhKOhRGOYHhZGBUV+gbLy9OCEej+ChSMAwfS0yUFvhvZtO5LSvT7vn2+irhbN12J79un3+PVCou3YJjrlEwLY8QIHc/KFgoLdcXptjASSdu2+rqF6okYjWB4DQoMdEmZ9+nT9XOeCMGwFkaGEG4MwxBJ0Nu4o0aM0F06d+3y31erVk0tjHRzSQXeyF4uKXd+HdMVNJ69eIIJRiSt7KIi3z7MdUqmhZGNmLEYyRAMrxa4m9pabdFHKhig4xjuHlHBBKNfPx3X+fGPIz9Gc3gJRkb3khKRDiKySEQ2O++e7TsRmeiss1lEJjrLikTkLyLyuYhsEJFprvUniUiliKx1XlfHUs5IiaeFEdjKN4Jx6aW+ZW73V+vWTXtJpZtLKvBBNZW1Cd4mSzAOHPDFUTLRJZWNdO6srejvv0+OhQHB3VLmOQo2/0pz+zb7ra31NSyMUGzfrt/bt9cj/e+6K/JjNEc2uqRuA5YopXoDS5zvfohIB2AqMBg4GZjqEpYZSqljgQHAEBH5kWvTOUqpMuf1ZIzljIhIBSMaC2P0aJ8QNBfDiMbCSKRLKpiF4XZJufNxJUowGhp8D5ApQ6QWRipdUtlI58562pCGhuQJRjALI5zU5qH2be5v9/7dMYzi4vjEn0KVAbxdUpnaS+oC4Dnn83PAhR7rnAMsUkptV0rtABYB5yqlqpVS7wEopWqANUBJjOWJC3v2xCfoHczCaN9ejxI2WUMTEcMIxyUVzYME3jGMnBxfBeG2MFq3ToxgBM6JsWOHfsAiiZG0bGktjHjjTkCYLhZGrILh3r/bJZXogZLZGMM4XClVAeC8ew3T6QZ86/pe7ixrRESKgdFoK8VwsYisE5HXRCQOswCHTyKD3t984+tLb2ZXDXRJmQq9tlYHyOM9cG/v3ti6AXpZGMXFvv9JdXVyXFLgLxiRuKPAWhiJwD1QL9sEQyQ1gpFOLqlmDSoRWQwc4fGTRxou7114LGscuygiecBs4EHXXN8LgNlKqYMich3aejkrSPmuAa4B6O41qilCamr0K5EuKTPA6oc/1O/uHhatW/tuEJNcLxG9pGIZUOUVwygu9j2YgS4pU/5EC0ak3UptDCP+ZLNglJT4u6QSOYod9L2Yl5dhFoZS6mylVF+P13xgq4h0AXDevWb0LQfcFkIJ4M7BOAvYrJR6wHXMKqWUqWqfAE4KUb5ZSqmBSqmBh0Uy12gQzE3WXIXqnpQllIVRW+s/tN9tYZx/vp60y51ewn2jmsowEb2kYhGMYBaGWzDcLqlevbT7zVhU8SBQMHbujM7CqKvT/ydrYcSHdBKMWFJ2uHtJmf2UlmqhiHeq/mCYOVcySjCa4U1govN5IjDfY523gZEi0t4Jdo90liEivwfaAVPcGxgRchgDbIyxnGET7nSLIr5KN9TAPfC1Xvfs0RWbEYy8PLjySv+L36GDrgQPHIjNwmjOJRWLYJhpWt0xjPbt9XKRpr2kWraElSt982nHg3i5pMw+rIURH4xgtGjhS7GRKJoTjGhG/7v3vXu3Fge3YBw8qK3S7dubneMrLgQKRkZ3qwWmASNEZDMwwvmOiAwUkScBlFLbgbuAlc7rv5VS20WkBO3W6gOsCeg+e5PT1fYT4CZgUozlDJtIehCZdUKlBgHfRf7WieSE8pyZm3vHjtgsjFAuqXDGmYQicIJ64w4ys8u5XVKJyiUUT8GorrbjMOKFEYxEpwUBff1ycoILRjRjcwxt2+r44f79/oIBOinh7t2JtzCgad622lpfNupUEFOnMKVUFTDcY/kq4GrX96eBpwPWKcc7voFS6lfAr2IpW7REIxihXFKgK6M2bXxdakMJhmm1bN+u3SWQmF5Ssbb+3LOBGZcUeTsR6QAAHYNJREFU+AQjN1eXO1HdDs3/yfiUoxEM8391C4Z1ScWG8Qon2h0FTRsugUTT1drgtl7M/k2yyM2b9XsyBMPLJZUq6wLsSO8mxNPCCHRJhSMY8bIwEumSAv+Wjzt+YAQjViumOXr10u+bN+vzrK6OLugNelvrkooPHTvGL6FiOITKJ7Vzp77G0TQCAgVDxNdZZdMm/W4FwxJ2DAMiszBAC0ZubuiJi9wWRrr2kgJ9I1dVNa2s3S6peKd7dtOunXZ/bNoUva/ayyVlLYzYyM3VKWLCmSo3HoQSjGh6zhncKc5379b3sknVH++5XULh5ZJKpWDYOb0DiKdLyix3C0a3bqH9j24Lw9wY6dZLCvTkMY88Al9/rb+bB7OoyJetNpGCAdC7txaMaNKCgA16J4qFCxPf5dTgdo0G4naVRrNf0D2ldu/W341AWAvD0kgig97uLrXBiJeFkWiX1KWX6v0/54zzD7QwEu2SAjj6aO2SilUwrIURX/r2Ta5ghHJJRdNDyuwXfBZG27a+ZzOVghFqhs9kYAUjgEgEwz3xkRdeFkZzpnq7dtpfmqheUmbMQTwsjCOP1ONIwD+GYUZ6J8PCqKjw9T6LRTCshZGZJMol5SUY+fn6+TT3W7JcUvv26R5boJ/fVDZqrGAEYMzbcEaHRmphbN/u60USDDPJ0I4dsVsYXi4pMzAx1spcRKd0NvMeJzuGAdrCAN+83LEEva2FkZk0Z2HEUzBAi4RS+j6JNhdbNOVwpwuyFkYaEUmepUiC3mb+73C6s7Zvr8Ul1l5SdXX6uG7iOT7CnaLdyyWVDAsDfIIRa7fanJzEZh+1xJ9Q3WrjbWGAL/BteoMlmsDBiVYw0oxI/PuRjPQ2c2CHIxgdOvi7pKLtJQVNrYx4CsaJJ/r6pie7Wy34utaa+cJjdUlZd1TmYYLegQ2jhgYdsI42huGedW/PHn8LA5IzyhuaThdrBSPNiKSi69BBtzKCTdDitjBMXppILAzjkorWwoDECoYIXH65Ll+gYCTDJVVUpBPC7dsXXX97dy+pgwetOyoTCXTZGPbu1aIRyzznbdv695ICn2AkI34BvsaiqQusYKQZkVR0EybA4sXBWxvuoHckguG2MHJzo7tBAgcNGuKdsuOOO2DtWp+otWrlc4UlWjDAF8eIpiWZn6//v9bCyFyC5ZOKJS2Ie987d3pbGMkSDPNcGcGwvaTSjEhcUq1awVmeSdc17qB3tBZGNNYFJMclBVqYjjnG990dCEy0Swp8ghFNxWCsQxPDsBZG5hFMMGJJPOjed0WFdiV7xTCSQaBg2F5SaUY8xigYYrUwqquji1+4j51oCyMQt2Akw8Iwge9oKwYjGNbCyEyCTdMaSx4p977Ly/2PY11SFj/iGayNxcKor4fKyugtjGCC4Z6nIhEkWzBicUmBb5pWa2FkJol0SbVrl3rB8LIwbGqQNCKeFka0QW8TE/nuu+gtjGS5pAJxdwBIhksqXhZGba21MDKRRLukTIMrnVxSVjDSiHj27nG7pMwFD9fCAC0Y0cwW5j52MJdUogYdJdvCKC3VYyei7eZYVKQ7F9TXW8HIRJoTjFhdUoGfe/bU95s7bpdIrGCkOYmwMExG1/z88CwGU/lVVESfJjqUYBQUJO6mS7ZgtGgBr74K/fpFt717Xm/rkso8zD1mBMJgXFLRNrgCtzWfjzxSd0hJxr0NWdZLSkQ6iMgiEdnsvHsagCIy0Vlns4hMdC1fKiL/cGbbWysinZ3lBSIyR0T+KSIfiUiPWMoZLgcPagWPl2Dk5urRw8YlZfJENYexMGpqEtNLKpGuomQLBsCFF/oGEEaKDXpnNsXF0LUrvP++//KdO/XzFsvMdF6CAcm7ryH7ekndBixRSvUGljjf/RCRDsBUYDBwMjA1QFguV0qVOa9tzrKrgB1KqV7A/cC9MZYzLEyAK54VakGBL+gd7ix3bvdKInpJJfKGT3a32lix3Wozm5wcndNs4UJ/KyOWtCAG9/Mai6USC0YwTNaHVLukYhWMCwAnwTXPARd6rHMOsEgptV0ptQNYBJwbwX5fA4aLJDZzS2UljBmjK7wRI+K33xYt/C2McHAH6hIhGNlmYcSCtTAyn/Hj9fV74w3fslgSDxpSZVW4ycvTopguMYxYBeNwpVQFgPPulQW/G/Ct63u5s8zwjOOO+o1LFBq3UUrVAbuAhPVL2LEDRo6EL7+Et96CPn3it29jYRgTORzcaS4y1SWVyDhJPLHdajOfQYO0S3L2bN+yWObCMBjBKCpKXVJKEV0HZIxgiMhiEVnv8bogzGN4WQbKeb9cKXUCcJrzuiKMbQLLd42IrBKRVZWVlWEWyZ8//hE++wz+/Gc444yodhGUgoLILQwRn1sqUy2MTHBHga+XlLUwMhcRuOwyWLIEtm7Vy+LhkjKCkSp3lCGjBEMpdbZSqq/Haz6wVUS6ADjv2zx2UQ64pw0qAb5z9v0v530P8DI6xuG3jYjkAe2A7UHKN0spNVApNfCw5iabCMLUqfDBB3DOOVFtHpJoXFLgax3Fe+BeogUjN1dXvJngjgIbw8gWxo/X+ctefVV/j6dLKtX3slswMrqXFPAmYHo9TQTme6zzNjBSRNo7we6RwNsikicinQBEJB8YBaz32O8lwLtKKU8LIx7k5cHJJze/XjREE/SG2C2MVLmkQFsZqX7IwqWoSP+P9u2zFkYm07evHvW/cKH+Hg+XlHle08XCUEon9szkXlLTgBEishkY4XxHRAaKyJMASqntwF3ASuf1386yArRwrAPWAv8CnnD2+xTQUUT+CdyMR++rTKFFC+3y2LPn0LAwIPMEA/TALysYmc0ZZ8CyZdpa3Ls3+1xSdXX6e8YO3FNKVQHDPZavAq52fX8aeDpgnX3ASUH2ewAYF0vZ0oWCAqiqCn/yJEM8Yxhr1uib7IQTkiMYRUWZFcMwWJdUZjNsGMya5RuTEatgmEZPugiG8RZkskvK0gwFBbrLLiTXwjA31euvw+DBMG6cTn9RXZ34ynz06MTEgxKBW5CthZHZDBum3+c7jvFYXVIFBfplBcOHTQ2SYFq0gG1OV4BIWjzxsjAWLdIPzj/+AevW6WWJFoz77kvs/uOJtTCyh27ddK6nN9/U32O1MEAntzQJLlNFy5bpIxjWwkgwBQW+lOLJtDBattTCcP758Le/6WVz5+r3THEXJQO3YFgLI/M5/XRfxoZ4CMbKlfDrX8e+n1gwFoaJR1oLI4txt1qT3Uvq66/1Q5OTo3uQWMFoirUwsothw+CZZ/TnWF1SEH2DLZ4EuqQyuZeUpRncrdZkWhigRSfHucLnn69HskPm9GBKBtbCyC5MHAPiY2GkA+kUw7CCkWCitTC6d9fvnb2SrUTBeef5PlsLw4cVjOyiRw/fs2MFI/5YwUgw0VoYffvC+vUwZEh8ynHaaT6hsILhw7qkso/TT9eVaqImCUs2VjAOIYxg5OdH7l46/vjw5s8Itxxnn60/W8HwYbvVZh9Tp8KLL8bv2Uk1VjAOIUyrNdzJkxLJuHG6PNHO4peNWAsj++jVS8+RkS0UFvoSZIINemc1ptUaiTsqUYwfD//6V/ImsM8EbAzDku4UFurEimYqYdutNoHU1tZSXl7OAZPuMcmMHq19qi1awMaNKSlCE6LMAh8XCgsLKSkpIT9NJsvIz9cZduvrrYVhSU+MK9uM57KCkUDKy8tp06YNPXr0IMGT9nlSUaFb9W3awDHHJP3waYVSiqqqKsrLyyktLU11cQDtJiwq0g+jtTAs6Ug6CUbWu6QOHDhAx44dUyIW4ItbxDIZfbYgInTs2DFl1l4wjFvKWhiWdMQKRpJJlViAb+CcFQxNKq9FMExPKWthWNIRKxiHENbCSH+MhWEFw5KOBAqG7SWVxRjB2Lq1nAsuuIDevXvTs2dPJk+eTE3g7EbAd999xyWXXNLsfs877zx27twZVZnuvPNOZsyYEdW22Yh1SVnSGSMYu3fr94y1MESkg4gsEpHNzrtnui8Rmeiss1lEJjrL2ojIWtfrexF5wPltkohUun672mu/mUBOjg72XnPNWC688EI2b97Mpk2b2Lt3L7fffrvfunV1dXTt2pXXXnut2f3+9a9/pThbch+kGGthWNIZ4zJNB5dUrL2kbgOWKKWmichtzvdfulcQkQ7AVGAgoIDVIvKmUmoHUOZabzUwz7XpHKXUz2Isnx9TpsDatfHcI5SVwQMPBP9dBFaufJfCwkJ++tOfApCbm8v9999PaWkppaWlvPfeexw4cIB9+/bx9NNPM2rUKNavX091dTWTJk3i888/57jjjmPLli3MnDmTgQMH0qNHD1atWsXevXv50Y9+xNChQ1m+fDndunVj/vz5tGzZkieeeIJZs2ZRU1NDr169eOGFFyhyDzywANbCsKQ32RTDuAB4zvn8HHChxzrnAIuUUtsdkVgEnOteQUR6A52BD2IsT9ohAl9+uYGyMv/ZaNu2bUv37t2pq6tjxYoVPPfcc7z77rt+6zzyyCO0b9+edevW8Zvf/IbVq1d7HmPz5s3ceOONbNiwgeLiYl5//XUAxo4dy8qVK/nkk0847rjjeOqppxJzkhmOtTAs6Uw6CUasFsbhSqkKAKVUhYh45VbtBnzr+l7uLHMzHm1RKNeyi0XkdGAT8HOl1Ld4ICLXANcAdDdpKoMQyhJIFMYllZvbtHeQUgoRYcSIEXQwE2C4WLZsGZMnTwagb9++9OvXz/MYpaWllJVpY+2kk05iy5YtAKxfv5477riDnTt3snfvXs7JlHlTk4y1MCzpTDoJRrMWhogsFpH1Hq8LwjyGVz9KFfD9MmC26/sCoIdSqh+wGJ8V03RHSs1SSg1USg087LDDwixS8mjVCgYOPJ5161b5Ld+9ezfffvstubm5tAqSVtNfP4NT4Goa5+bmUldXB8CkSZN4+OGH+fTTT5k6dWrajX9IF1q21MKel/XDWC2ZSEb1klJKna2U6uvxmg9sFZEuAM77No9dlAM/cH0vAb4zX0SkP5CnlGr0tyilqpRSB52vTwD+/pwMIjcXLrtsONXV1Tz//PMA1NfX84tf/IJJkyaFjCkMHTqUuc40eZ999hmffvppRMfes2cPXbp0oba2lpdeein6k8hyioqsO8qSvmRNLyngTWCi83kiMN9jnbeBkSLS3ulFNdJZZhiPv3VhxMcwBkiTLEzRISK88cYbvPrqq/Tu3Zujjz6awsJC7rnnnpDb3XDDDVRWVtKvXz/uvfde+vXrR7sIshjeddddDB48mBEjRnDsscfGehpZy09/Cn/8Y6pLYbF4E2hhpNISlnDdHp4bi3QE5gLdgW+AcUqp7SIyELhOKXW1s96VgJlK/W6l1DOufXwJnKeU+ty17A9ooagDtgPXu38PxsCBA9WqVf6un40bN3LcccdFfY6ppL6+ntraWgoLC/niiy8YPnw4mzZtokWGO9sz+ZpYLMlm3z49h40RCjMvRjwRkdVKqYHNrReTVimlqoDhHstXAVe7vj8NPB1kH0d5LPsV8KtYypYNVFdXc+aZZ1JbW4tSikcffTTjxcJisUSGcZfW1flP+JUKbJgvjWnTpg2BFpPFYjm0yMvTr7q61MYvwKYGsVgslrTHxDFS7WCwgmGxWCxpjhEMa2FYLBaLJSRWMCwWi8USFlYwDiFyc3MpKyujb9++jB49utm05Fu2bKFv374ALF26lFGjRgHw5ptvMm3atLiUyQwa3GM6dwOTJ09GRPj+++/jcgyLxRIfrGAcQrRs2ZK1a9eyfv16OnTowMyZM6Paz5gxY7jtttviVq5evXoxf74ea9nQ0MB7771Ht26Bab4sFkuqMd1pUy0Yh1a32lTkNw/g1FNPZd26dYDOFXXrrbeycOFCRIQ77riDSy+9NOi2zz77LKtWreLhhx9m0qRJtG3bllWrVvHvf/+b6dOnc8kll9DQ0MDPfvYz3n//fUpLS2loaODKK6/0nJRp/PjxzJkzhwkTJrB06VKGDBnCwoULG39/8cUXefDBB6mpqWHw4ME88sgj5Obmcv3117Ny5Ur279/PJZdcwu9+9zsAevTowcSJE1mwYAG1tbW8+uqrdoS5xRIHbC+pQ5D6+nqWLFnCmDFjAJg3bx5r167lk08+YfHixdxyyy1UVFSEvb+KigqWLVvGW2+91Wh5zJs3jy1btvDpp5/y5JNPsmLFiqDb9+7dm8rKSnbs2MHs2bO57LLLGn/buHEjc+bM4cMPP2Tt2rXk5uY25qO6++67WbVqFevWreP9999vFECATp06sWbNGq6//no7q5/FEifSxSV1aFkYqchvDuzfv5+ysjK2bNnCSSedxIgRIwCdvnz8+PHk5uZy+OGHM2zYMFauXBk0jXkgF154ITk5OfTp04etW7c27nPcuHHk5ORwxBFHcOaZZ4bcx9ixY3nllVf46KOPePzxxxuXL1myhNWrVzNo0KDGc+jcWWevnzt3LrNmzaKuro6Kigo+++yzxjKPHTsW0GnW582bh8ViiR0rGIcQJoaxa9cuRo0axcyZM7npppvCTl8eDHdac7OvSPd52WWXceKJJzJx4kRycnwGp1KKiRMn8oc//MFv/a+++ooZM2awcuVK2rdvz6RJk/zSppsyudOsWyyW2EgXwbAuqSTSrl07HnzwQWbMmEFtbS2nn346c+bMob6+nsrKSv72t79x8sknx3SMoUOH8vrrr9PQ0MDWrVtZunRpyPW7d+/O3XffzQ033OC3fPjw4bz22mts26Yz1m/fvp2vv/6a3bt306pVK9q1a8fWrVv9Yh4WiyUxpItgWAsjyQwYMID+/fvzyiuvMGHCBFasWEH//v0REaZPn84RRxzROGNeNFx88cUsWbKEvn37cvTRRzN48OBmU6Jfe+21TZb16dOH3//+94wcOZKGhgby8/OZOXMmp5xyCgMGDOD444/nqKOOYsiQIVGX1WKxhEe6CEZM6c3TjWxLbx4te/fupXXr1lRVVXHyySfz4YcfcsQRR6S6WI0citfEYomFm26Chx6CsWPh9dfjv/+kpDe3pCejRo1i586d1NTU8Jvf/CatxMJisUROulgYVjCykObiFhaLJbNIF8GIKegtIh1EZJGIbHbe2wdZ739FZKeIvBWwvFREPnK2nyMiLZzlBc73fzq/94ilnBaLxZLJZIVgALcBS5RSvYElzncv7gOu8Fh+L3C/s/0O4Cpn+VXADqVUL+B+Zz2LxWI5JMkWwbgAeM75/BxwoddKSqklwB73MhER4CzgNY/t3ft9DRjurG+xWCyHHNkiGIcrpSoAnPfOEWzbEdiplDKju8oBk/muG/Cts986YJezfhNE5BoRWSUiqyorK6M4BYvFYklvMiaXlIgsFpH1Hq8LYjy2l8WgwvjNf6FSs5RSA5VSAw877LAYi5QY0jG9+f/93/8xePBgysrKOO6447jzzjsbj7d8+fKg27Vu3Toux7dYLOGTLhZGs72klFJnB/tNRLaKSBelVIWIdAG2RXDs74FiEclzrIgS4Dvnt3LgB0C5iOQB7YDtEew7rTCpQQAmTpzIzJkzuf322yPez5gxYxoTF8bKxIkTmTt3Lv3796e+vp5//OMfgBaM1q1b88Mf/jAux7FYLLGTLenN3wQmAtOc9/nhbqiUUiLyHnAJ8ErA9ma/K5zf31VxGGE4ZcqUxoo7XpSVlfFABqY337ZtG126dAG0BdSnTx+2bNnCY489Rm5uLi+++CIPPfQQJSUl/OQnP6Guro5zzz03iv+QxWKJlXSxMGKNYUwDRojIZmCE8x0RGSgiT5qVROQD4FV08LpcRM5xfvolcLOI/BMdo3jKWf4U0NFZfjPBe19lFOmU3vznP/85xxxzDBdddBGPP/44Bw4coEePHlx33XX8/Oc/Z+3atZx22mlMnjy5cf4LOwDQYkkN6SIYMVkYSqkqYLjH8lXA1a7vpwXZ/kugSbY9pdQBYFwsZfMiEksgnqRjevPf/va3XH755bzzzju8/PLLzJ4923PA34cffsjrTi6CK664gl/+8pdR/AcsFksspItg2Gy1ScDEML7++mtqamoap2hNdXrznj17cv3117NkyRI++eQTqqqqPNezPZotltSSMb2kLPEjndKb/+Uvf2kUl82bN5Obm0txcTFt2rRhzx7fkJkhQ4bwyiuvADTOuGexWJKLtTAOUdzpzS+66CL69etH//79OeussxrTm8fCxRdfTElJCX379uXaa68Nmt78hRde4JhjjqGsrIwrrriCl156idzcXEaPHs0bb7xBWVkZH3zwAX/605+YOXMmgwYNYteuXTGVzWKxREe6CIZNb56F2PTmFkt20dAAU6fC9ddD167x379Nb34IY9ObWyzZRU4O3HVXqkthBSMrsenNLRZLIjgkYhjZ5HbLdOy1sFgyl6wXjMLCQqqqqmxFlQYopaiqqqLQRPAsFktGkfUuqZKSEsrLy7GZbNODwsJCSkpKUl0Mi8USBVkvGPn5+ZSWlqa6GBaLxZLxZL1LymKxWCzxwQqGxWKxWMLCCobFYrFYwiKrRnqLSCXwdZIP2wk9GVS2Ys8vs8nm88vmc4Pknt+RSqlmpyzNKsFIBSKyKpwh9ZmKPb/MJpvPL5vPDdLz/KxLymKxWCxhYQXDYrFYLGFhBSN2ZqW6AAnGnl9mk83nl83nBml4fjaGYbFYLJawsBaGxWKxWMLCCobFYrFYwsIKhgci8rSIbBOR9a5l/UVkhYh8KiILRKSts7yHiOwXkbXO6zHXNic56/9TRB4UEUnF+biJx7mJSJGI/EVEPheRDSIyLVXnE0i8rp1r2zfd+0o1cbw3W4jILBHZ5FzHi1NxPoHE8fzGO+uvE5H/FZFOqTgfN5Gcm/NbP+e3Dc7vhc7y1NUrSin7CngBpwMnAutdy1YCw5zPVwJ3OZ97uNcL2M/fgVMBARYCP8qGcwOKgDOdzy2AD9Lh3OJ57ZzfxwIvh1onU88P+B3we+dzDtAp1ecWr/NDJ1XdZs4JmA7cmWHnlgesA/o73zsCuc7nlNUr1sLwQCn1N2B7wOJjgL85nxcBIVtkItIFaKuUWqH0VX4euDDeZY2UeJybUqpaKfWe87kGWAOkRc7yeJwfgIi0Bm4Gfh/XAsZIvM4PXTn9wdlng1IqLUZMx+n8xHm1clrfbYHv4lnOaIjw3EYC65RSnzjbViml6lNdr1jBCJ/1wBjn8zjgB67fSkXkYxF5X0ROc5Z1A8pd65Q7y9KRSM+tEREpBkYDSxJfzKiJ5vzuAv4HqE5SGWMhovNzrhnAXSKyRkReFZHDk1jeSIno/JRStcD1wKdooegDPJXE8kZCsHM7GlAi8rZzjW51lqe0XrGCET5XAjeKyGqgDVDjLK8AuiulBqBbpC87fkgvv2K69mGO9NwAEJE8YDbwoFLqyySXORIiOj8RKQN6KaXeSE1xIybS65eHtgg/VEqdCKwAZiS/2GET6fXLRwvGAKAr2rXzq+QXOyyCnVseMBS43Hm/SESGk+J6JesnUIoXSqnP0WYiInI0cL6z/CBw0Pm8WkS+QLcOyvF305SQBmaxF1Gc2ypn01nAZqXUA0kvdAREcX6DgJNEZAv6GeksIkuVUmckv/TNE8X5rUZbTkYQXwWuSnKxwyaK8xNn2RfONnOB25Jf8uYJdm7o+uN94yoUkb+i4x8vksJ6xVoYYSIinZ33HOAOwPQYOkxEcp3PRwG9gS+VUhXAHhE5xfGj/j9gfkoK3wyRnpvz/fdAO2BKKsocCVFcu0eVUl2VUj3QrbtN6SoWENX5KWABcIazi+HAZ0kudthEcX/+C+gjIib76ghgY7LLHQ7Bzg14G+gnukdiHjAM+Czl9Uqqew6k4wvtZqkAatFKfxUwGdjkvKbhGyV/MbAB+AQd/B3t2s9AtI/yC+Bhs02mnxu6VaPQD+Fa53V1qs8tntfOtb8epFcvqXjdm0eig63r0PGn7qk+tzif33XO/bkOLY4dM+ncnPUnOOe3HpjuWp6yesWmBrFYLBZLWFiXlMVisVjCwgqGxWKxWMLCCobFYrFYwsIKhsVisVjCwgqGxWKxWMLCCobFYrFYwsIKhsVisVjC4v8D3pt0tuwLiOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234ea63e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -6.332387e+00\n",
      "p-value                        2.885059e-08\n",
      "#Lags Used                     9.000000e+00\n",
      "Number of Observations Used    1.220000e+02\n",
      "Critical Value (5%)           -2.885538e+00\n",
      "Critical Value (1%)           -3.485122e+00\n",
      "Critical Value (10%)          -2.579569e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "decomposedLogData = residual\n",
    "decomposedLogData.dropna(inplace=True)\n",
    "test_stationarity(decomposedLogData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c4a0310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "\n",
    "lag_acf = acf(datasetLogDiffShifting, nlags=20)\n",
    "lag_pacf = pacf(datasetLogDiffShifting, nlags=20, method='ols')\n",
    "\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(datasetLogDiffShifting)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(datasetLogDiffShifting)),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(datasetLogDiffShifting)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(datasetLogDiffShifting)),linestyle='--',color='gray')\n",
    "plt.title('Partial Autocorrelation Function')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting AR model\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c496c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "#AR MODEL\n",
    "model = ARIMA(indexedDataset_logScale, order=(2, 1, 0))  \n",
    "results_AR = model.fit(disp=-1)  \n",
    "plt.plot(datasetLogDiffShifting)\n",
    "plt.plot(results_AR.fittedvalues, color='red')\n",
    "plt.title('RSS: %.4f'% sum((results_AR.fittedvalues-datasetLogDiffShifting[\"#Passengers\"])**2))\n",
    "print('Plotting AR model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results_AR.fittedvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting AR model\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c3b92d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARIMA(indexedDataset_logScale, order=(0, 1, 2))  \n",
    "results_MA = model.fit(disp=-1)  \n",
    "plt.plot(datasetLogDiffShifting)\n",
    "plt.plot(results_MA.fittedvalues, color='red')\n",
    "plt.title('RSS: %.4f'% sum((results_MA.fittedvalues-datasetLogDiffShifting[\"#Passengers\"])**2))\n",
    "print('Plotting AR model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_MA.fittedvalues.to_csv('s.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'RSS: 1.0292')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c393790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARIMA(indexedDataset_logScale, order=(2, 1, 2))  \n",
    "results_ARIMA = model.fit(disp=-1)  \n",
    "plt.plot(datasetLogDiffShifting)\n",
    "plt.plot(results_ARIMA.fittedvalues, color='red')\n",
    "plt.title('RSS: %.4f'% sum((results_ARIMA.fittedvalues-datasetLogDiffShifting[\"#Passengers\"])**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetLogDiffShifting.to_csv(\"datasetLogDiffShifting.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_ARIMA.fittedvalues.to_csv(\"results_ARIMA.fittedvalues.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-02-01    0.009580\n",
       "1949-03-01    0.017491\n",
       "1949-04-01    0.027670\n",
       "1949-05-01   -0.004521\n",
       "1949-06-01   -0.023890\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)\n",
    "predictions_ARIMA_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_ARIMA_diff.to_csv(\"predictions_ARIMA_diff.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-02-01    0.009580\n",
       "1949-03-01    0.017491\n",
       "1949-04-01    0.027670\n",
       "1949-05-01   -0.004521\n",
       "1949-06-01   -0.023890\n",
       "1949-07-01   -0.004347\n",
       "1949-08-01   -0.010978\n",
       "1949-09-01   -0.029929\n",
       "1949-10-01   -0.034467\n",
       "1949-11-01   -0.011279\n",
       "1949-12-01    0.037212\n",
       "1950-01-01    0.068135\n",
       "1950-02-01    0.089434\n",
       "1950-03-01    0.090029\n",
       "1950-04-01    0.066152\n",
       "1950-05-01    0.044053\n",
       "1950-06-01    0.035467\n",
       "1950-07-01    0.003100\n",
       "1950-08-01   -0.047127\n",
       "1950-09-01   -0.086353\n",
       "1950-10-01   -0.098465\n",
       "1950-11-01   -0.067846\n",
       "1950-12-01   -0.005228\n",
       "1951-01-01    0.017385\n",
       "1951-02-01    0.034016\n",
       "1951-03-01    0.042561\n",
       "1951-04-01    0.018748\n",
       "1951-05-01    0.015260\n",
       "1951-06-01    0.002489\n",
       "1951-07-01   -0.012408\n",
       "                ...   \n",
       "1958-07-01    0.054477\n",
       "1958-08-01   -0.016466\n",
       "1958-09-01   -0.079842\n",
       "1958-10-01   -0.083143\n",
       "1958-11-01   -0.058869\n",
       "1958-12-01   -0.002663\n",
       "1959-01-01    0.031199\n",
       "1959-02-01    0.051445\n",
       "1959-03-01    0.079806\n",
       "1959-04-01    0.065334\n",
       "1959-05-01    0.058190\n",
       "1959-06-01    0.036074\n",
       "1959-07-01   -0.006667\n",
       "1959-08-01   -0.069020\n",
       "1959-09-01   -0.116861\n",
       "1959-10-01   -0.109304\n",
       "1959-11-01   -0.069265\n",
       "1959-12-01   -0.003643\n",
       "1960-01-01    0.034219\n",
       "1960-02-01    0.066011\n",
       "1960-03-01    0.105113\n",
       "1960-04-01    0.117957\n",
       "1960-05-01    0.104054\n",
       "1960-06-01    0.081795\n",
       "1960-07-01    0.031718\n",
       "1960-08-01   -0.041176\n",
       "1960-09-01   -0.092350\n",
       "1960-10-01   -0.094013\n",
       "1960-11-01   -0.069924\n",
       "1960-12-01   -0.008127\n",
       "Length: 143, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-02-01    0.009580\n",
       "1949-03-01    0.027071\n",
       "1949-04-01    0.054742\n",
       "1949-05-01    0.050221\n",
       "1949-06-01    0.026331\n",
       "1949-07-01    0.021984\n",
       "1949-08-01    0.011007\n",
       "1949-09-01   -0.018922\n",
       "1949-10-01   -0.053389\n",
       "1949-11-01   -0.064668\n",
       "1949-12-01   -0.027456\n",
       "1950-01-01    0.040679\n",
       "1950-02-01    0.130113\n",
       "1950-03-01    0.220142\n",
       "1950-04-01    0.286294\n",
       "1950-05-01    0.330347\n",
       "1950-06-01    0.365815\n",
       "1950-07-01    0.368915\n",
       "1950-08-01    0.321787\n",
       "1950-09-01    0.235435\n",
       "1950-10-01    0.136970\n",
       "1950-11-01    0.069123\n",
       "1950-12-01    0.063896\n",
       "1951-01-01    0.081281\n",
       "1951-02-01    0.115296\n",
       "1951-03-01    0.157858\n",
       "1951-04-01    0.176605\n",
       "1951-05-01    0.191865\n",
       "1951-06-01    0.194354\n",
       "1951-07-01    0.181946\n",
       "                ...   \n",
       "1958-07-01    1.166556\n",
       "1958-08-01    1.150090\n",
       "1958-09-01    1.070249\n",
       "1958-10-01    0.987106\n",
       "1958-11-01    0.928236\n",
       "1958-12-01    0.925574\n",
       "1959-01-01    0.956773\n",
       "1959-02-01    1.008218\n",
       "1959-03-01    1.088024\n",
       "1959-04-01    1.153358\n",
       "1959-05-01    1.211548\n",
       "1959-06-01    1.247623\n",
       "1959-07-01    1.240956\n",
       "1959-08-01    1.171936\n",
       "1959-09-01    1.055074\n",
       "1959-10-01    0.945770\n",
       "1959-11-01    0.876505\n",
       "1959-12-01    0.872862\n",
       "1960-01-01    0.907081\n",
       "1960-02-01    0.973091\n",
       "1960-03-01    1.078204\n",
       "1960-04-01    1.196161\n",
       "1960-05-01    1.300215\n",
       "1960-06-01    1.382011\n",
       "1960-07-01    1.413729\n",
       "1960-08-01    1.372553\n",
       "1960-09-01    1.280203\n",
       "1960-10-01    1.186190\n",
       "1960-11-01    1.116266\n",
       "1960-12-01    1.108139\n",
       "Length: 143, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_diff_cumsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_ARIMA_diff_cumsum.to_csv(\"predictions_ARIMA_diff_cumsum.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "predictions_ARIMA_log = pd.Series(indexedDataset_logScale['#Passengers'].ix[0], index=indexedDataset_logScale['#Passengers'].index)\n",
    "predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01    4.718499\n",
       "1949-02-01    4.728079\n",
       "1949-03-01    4.745570\n",
       "1949-04-01    4.773241\n",
       "1949-05-01    4.768720\n",
       "1949-06-01    4.744830\n",
       "1949-07-01    4.740483\n",
       "1949-08-01    4.729506\n",
       "1949-09-01    4.699577\n",
       "1949-10-01    4.665110\n",
       "1949-11-01    4.653831\n",
       "1949-12-01    4.691043\n",
       "1950-01-01    4.759178\n",
       "1950-02-01    4.848612\n",
       "1950-03-01    4.938641\n",
       "1950-04-01    5.004793\n",
       "1950-05-01    5.048846\n",
       "1950-06-01    5.084313\n",
       "1950-07-01    5.087414\n",
       "1950-08-01    5.040286\n",
       "1950-09-01    4.953933\n",
       "1950-10-01    4.855468\n",
       "1950-11-01    4.787622\n",
       "1950-12-01    4.782395\n",
       "1951-01-01    4.799779\n",
       "1951-02-01    4.833795\n",
       "1951-03-01    4.876356\n",
       "1951-04-01    4.895104\n",
       "1951-05-01    4.910364\n",
       "1951-06-01    4.912853\n",
       "                ...   \n",
       "1958-07-01    5.885055\n",
       "1958-08-01    5.868589\n",
       "1958-09-01    5.788748\n",
       "1958-10-01    5.705604\n",
       "1958-11-01    5.646735\n",
       "1958-12-01    5.644072\n",
       "1959-01-01    5.675271\n",
       "1959-02-01    5.726717\n",
       "1959-03-01    5.806523\n",
       "1959-04-01    5.871857\n",
       "1959-05-01    5.930047\n",
       "1959-06-01    5.966122\n",
       "1959-07-01    5.959455\n",
       "1959-08-01    5.890435\n",
       "1959-09-01    5.773573\n",
       "1959-10-01    5.664269\n",
       "1959-11-01    5.595004\n",
       "1959-12-01    5.591361\n",
       "1960-01-01    5.625580\n",
       "1960-02-01    5.691590\n",
       "1960-03-01    5.796703\n",
       "1960-04-01    5.914660\n",
       "1960-05-01    6.018714\n",
       "1960-06-01    6.100510\n",
       "1960-07-01    6.132227\n",
       "1960-08-01    6.091051\n",
       "1960-09-01    5.998702\n",
       "1960-10-01    5.904688\n",
       "1960-11-01    5.834765\n",
       "1960-12-01    5.826638\n",
       "Length: 144, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_ARIMA_log.to_csv(\"predictions_ARIMA_log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'RMSE: 90.1046')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c496350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions_ARIMA = np.exp(predictions_ARIMA_log)\n",
    "plt.plot(indexedDataset)\n",
    "plt.plot(predictions_ARIMA)\n",
    "predictions_ARIMA.to_csv(\"predictions_ARIMAs.csv\")\n",
    "plt.title('RMSE: %.4f'% np.sqrt(sum((predictions_ARIMA-indexedDataset[\"#Passengers\"])**2)/len(indexedDataset[\"#Passengers\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # To visualize\n",
    "import pandas as pd  # To read data\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('predictions_ARIMASS.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>112.000000</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113.078122</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>115.073416</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>118.301993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>117.768370</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>114.988278</td>\n",
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       "      <th>6</th>\n",
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       "      <td>114.489526</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>113.239584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>109.900662</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>106.177217</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>104.986416</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>108.966778</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>116.649987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>127.563195</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>149.126196</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>155.842561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>161.469037</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>161.970399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>154.514241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>141.731366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>128.440835</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>120.015650</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>119.389905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>121.483624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>125.687052</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>131.151924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>133.633908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>135.688787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>136.026964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>115</td>\n",
       "      <td>359.622398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>116</td>\n",
       "      <td>353.749511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>326.603706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>118</td>\n",
       "      <td>300.547086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>119</td>\n",
       "      <td>283.364761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>120</td>\n",
       "      <td>282.611297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>121</td>\n",
       "      <td>291.567454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>306.959710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>123</td>\n",
       "      <td>332.461140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>354.907520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>125</td>\n",
       "      <td>376.172214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>389.990167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>387.398889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>361.562412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>321.685134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>288.377142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>269.078730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>268.100161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>277.433065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>296.364569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>329.212442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>370.428376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>411.049736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>446.085084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>460.460641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>441.885781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>402.905311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>366.752938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>341.984252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>339.216224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       X           Y\n",
       "0      1  112.000000\n",
       "1      2  113.078122\n",
       "2      3  115.073416\n",
       "3      4  118.301993\n",
       "4      5  117.768370\n",
       "5      6  114.988278\n",
       "6      7  114.489526\n",
       "7      8  113.239584\n",
       "8      9  109.900662\n",
       "9     10  106.177217\n",
       "10    11  104.986416\n",
       "11    12  108.966778\n",
       "12    13  116.649987\n",
       "13    14  127.563195\n",
       "14    15  139.580451\n",
       "15    16  149.126196\n",
       "16    17  155.842561\n",
       "17    18  161.469037\n",
       "18    19  161.970399\n",
       "19    20  154.514241\n",
       "20    21  141.731366\n",
       "21    22  128.440835\n",
       "22    23  120.015650\n",
       "23    24  119.389905\n",
       "24    25  121.483624\n",
       "25    26  125.687052\n",
       "26    27  131.151924\n",
       "27    28  133.633908\n",
       "28    29  135.688787\n",
       "29    30  136.026964\n",
       "..   ...         ...\n",
       "114  115  359.622398\n",
       "115  116  353.749511\n",
       "116  117  326.603706\n",
       "117  118  300.547086\n",
       "118  119  283.364761\n",
       "119  120  282.611297\n",
       "120  121  291.567454\n",
       "121  122  306.959710\n",
       "122  123  332.461140\n",
       "123  124  354.907520\n",
       "124  125  376.172214\n",
       "125  126  389.990167\n",
       "126  127  387.398889\n",
       "127  128  361.562412\n",
       "128  129  321.685134\n",
       "129  130  288.377142\n",
       "130  131  269.078730\n",
       "131  132  268.100161\n",
       "132  133  277.433065\n",
       "133  134  296.364569\n",
       "134  135  329.212442\n",
       "135  136  370.428376\n",
       "136  137  411.049736\n",
       "137  138  446.085084\n",
       "138  139  460.460641\n",
       "139  140  441.885781\n",
       "140  141  402.905311\n",
       "141  142  366.752938\n",
       "142  143  341.984252\n",
       "143  144  339.216224\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data.iloc[:, 1].values.reshape(-1, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, 0].values.reshape(-1, 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 1)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1],\n",
       "       [  2],\n",
       "       [  3],\n",
       "       [  4],\n",
       "       [  5],\n",
       "       [  6],\n",
       "       [  7],\n",
       "       [  8],\n",
       "       [  9],\n",
       "       [ 10],\n",
       "       [ 11],\n",
       "       [ 12],\n",
       "       [ 13],\n",
       "       [ 14],\n",
       "       [ 15],\n",
       "       [ 16],\n",
       "       [ 17],\n",
       "       [ 18],\n",
       "       [ 19],\n",
       "       [ 20],\n",
       "       [ 21],\n",
       "       [ 22],\n",
       "       [ 23],\n",
       "       [ 24],\n",
       "       [ 25],\n",
       "       [ 26],\n",
       "       [ 27],\n",
       "       [ 28],\n",
       "       [ 29],\n",
       "       [ 30],\n",
       "       [ 31],\n",
       "       [ 32],\n",
       "       [ 33],\n",
       "       [ 34],\n",
       "       [ 35],\n",
       "       [ 36],\n",
       "       [ 37],\n",
       "       [ 38],\n",
       "       [ 39],\n",
       "       [ 40],\n",
       "       [ 41],\n",
       "       [ 42],\n",
       "       [ 43],\n",
       "       [ 44],\n",
       "       [ 45],\n",
       "       [ 46],\n",
       "       [ 47],\n",
       "       [ 48],\n",
       "       [ 49],\n",
       "       [ 50],\n",
       "       [ 51],\n",
       "       [ 52],\n",
       "       [ 53],\n",
       "       [ 54],\n",
       "       [ 55],\n",
       "       [ 56],\n",
       "       [ 57],\n",
       "       [ 58],\n",
       "       [ 59],\n",
       "       [ 60],\n",
       "       [ 61],\n",
       "       [ 62],\n",
       "       [ 63],\n",
       "       [ 64],\n",
       "       [ 65],\n",
       "       [ 66],\n",
       "       [ 67],\n",
       "       [ 68],\n",
       "       [ 69],\n",
       "       [ 70],\n",
       "       [ 71],\n",
       "       [ 72],\n",
       "       [ 73],\n",
       "       [ 74],\n",
       "       [ 75],\n",
       "       [ 76],\n",
       "       [ 77],\n",
       "       [ 78],\n",
       "       [ 79],\n",
       "       [ 80],\n",
       "       [ 81],\n",
       "       [ 82],\n",
       "       [ 83],\n",
       "       [ 84],\n",
       "       [ 85],\n",
       "       [ 86],\n",
       "       [ 87],\n",
       "       [ 88],\n",
       "       [ 89],\n",
       "       [ 90],\n",
       "       [ 91],\n",
       "       [ 92],\n",
       "       [ 93],\n",
       "       [ 94],\n",
       "       [ 95],\n",
       "       [ 96],\n",
       "       [ 97],\n",
       "       [ 98],\n",
       "       [ 99],\n",
       "       [100],\n",
       "       [101],\n",
       "       [102],\n",
       "       [103],\n",
       "       [104],\n",
       "       [105],\n",
       "       [106],\n",
       "       [107],\n",
       "       [108],\n",
       "       [109],\n",
       "       [110],\n",
       "       [111],\n",
       "       [112],\n",
       "       [113],\n",
       "       [114],\n",
       "       [115],\n",
       "       [116],\n",
       "       [117],\n",
       "       [118],\n",
       "       [119],\n",
       "       [120],\n",
       "       [121],\n",
       "       [122],\n",
       "       [123],\n",
       "       [124],\n",
       "       [125],\n",
       "       [126],\n",
       "       [127],\n",
       "       [128],\n",
       "       [129],\n",
       "       [130],\n",
       "       [131],\n",
       "       [132],\n",
       "       [133],\n",
       "       [134],\n",
       "       [135],\n",
       "       [136],\n",
       "       [137],\n",
       "       [138],\n",
       "       [139],\n",
       "       [140],\n",
       "       [141],\n",
       "       [142],\n",
       "       [143],\n",
       "       [144]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "poly = PolynomialFeatures(degree = 4) \n",
    "X_poly = poly.fit_transform(X) \n",
    "  \n",
    "poly.fit(X_poly, Y) \n",
    "lin2 = LinearRegression() \n",
    "lin2.fit(X_poly, Y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c63f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color = 'blue') \n",
    "plt.plot(X, lin2.predict(poly.fit_transform(X)), color = 'red') \n",
    "plt.title('Polynomial Regression') \n",
    "plt.xlabel('Number of passangers') \n",
    "plt.ylabel('Month') \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fWL8exo41n1Ye5FtZLs64QDiO4wTMnWvrSALRvLnlUfrtt8Kff+1aKEc6fV/rb8md3n0XOnfO95j69W29YkXhr1tYXCAcx3EC5s6FihUjZ24tjpFMa9coQxnIgbPGwZAh1osogNB1Fy0q/HULiwuE4zhOwNy50KqVhQVyUhwCUfe5O7mUYfx+3X02YzoKqlWDAw90gXAcx4mJhQutqE9xEWkEU4hGjWx6wrJlhTz5E09w2KeP8QJXUvHh+2I6tGVL+60ljQuE4zgpyzXXQK9exeOf37TJhpPmVeq5fHmoVSsrl1JMvPYa3HILM5uexV37PkflKjmnUeePC4TjOE4MZGbC9OmWH2ngwKLPE5g3z9Z59SAA6tSBNWtiPPHYsXDZZdCzJ4+3e4PadcoWfEwOWrQwASvpoa4uEI7jpCQ//wxbt8Lhh8Pnn8Pw4UU7X34jmEIcdFCMPYhvvoH+/aF9e/joI9I2VIyqklxOWra0dUn3IuImECJSSUSmichsEZkvIg8E7cNE5BcRmRUs7YN2EZHBIrJUROaISMd42eY4Tuozfbqtn38ejjzS8tsVpRcxd64FhEPzDiIRUw9i3jw45RSoVw8++QT23TdyHqYoaNHC1iUdqI5nD2IX0FNV2wHtgZNEpHuw7VZVbR8ss4K23kCzYBkIvBBH2xzHSXFmzLAhqW3bwpln2pt9UeYohALUubKshnHQQbBunbm38mX5cksHu88+1r2pXRvII1FfFDRsaL91r+lBqBHKnl4+WPLT937Aa8Fx3wPVRKROvOxzHCe1mT7dPDfly2fNWyhsOm5Ve+HPz70E1oPIyChAiNavh7/9Df780yoPNWoEWBqPrVsLJxBly9ow272pB4GIlBWRWcB6YLyqTg02DQrcSE+JSMWgrS6wMuzwtKAt5zkHish0EZm+YcOGeJrvOE6SkpkJP/4InTrZ91C21cLWjV650h7e0QgE5BOH+CNIobFyJYwZk+2E69bZujACAeZm2mt6EACqultV2wP1gK4i0ha4E2gJdAEOAG4Pdo/UscvV41DVoaraWVU716pVK06WO46TzCxZYs/ikEA0aQJlyhReIEIB6ryGuIYIPdwjxiF27IB+/WDWLEuhceSR2TYXWIu6AFq2tDkYf/1VuOMLQ4mMYlLVzcBk4CRVXRO4kXYBrwJdg93SgPphh9UDVpeEfY7jpBYzZtg6lMaoQgXz08dbIPLsQaSn22ilyZNh2DALTuegqALRogXs3m2jt0qKeI5iqiUi1YLP+wDHAwtDcQUREeA0IBh9zMfARcFopu7AFlWNdcSx4zilgBkzoFIlaN06q61Zs6IJRP36NoopPyL2IHbvhgsvtPkO//0vXHBBxGNDAnHggYWzMTTUtThnjhdEhIwjxUYdYLiIlMWEaKSqjhGRSSJSC3MpzQKuDPb/BOgDLAX+BC6No22O46QwM2ZYgDo8Z1KzZjB1qgWc8xuJFIn8UmyEU7WqLXsEQhWuuALeeSernnQerF1rdhXWM96mDRxyCPz973b9224zt1o8iZtAqOocoEOE9p557K/ANfGyx3GcvYelS+GEE7K3NW0KW7bYbONYHsLp6Rb8jSKxKmBuprVrMXG46SZ4+WW45x649dZ8j1u71moClS8fvW3h7LMP/PCD6dGdd5pIPPNM4c4VLfHsQTiO4xQ7GRn2cKxfP3t7aKjrkiWxCcTixSYS0fQgwNxMa9YADzwATz8N118P//pXgccVdg5EONWrW2flpJPgqKOKdq5ocIFwHCelWLPGhrnWq5e9PVwgjjgi+vNFG6AOUacOdJj0BHz5gOVYevLJqHxaxSEQYJe67LKinycaXCAcx0kp0tJsnVMgGje2CWWxBqrnzrXjQkHggjh941DO2XiLjVoaOjTqQMDatVk1JVIFT9bnOE5KkZdAlC9vk5ZjnU09f749uCtWLHhfRozg7C+uZCx92D7kDVOWKFAtvh5ESeIC4ThOSpGXQEDhhrouWhRl7+GDD+DCC1nX/BjO4j3WbqoQ9TW2bIFdu1wgHMdx4kpaGlSubAHbnDRtagIRbVbXjAybeFag62fUKDj7bOjalXmPjGYn+8RUF2J1MOW3Topll3OBcBwnpUhLs95DpLhws2aWgmP9+ujOtXy5jWAKpdOOyNixFm/o2BE++4zah+wLxFYX4tdfbd2gQfTHJAMuEI7jpBQrV0Z2L0H2kUzRsHixrfPsQYwbB2ecAYcdZp/32y//fEx54ALhOI5TAoR6EJGIVSBC6bMj9iAmTLDke61bW02HIA9HzZoWm46lB7FypR3jLibHcZw4sXu3+fPzEohGjSz9RrQjmRYvtlhGjRo5NnzxBZx6qinHhAlwwAF7NpUpY8Hm1TGkEv31Vzj44OypQVIBFwjHcVKGdetMJPISiHLlTCRicTE1b54jnvHll5aNtUkTE4dc6mGzuFeuzNWcJ7/+mnruJXCBcBwnhQgNcc2ZZiOcWIa6LlqUw7307beWlKlBA5g4Mc+cHS4QjuM4SUZ+cyBChASioKGu27bBqlVhAervv7ckR3XrwqRJ+eblbtDAHvrRDKfNzDQxcYFwHMeJI9EKxPbtBQeRQ72MFi2Ar7+2OtK1a5s4FBBNrl8fdu4soDZ1wPr1NpTWBcJxHCeOrFxpKTEihAX2EBrJVFCgOjTEtcOWydZzqFMHpkyxHkQBhB72oeGr+ZGqQ1zBBcJxnBQiv0lyIZo2tXVBcYhFi6AXE2jyzz5WrzRKcYCsGEg0cYiQQOQXN0lWXCAcx0kZ0tIKftA2bGijmQoSiEqTP2MMpyBNm9qw1hgSJYVs8B6E4zhOkpDfJLkQ5crZCNV8BWL0aG6Y3I+V+7Y2cahdOyY7atUyV1e0PYiqVQuud52MxE0gRKSSiEwTkdkiMl9EHgjaG4vIVBFZIiLviEiFoL1i8H1psL1RvGxzHCf1yMy0UUcFCQQUMNT1gw/QM85glrbjvSsn5h/QyIMyZcyOaAQiNIIp1jrZyUDUAiEiR4jIeSJyUWgp4JBdQE9VbQe0B04Ske7AY8BTqtoM+B24PNj/cuB3VW0KPBXs5ziOA8CGDTYaKFqBWLo0wjDUd96BAQPY3LQLJzCe7r0jpISNktBQ14JI1TkQEKVAiMjrwOPAUUCXYOmc3zFqbAu+lg8WBXoC7wXtw4HTgs/9gu8E23uJpKLmOo4TD0Jv69EIRNOm8OefORLqvfkmnHceHH44T544jp0V9qd798LbE+1kuVQWiGgzg3QGWqtGm2XdEJGywAygKfA88DOwWVUzgl3SgNCwgbrASgBVzRCRLUANYGOOcw4EBgI0SNW77jhOzEQzByJE69a2nj3bciAxZAhcdRUcdxyMHs24HlXo1g322afw9tSvby6vjIy8cyzt2GHzIFJxBBNE72KaB8RcC0lVd6tqe6Ae0BVoFWm3YB2pt5BLkFR1qKp2VtXOtfKYBu84zt5HNGk2QnTpYnGC774DHn0UrrwSeveGMWPYursKM2aYVhSFBg0sLpJf2u+Qzan6LptvD0JERmMP6X2BBSIyDYstAKCqp0ZzEVXdLCKTge5ANREpF/Qi6gGhnIhpQH0gTUTKAfsDm2L7OY7j7K2kpUGFCpZuuyCqVoVD2yptXrsDVvwbzj0Xhg+H8uX55lN7sB97bNHsCR/qmpdoLVtm64YNi3atRFGQi+nxwp5YRGoB6YE47AMcjwWevwDOAt4GLgZGBYd8HHz/Ltg+KVaXluM4ey9paTaPrUw0fo/du3k2/WqOXjGUzCuupMzzz1lBBmDyZChfHg4/vGj2hHoF+cUhfvzR1ocdVrRrJYp8BUJVpwCIyGOqenv4NhF5DJiSz+F1gOFBHKIMMFJVx4jIAuBtEXkImAm8HOz/MvC6iCzFeg7nFOYHOY6zdxLNHAgA/voLLryQo38aycPcSd+rB3Fo2SwP9uTJ0K2b1bUuCtHMpp4+3QLmkepnpwLRxiBOiNDWO78DVHWOqnZQ1cNUta2q/itoX6aqXVW1qar2V9VdQfvO4HvTYPuy2H6K4zh7M/mVGt3Dn39aFbiRI9l427+5m4f57vsscViyBKZNgxNPLLo9++0H+++f/1DXGTOgc77jPZObfAVCRK4SkblACxGZE7b8AswtGRMdxyntqEbRg9i82TKyjhsHL75IjUdvpWbNIFAd8PzzNuLo8svzPk0s1K+ft0Bs2AArVkCnTsVzrURQUAziLeBT4BHgjrD2P1TVA8iO45QIGzea5yjPEUzr1llG1vnzbTJc//4IFmcICcQff8Crr8KAAcVXG7pFC5g1K/K2GTNsvdf2IFR1i6ouV9VzsVFG6diopqoikqIDtxzHSTXynQOxdCkceaSlZx09Gvr337Pp8MOtedMmeO012LoV/vnP4rOrUyf4+WfrvORk+nRbd+xYfNcraaKaKCci1wL3A+uAzKBZgRSNzTuOk0rkKRDTpsHJJ9vnSZPIOTX6iCNs3amTVZDr0sUC1MVF6OH/44/Qs2f2bdOnW7W6/fYrvuuVNNEGqW8AWqhqG1U9NFhcHBzHiUhGhrlYimugekSBGDPGZrvtt5/Vko6QN+Ooo+CJJ0wgatWC++4r3qR5ofhCyJ0UTqoHqCF6gVgJbImnIY7j7B1MmwZdu9rDcdy44jnnypUWXN6TlXvoUBut1Lq1iUOojFwOypaFm26C996DBQuyOhvFRc2aNh8ip0CsXWuiluoCEW0upmXAZBEZS/aZ1E/GxSrHcVKSKVOgRw+rvVOxInz+ucWOi0poklzZMgr33gcPPmipM0aOtGnTCaRTp6wJcSFC8YdUF4hoexC/AuOBCljajdDiOI6zh48/tnQYCxaY//+LL4rnvGlp0PDgdBuf+uCDcNllMGpUwsUBLA6xZAlsCfOxvP22eb5SeYgrRNmDUNVQsZ997eueNN6O4zh7+P57eyhWq2Y9ifvusxFEBxxQtPNu+nUbw7b3h+8+g3vvhfvvT5oKPCERmDnTQiIbNsC778LAgUWfrZ1ooq0H0VZEZmJZXeeLyAwRaRNf0xzHSSX++st88aEcRz16WJB6Sn4JeaJAV6/hlWXHcei68RZ7eOCBpBEHyB2ofvVVuxdXXpk4m4qLaF1MQ4GbVLWhqjYEbgZejJ9ZjuOkGjNnwq5dWQLRtau9QRfJzTR7NpldutJcFzLm8o/gH/8oFluLk9q1bXTVjBmWJXbIEDjmGGizF7xCRysQVVR1z59ZVScDVeJikeM4KUloxnJotGmFCjZ/rdACMXo0HHkkuzOUo/gaTjmlWOyMB507m1upc2dL8X3VVYm2qHiIViCWicj/iUijYLkH+CWehjmOk1p8/72lwqhbN6utRw+YN8/88lGjCk89ZcNYW7bkswemMZv2NGlS7CYXG//+N9x4o6Ui79gRzjgj0RYVD9EKxGVALeAD4MPg86XxMspxnNTju+9y11jo0cPWUcch0tPt9fumm+D002HKFBZsPhiAxo2Lz9biplkzE4np083VVKFCoi0qHqISCFX9XVWvU9WOQQrv61X193gb5zhOarB6tWU1zTmZuX17iyfPnx/FSTZvtplsQ4bA7bebz6ZKFZYtMz9/EoxoLXUUVHL04/y2R1ty1HGcvZtQ/CFnD6JSJZtpvHhxASdYuNBcSsuWwSuvwKVZDoply5K797A3U9A8iMOxNBsjgKlA8owtcxwnafjhByvj2aFD7m3Nm9tEsjwZOxbOO8+mXk+aBEcfnW3zsmUR0yw5JUBBLqaDgLuAtsAzWGW5jao6JVSO1HEcZ/ZsaNXKnvE5adbMBCJX4j5VeOQR6NvX6nJOn55LHDIyzHWVzAHqvZmC6kHsVtXPVPVioDuwFMvJVIwZ1R3HSXXmzIF27SJva9bMwgsbN4Y1bt8O554Ld90F55wDX31lvqgcrFwJu3e7QCSKAoPUIlJRRM4A3gCuAQZjo5kKOq6+iHwhIj+JyHwRuT5ov19EVonIrGDpE3bMnSKyVEQWiUgxVI11nKLx+OPw9NNWOtKJzMaNFqTOTyAgzM20YoXl4R45Eh57DN58M8+cFMuCyvQuEImhoCD1cMy99CnwgKrOi+HcGcDNqvpjkMNphoiMD7Y9paqP57hWa+AcoA1wMDBBRJqr6u4Yrum4iKcbAAAgAElEQVQ4xcayZXDrrfb5xhvhP/+BW25JrE3JyJw5tj4sjwoxzZvbeskSOGL7eIs3pKdb7KF373zP7QKRWArqQVwINAeuB74Vka3B8oeIbM3vQFVdo6o/Bp//AH4C6uZzSD/gbVXdpaq/YO6srtH+EMcpbsaOtfW4cTb5acSIxNqTrMyebeu8ehCNGkG5Mpkc/OogOPFEywU+bVqB4gAmEOXLZ59855QcBcUgyqjqvsGyX9iyr6pGXUhPRBoBHbCRUADXisgcEXlFRKoHbXWxEVMh0oggKCIyUESmi8j0DTFNz3Sc2BgzxorS/+1vtsydCzt3Jtqq5GP2bDjwwLBiPjkov+13xlXqxwlT7rG4w/ffZ3UrCmDZMmjY0Ar/OCVPtDOpC42IVAXeB25Q1a3AC8AhQHtgDfBEaNcIh+cqWKiqQ1W1s6p2rlWrVpysdko727bB5MlZFcg6dzavyNy5CTUrKckvQM3MmdCpE0fvGMcjdZ+DN96AKtGncfvlF3cvJZK4CoSIlMfE4U1V/QBAVdcFo6MysYywITdSGlA/7PB6wOp42uc4eTFhgqVsDuWHC1UGC1UKc4z0dJslHVEgXn3Vqgalp/Nc/y8ZtPkaNMapVMuWuUAkkrgJhIgI8DLwU3hpUhGpE7bb6ViNCYCPgXOCUVONgWbAtHjZ5zj5MWaMVQQ76ij73qCB1R92gcjO4sUmpNkC1Nu2wSWXWNW3I4+EH3+kwjHd2b4d1qyJ/txbtsBvv7lAJJJoa1IXhiOxIPdcEZkVtN0FnCsi7TH30XLgCgBVnS8iI4EF2Aioa3wEk5MIMjPhk08snlq+vLWJWC/CBSI7uQLUc+bA2WfDokXwf/9nJeXKls021PXgg6M7d2gEk6fZSBxxEwhV/ZrIcYVP8jlmEDAoXjY5TjQsXGhvuiedlL29c2eb+Pvnn6lfSrK4mDPHRLRlC4UhQ+H666F6dfPR9ey5Z79wgTj22OjOHcrfFGU824kDcQ9SO06qMXOmrbt0yd7eubPN6g29NTsmEF2ab6H8hedYjc1jj7UbFCYOYC66ChWsYxEtixZZzy0kLk7J4wLhODmYOdNyCrVsmb3dA9W5KTtjGh+s6Ajvv2/dq08/jTjetWxZ6wnEIhALF5qw7LNPMRrsxIQLhOPkYOZMOPTQrPhDiIMPtjleqSoQkydHWZchGjIy2HnPQ3y4/ggqlU2HL7+EO+6wkmp50LKlPfSjZdGi3CLtlCwuEI4ThqoJRKS01SLWnoouppUrbbJfhw7wxBMWiC80y5bBscdSadD/8S79+X7IbBvOWgAtW9qhu3YVfAlVi0G0aFEEO50i4wLhOGH8+iv8/ntkgQDzh//8c4TU1UnOE0+Yzccfb/mkbrihECdRheHDrUzcvHlMvOxNzmMELbtXL/hYTCB277b7VxCrV9toWReIxOIC4ThhhALUeQnEIYfYg2v9+pKzqaisXw9Dh8IFF1h+qbPOgrfeirEXsWkTDBhg8xs6dIA5c/hwn/PYd9+IWbojEnIXReNmCu3jApFYXCAcJ4yZM82Nnldm0qZNbR3NW3Cy8PTTlkPqjjvMTda3r01AC2VhLZBPPrGgzKhR8OijVvWtYUPmzYO2be2c0RB62EcjEKFgtscgEosLhOOEMXOmPcjymucQEoilS0vOpqKwfTs8/7z1GkIP6NAI1EmTCjh482arDX3yyXDAAZZk7/bboWxZVC0v1aGHRm9L1apQr170AlG1avST6pz44ALhOGHkFaAO0aiR9TBSRSDGjIGtW+Hqq7Pa6tWzIacTJ+Zz4KefWvfg9dfh7rtt6FbHjns2r11rXqe2bWOzJ9qRTAsXmo3R9k6c+OAC4TgBGzdCWlr+AlGhgvncU0UgRoywt/AcpZ7p1ctGpqan5zhg82bLodSnD1SrZr2Ghx7KVWw6lNW2sAJRUJB/0SKPPyQDLhCOExAavtq+ff77NW2aGjGIzZutI3D22bnrKfTsacH2H34Iaxwzxp74w4fDnXfCjBlZswNzMC9IsVkYgfjjj/yT9u3YYaPJPP6QeFwgHCcgNImsoIde06ap0YP44APLtHruubm39ehh60mTsKf1gAEWvQ71Gh5+OFevIZy5c61IUKwlWaIZybRkifUwvAeReFwgHCdgwQKLxR54YP77NW1q/vdNm0rGrsIyYoQNy43UCahRAzq0y6Ty60OgVSv4+GNzJf34Y+4kVBGYNy+2AHWIaAQi5L7yHkTicYFwnID586F164IDo6kw1HXdOusdnHtuHr9nwQLeXX8MNy2+Eu3Y0Z7Kd99tQZYCyMy0exWrewksHlK1av4CMWGCJYQtzPmd4sUFwnEwl8b8+dCmTcH7HnKIrZNZIMaMsQf5WWfl2PDnn1anoX176m79iUt4lZ+HTIwpZeqyZRYnKEwPQsRiPNPyKAWmCp9/bjO+vQ514nGBcBzsjfv3360HURChCmfJHIf4+GNo2DBswp8qfPih/cCHHoKzz2bRRwsZziXMmh3bWNLCBqhDHHecjZr944/I51692oo1OYnHBcJxsPgDRNeDqFwZ6tYtfoFYsACuuspGHxWFP/+E8ePh1FMD99LixVb96IwzrI7qlCnw+uu0OKoWZcvGnnwwFCOIRkwj0aOH5WT66qvc28aNs7ULRHLgAuE4ZI1givahV9wjmdLS7KH4v//B448X7VwTJ5oL6PQTttlw1bZtbWTSM89YEPqYYwCoVMkCwbNmFXDCHMybZ72oqlULZ9/hh1uo44svcm/77DP7G9SrV7hzO8WLC4TjYG/v1atbvYdoOOSQ4hOILVugd29bH320Pcc3biz8+UaPyuTvld7guKtbWe6k886zXsR110G57FWG27WLXSDmzi1aAHmffUwkcgrE9u3Wq/DeQ/IQN4EQkfoi8oWI/CQi80Xk+qD9ABEZLyJLgnX1oF1EZLCILBWROSLSMf8rOE7xEe0IphDNm1vcYsuWol/78cdNoD78EIYMsQdlYXsRmVO+4urh3Xhx54VI7drwzTcwbFieY3fbt7fey2+/RXf+XbtMawoToA6nRw9La/L771ltU6bYvA0XiOQhnj2IDOBmVW0FdAeuEZHWwB3ARFVtBkwMvgP0BpoFy0DghTja5jh7iGUEU4jQJK5YSmjmde2RI+2B2auXTUk47zx49tkYU4ovXQpnnkmZ446hRsZavr3yNZsmXUAhn3btbB1tHGLhQosfFHUIao8eNsrqyy/te2YmDBpk81ACD5iTBMRNIFR1jar+GHz+A/gJqAv0A4YHuw0HTgs+9wNeU+N7oJqI1ImXfY4TYv16m/RWGIFYvLho154zx87Rv39W2113WaD57bejOMGmTXDzzdb9GTeOyb0epCWLaPHQhfmW/wwRq0CERjAVtQfRrZvFQEIZZZ9/Hr79Fp56ymtQJxMlEoMQkUZAB2AqcKCqrgETESBU4bwusDLssLSgLee5BorIdBGZvmHDhnia7ZQSYg1Qg8UgypYteg/i3XftOX766VltrVubC+vTT/M5cNs2e+Vu0sQKPlx8MSxZwv0Z99CiQ2Vq1Iju+gceaHGXaOMQ8+ZZre4Ypk1EpGJFm+vw7LOWK+rOO22g1YUXFu28TvESd4EQkarA+8ANqro1v10jtOXK+aiqQ1W1s6p2rhVrIhjHiUBoVm+rVtEfU6ECNG5cNIFQNYHo0QNq186+rXdvmDzZehLZ2LXLnqqHHAL33APHHmtP9xdf5M/96/Ddd/bgjYX27aMXiLlzrfcUxYTrAhk+HG67zarcidgILk/vnVzEVSBEpDwmDm+q6gdB87qQ6yhYhzytaUD9sMPrAavjaZ/jgLl4qlSJvThNixZFE4hI7qUQffpYFbjJk4OG3bvtidqihY1Gat3afDKjRu3x93z9tQV5e/WKzY727S1IvnNn/vupmpAU1b0U4oADbJDVihXWM2nYsHjO6xQf8RzFJMDLwE+q+mTYpo+Bi4PPFwOjwtovCkYzdQe2hFxRjhNPFi8uXHGaFi0s82hMtZ3DiOReCnHMMTYh79Mxu62AdNu2Vg+6Vi3LRTFpko0VDWPCBHP/HHVUbHZ06wYZGVn1uPNi6VJYtSp3bYmiUqOGi0OyEs8exJHAhUBPEZkVLH2AR4ETRGQJcELwHeATYBmwFHgRuDrCOR2n2Fm8uHA+9ebNbUJaWlrsx+bnXgKoVC6DB5u/xo0vtYbzz7f5C++9Z0mMTjghoppNnGiaUaVKbLZ062br77/Pf79QQDnWHoqTupQreJfCoapfEzmuAJDrn5iqKnBNvOxxnEj89RcsXx65ZkJBhA91bdAgtmND7qWbbsqxIT3dynwOGsRNy5Yxi3asGvw+da85Ld9RSb/9Zj2ABx6IzQ6AOnXM/oIEYuJESzFS1AC1kzr4TGqnVPPLL+beb9489mOLMhci5F4644yg4c8/baxn8+Zw+eVQvTrrho6iAzN5d/cZBQ5ZnTTJeiWFfbvv3h2mTs17e2amzXzu1csDyaUJFwinVBOax1CYt+KDDoJ9941dIMLdS7VkI9x/v73CX3utnXTMGPjhBw78x6m0aiV88knB5xwxwlxVUdT6iUj37hYszqsU6Ny5lv6jZ8/Cnd9JTVwgnFJNSCAK04MQsV5ErJPl5syB9MXLeDrjWhOGBx6wGc9ffWUjk04+ec9reu/eloJi27a8z7dhA4weDRdcYEHqwtC9u63z6kWE4g8uEKULFwinVLN4sY2iOeCAwh0f01BXVfjqK8qe058lNKPNt0Mt+DF/vhVwOOqoXP6bPn0sThIp82mIt96yUUiXXFK43wDQoYOJS15xiEmTrJdVv37k7c7eiQuEU6pZsqRoQdeWLeHXXyMXv9nDjh3w8sv2FD7mGOotnsh7DW9Bli+39nymcB91lI1Kys/NNGwYdOpUtPkJlSrZfIhIArFjh/VivPdQ+nCBcEo1oTkQhaVzZ+sY/PBDhI2//gp33GHFDf7+d8jMZNkdQzk4M40/73ssqpl5oZQUn35q18nJrFm2XHpp4X9DiO7drdJbRkb29rfeMgE855yiX8NJLVwgnFLLtm028asoAhGaQ/Ddd0FDRoa5i045xXJx/Oc/VmNz8mSYPZuntv2DzIqVs0YvRUHv3hZA/umn3NsGD7a0F8Xx8D7uOEs1HqrqBiZKgwdb6dJjjy36NZzUIm7zIBwn2QkV/CmKQFSvbjmcfp64HHa+DK+8YkWV69Sx3sPAgXumCaenwzvvQN++sP/+0V+jd29bjxmT3Rs1Y4a5l266iaiT8+VH377WqXn2WYuTg6XjnjMHXnrJh7eWRrwH4ZRaliyxdaFjEDt3wrvv8s7W3rz0RRN00CBz5H/0kbmXBg3KlkNiwgQbcXT++bFdpkEDi0U880xW8j5VuP56qFkT/u//Cml/DsqXt5rY48ZlJTAcPNgC+OedVzzXcFILFwin1BJ6CDZtGsNBqlal7YorbM7CgAE0/nMe/+Jefpm03FKT9uuXq7QnwJtvQrVqWT2CWHj4YeuYDB5s3994w8x4+OHYeiMFMXCguawGD7aexEcfWZvXaCiduIvJKbXMmGHupapVo9j555/tqfzaa7BsmWXSO/NMuOgiVtTuwQPtytJoOTTJ4/CtW62k6AUXWOA5Vo4+2lxAjz5qM7/vu8/iH8URnA6ndm0beftCUM+xTx+4/fbivYaTOngPwimVqNqksK5d89lp2TJ47DEbQ9q0qU1oa9zY0m6vW2dicfzxtGpblv33DwtUR+Dtt809dPnlhbf54YdtNNE995hYfP65FS0qbm67zarrDR1qcY9q1Yr/Gk5q4D0Ip1SyahWsXRtBIH75xfJgvPuujfkEe1V//HEYMCDiTLEyZWyIaH4C8fLLlrG7sKkwwI5/6inLi3TddVFVFC0UrVtnlRZ1SjcuEE6xsnkznHiiuUGaNLGBPB07Jtqq3EybZuuuXbFo9UcfmSiEJjR07WpDVM86Cxo1KvB8hx9uHYzNm3O/cc+bZ9d78smijwS67rqiHe84seAupiRhzhzzWAwaZC+xqcoDD9gz9oADbNTOBReYWCQVGRlseG8Kj5e5lS4XtbRAxG23md/p3/8219LUqXDLLVGJA9iwUFV45JHc2155xUYIeb1lJ+VQ1ZRdOnXqpHsDo0ap2uPFlt69E21R4ViwQLVcOdWBA+37yJH2e15/PbF2qarq77+rjhihet55qtWrq4L+JeVVTzhBdfBg1V9+KfIlLrvMfv/cuVlt8+erVqumeuaZRT694xQbwHSN4hkrGmn+forQuXNnnR7yE6coGzdaQLBOHRg50rwc99xjQxiPOCLR1kWPKpx0kr14L1lilTEzM829tH27zQKOMPIzfuzebcOUxo+35euvra1mTbTPyVw4si+1zz+BJ1/ar9guuXGjJe9r3dqyq65bZ7OTwRK1xjSc1nHiiIjMUNXOBe4YjYok65LqPYjMTNWzzlItX1519mxr27ZNtXZt1Z49E2tbrHz+ufUWnnwye3uod/TSSyVgxNKlqi+8YK/rQS9BQbV9e9U77lD95hvVjAxdsMCaX321+E14+eWsy5Yta3/LBQuK/zqOUxSIsgfhQeoEMmqUlRl++GHLdQOWufOuu+CGGyx9T+gNNNl56CErR3l1jkrifftCu3YwZEjRhnhGZNUqywUxebL1EkLBm3r14LTTrHZzr165ij5nC1AXM5deagOdZs2y4jv/+Iel4nCclCQaFSnMArwCrAfmhbXdD6wCZgVLn7BtdwJLgUXAidFcI5V7EDt3qjZpotq6tWp6evZtO3aoHnSQ6hlnJMa2WJkyxd6Yn3km8vZ//UtVRHXduiJcJDNTdckSe0W/+GK7eaFX9X33VT31VNVnn1VduND2zYcrr7RDMjKKYI/jpDAkQQ9iGPAc8FqO9qdU9fHwBhFpDZwDtAEOBiaISHNVTbbxL8XG4ME2WGbcuNy++UqVLFvDW29ZsZgKFRJjY7Q89JC9pP/975G39+kD994Ln30GF10U5Un/+stew6dOtfjBl1/axAVAa9RgZtVjeKvsP/lKjmZj9XaMfqRcfmUV9qBq9/zYY+Mzycxx9ibiNsxVVb8ENkW5ez/gbVXdpaq/YD2JODgAkoP16+HBB21o5N/+Fnmfk0+2WbNffVWytsXK1Knm3bn5Zss+EYkOHSxtUZ5Fb1QtlcVbb1kGuu7drdhzt2428P+bb6xazf/+x4pP5nPYQRvo/OsH/H7xDfS8pRPbd5XjlFPsvhbEokXmierTp9A/2XFKDYmIQVwrIhcB04GbVfV3oC4QXssqLWjLhYgMBAYCNGjQIM6mxodnnrGRPY8/nvc+PXtazp4xY8yNnqwMGmRzHq66Ku99ypSxBHUffmjlEspt+c1mKU+dasu0aTYECCwrXKdO8M9/mkB062ZOfRF+/tmymqanWwGdE0+0Q04/3XoEp51mpTnzy3U0dqytQ+msHcfJm5IWiBeABwEN1k8AlwGR5pdGHH+rqkOBoWDDXONjZvxIT7eJU336WLnKvKhSBXr0sAfaU0+VnH2xMGuWDef817/shT8XqrByJcycyS3bZtJv80wy6s6k3PqVtl3EIrh9+1rEuFs3yydRvnyuU61aZTHn9HTzNoW7k7p2tfRIZ59tdQuuuSZvm8eOtUuk6LuF45QoJSoQqrou9FlEXgTGBF/TgPAkN/WA1SVoWokxerS50q+4ouB9Tz7ZXqSLWjc5Xjz8MOy3n9lIRoYZOnNm9mWTeRlbiSC0YGGNo2h/SwfzO3XpElWu6owMi8ls3AiTJkUu4dy/v/XM/v1vS08dQWPYutVcdjffXMQf7jilhBIVCBGpo6prgq+nA6GUYB8Db4nIk1iQuhkwrSRtKymGDLFRmNHUBAgJxNixNuw1KcjMhBUrWPnpPJq+O4+72syj2rHzrLjCX3/ZPhUqwKGHwhlnmBB06IAcdhjX9K3C+vUw79bYLvnkkzbn7d13rQZ0JETg7rvtnr35JlxySe59xo83sfH4g+NER9xmUovICOA4oCawDrgv+N4ecx8tB64ICYaI3I25mzKAG1T104KukWozqZctg0MOgfvvt3z+0dC6tc0vGD8+rqblRtUq1Myfb9nmQsuCBRZACdhdrwFlD2tr08HbtrWKaq1aRXyFf/ZZizkvXGgzjqNh6VLTmt694YMPCja5Y0dLq71gQe5RSpddZnGQDRtKeFa34yQZPpM6CbnnHtUyZVRXroz+mFtusZnWW7fGzy7dsEH1iy9Un3vOJgkcdZQlEApPEHXggaq9eqlef73ufP5F7VX5Wx149uaYLpOWZqcaNCj6Y44/XnW//VRXrYpu/1D+p2HDsrdv2KBaubLqJZdEf23H2VvBczElH61b23DPSZOiP2bKFJtN/f775rEpCttWb6X84vlUXDIve89g3bqsnapVs55A+NKmjRU/DnjtNbj4YrPtmGNis+GII6yU848/Frzvt9/CkUeai+nGG6M7f2amjXRavNjyP9WqZe333GMxk/nzfWaz40Tbg/COdgmxcKE9sHKmoiiII46wOO7YsTEIxI4ddrH581kzYR4rP5nHQb/No4H+umeXzMpVKNO2jTntQ+6htm0ta2ABRQteesmC5kcfHdtvASuvcPPNNu3hkEPy3/fxx6F6dUtXES1lysCLL1ro48YbrUroli3w3HN2/1wcHCd6XCBKiA8/tPVpp8V2XPnyNt7/k0/s7ThbFbH0dBs5FB4jmDfPnr6ZmQAcQAV+K9uK9c2PZlnjtvy4sw3/+7otq7Uh04eXyXeobSQWLrSRQI89VrjiN2ecYQLx/vtWgiEvFi+2Gj533RVlzegw2rSx4x54wIbf7thhInHXXbHb6zilGXcxlRBdutjDferU2I997TW47eK1fDV4Fs22z4LZs00IFi0ykQCLyDZrBm3bMlfb8sS4tvy4qw2n39qUm28vx35hWa1XrLC5aE2amBsnloDtLbfYcNKVK81dVhhCZTdDxdsicdVVNl9kxYrCXWfXLitWNHasCUTv3vnM5HacUoYHqZOIFSsscPrII1HsvHu36qJFqu+8YymqTzpJM2oflD1g3KiRat++qnfeqfrGG6qzZlmGP1WdMMF26d49/zTToWDuv/4V/e/Yvt2yaJ91VvTHROKZZ+zaU6ZE3r5woWqlSqqXX16066iq7tql+sMPqps2Ff1cjrO3gAepk4fBgy3F0KJFVt1yD5mZ1jhtmr1Oz5xpvYPQMNLy5c1f0r49T37RngUVOvDStMNyFz0O+P13GxJataoFgfPKjRTivPNsbsG8edENO33lFUvZ/cUXRUtDvmOHxR+aNrVAd7iratcuS8W0cqXdiroRE644jlMUvAeRRBx9tKX11lWrVD/80HoGPXva+M3wlNXHHKN63XWqr7yiOnOm5QQPGDzYdvvmm8jXyMxUPeccK3k5fXp0dq1bZ0M/L7ig4H0zM1U7dlRt06bAbNpR8dxz9ns+/zx7+w03WPvo0UW/huM4kSHKHkTCH/JFWZJaIDIyVGfO1N/uf0ZHcpZu2a9ulhiUK6faqZPqVVdZWbMFC8y1lA9//GHunbxqRDz8sMY8x0BV9dZbbW7GwoX57/fdd3b+//43tvPnxc6dqvXrW7G3L76winqnn27XuO664rmG4ziRcYEoaXbuVP36a3tS9+6drXewnAa6te+5qk8/rfrtt3viBbFy111WeGfx4uztL75olzr//AJ1JhfR9iLOPts6OcU5Ye/tt02cwjtRDz5ocQPHceKHC0S8ychQ/f57e6Ide6xFVUNPulatVAcOVH39dT2p1XLt1q14LrlmjWqFCha8/esv1fXr7W27TBnVk06ytsIQ6kXkFdSeNs1+1p13Ft72vPjtN9UxY8yFVqSKc47jRE20AuFB6hykpVkun0aNIlRy+/lnS4o0frxNh9682do7dLCo7dFH2zTeYPruggUWY37mGctBVBwMHGgTwcqVsxj2rl1Wye3JJy1FeGHYsMECxocfbnUWwoPGqjZbevFim3IRPlzWcZzUxGdSF4Jhw+wBnJ5ucxZOPyWdly79hmpfjbY83UuW2I7169uMrxNOsGo+oXwOORgxws4zYEDx2Th4sD2wFyywyV/XXlv02cG1atmkshtvtJ956qlZ2z74wCp+Dhni4uA4pQ3vQWDDLu+911I79Dt2Mze1+pR9J4+m0cJPqc5mdperwB+delDhjJOp3O9vNla1gGnEu3bZUM7WreHzz4tsYtxJT4d27czu+fOtLvayZdYpql7digN5BlTH2TuItgcRt5rUqcCGDfD889CxyWbWPz6cOQ1O5sNva3PM/86jw28T0FNP47q671MtYyPVp37Gfnf9k+OvacGw4UJBuvryy1YF7fbbS+a3FJXy5a13EhKFDz+0sqc7d1qpaBcHxyl9lMoexKRJ8OAtW6g/82P6M5KTZBzlNR0aNjR/0OmnWx3LsmXJyMgqdP/tt/Dee+ZpuvBCS1qXK06BvYU3bWqn++qrwuUsShRvv225klavtiSBEydaWg7HcfYeou1BlEqBWDToXRr/3wVU0L/YdWB9Kpw/ADl7gCUJKuBprgqDBsH//Z/VjP7oo9y++RdesKytn39uYYpUY9s2izn07Gnxd8dx9i5cIPLj55/NtzRgAHTrVqhX/DfegEsvtQpmn31mfnqwnsbhh1v84euvU6v34DhO6cBHMeXHIYfYuNAicMEFlkq6f38byPTUU+ZS6tnTAr5Dhrg4OI6T2pTqIHVR6dcPRo2yOQLHHWfpszdtgnHjrPaO4zhOKhM3gRCRV0RkvYjMC2s7QETGi8iSYF09aBcRGSwiS0Vkjoh0jJddxU3v3rB2rQWv//EPizt0LjhHouM4TtITzx7EMOCkHG13ABNVtRkwMfgO0BtoFiwDgRfiaFexU7UqnHmmuZW6dUu0NY7jOMVD3ARCVb8ENuVo7gcMDz4PB04LawEjNKYAAAoISURBVH8tSBPyPVBNROrEyzbHcRynYEo6BnGgqq4BCNa1g/a6wMqw/dKCtlyIyEARmS4i0zds2BBXYx3HcUozyRKkjjTeJ+L4W1UdqqqdVbVzrTxyIDmO4zhFp6QFYl3IdRSs1wftaUD9sP3qAatL2DbHcRwnjJIWiI+Bi4PPFwOjwtovCkYzdQe2hFxRjuM4TmKI20Q5ERkBHAfUFJE04D7gUWCkiFwO/Ar0D3b/BOgDLAX+BC6Nl12O4zhOdMRNIFT13Dw29YqwrwLXxMsWx3EcJ3aSJUjtOI7jJBkpnaxPRDYAK2I8rCawMQ7mxINUsTVV7AS3NR6kip2QOrbG286GqlrgMNCUFojCICLTo8limAykiq2pYie4rfEgVeyE1LE1Wex0F5PjOI4TERcIx3EcJyKlUSCGJtqAGEgVW1PFTnBb40Gq2AmpY2tS2FnqYhCO4zhOdJTGHoTjOI4TBS4QjuM4TkRKlUCIyEkisiioXHdHwUeUDCJSX0S+EJGfRGS+iFwftEeswJcMiEhZEZkpImOC741FZGpg6zsiUiEJbKwmIu+JyMLg3h6erPdURG4M/vbzRGSEiFRKlnuaKtUh87DzP8Hff46IfCgi1cK23RnYuUhETiwpO/OyNWzbLSKiIlIz+J6we1pqBEJEygLPY9XrWgPnikjrxFq1hwzgZlVtBXQHrglsy6sCXzJwPfBT2PfHgKcCW38HLk+IVdl5BvhMVVsC7TB7k+6eikhd4Dqgs6q2BcoC55A893QYqVEdchi57RwPtFXVw4DFwJ0Awf+vc4A2wTH/DZ4RJcUwctuKiNQHTsBy1YVI2D0tNQIBdAWWquoyVf0LeBurZJdwVHWNqv4YfP4De5DVJe8KfAlFROoBJwMvBd8F6Am8F+yScFtFZD/gGOBlAFX9S1U3k6T3FMuLto+IlAMqA2tIknuaKtUhI9mpqp+rakbw9XuslEDIzrdVdZeq/oIlCu1aEnbmZWvAU8BtZK+Hk7B7WpoEIuqqdYlERBoBHYCp5F2BL9E8jf0jzgy+1wA2h/1HTIZ72wTYALwauMJeEpEqJOE9VdVVwOPYW+MaYAswg+S7p+EUuTpkArgM+DT4nHR2isipwCpVnZ1jU8JsLU0CEXXVukQhIlWB94EbVHVrou2JhIicAqxX1RnhzRF2TfS9LQd0BF5Q1Q7AdpLAnRSJwH/fD2gMHAxUwdwKOUn0PY2GZPy3gIjcjbly3ww1RdgtYXaKSGXgbuDeSJsjtJWIraVJIJK6ap2IlMfE4U1V/SBozqsCXyI5EjhVRJZjbrqeWI+iWuAegeS4t2lAmqpODb6/hwlGMt7T44FfVHWDqqYDHwBHkHz3NJyUqQ4pIhcDpwDna9bEr2Sz8xDsBWF28H+rHvCjiBxEAm0tTQLxA9AsGBlSAQtQfZxgm4A9PvyXgZ9U9cmwTXlV4EsYqnqnqtZT1UbYPZykqucDXwBnBbsl3FZVXQusFJEWQVMvYAFJeE8x11J3Eakc/FsI2ZpU9zQHKVEdUkROAm4HTlXVP8M2fQycIyIVRaQxFgCelggbAVR1rqrWVtVGwf+tNKBj8O84cfdUVUvNglWtWwz8DNydaHvC7DoK6zLOAWYFSx/Mtz8RWBKsD0i0rTnsPg4YE3xugv0HWwq8C1RMAvvaA9OD+/oRUD1Z7ynwALAQmAe8DlRMlnsKjMBiI+nYg+vyvO4j5g55Pvg/NhcbmZVIO5di/vvQ/6v/he1/d2DnIqB3ou9pju3LgZqJvqeeasNxHMeJSGlyMTmO4zgx4ALhOI7jRMQFwnEcx4mIC4TjOI4TERcIx3EcJyIuEE7CCDJWPhH2/RYRub+Yzj1MRM4qeM8iX6d/kCn2i3hfy3FKGhcIJ5HsAs4IpTVOFmLM6nk5cLWq9oiXPYkmbDa3U8pwgXASSQZWe/fGnBty9gBEZFuwPk5EpojISBFZLCKPisj5IjJNROaKyCFhpzleRL4K9jslOL5sUCPghyC3/hVh5/1CRN7CJiPltOfc4PzzROSxoO1ebJLj/0TkPzn2P05EvgxqECwQkf+JSJlg2wsiMl2s/sMDYcc8Guw7R0QeD9r6B9ecLSJfBm2Ngt/1Y7AcEXbNyZJVA+PNYGY2ItInaPtarLZAqI5HFbHaBD8ESQ37Be2XiMi7IjIa+FxE6gS/Z1Zgz9Ex/J2dVCURMzN98UVVAbYB+2GzRvcHbgHuD7YNA84K3zdYHwdsBupgs41XAQ8E264Hng47/jPsJagZNlu1EpZP/55gn4rYTOvGwXm3A40j2Hkwlg6jFpYEcBJwWrBtMhFmtgbn24nNhi6L1SU4K9gWmnVcNjj+MOAAbEZvaPJqtWA9F6ibo60yUCn43AyYHnbNLViunjLAd5iAVcJmEzcO9htB1gz4h4ELQufHMg1UAS4J7lnI1psJsg8Edu+b6H8/vsR/8R6Ek1DUsta+hhXMiZYf1Gpo7MLSD3wetM8FGoXtN1JVM1V1CbAMaAn8DctrMwtLqV4De8gCTFOrDZCTLsBktWR6oYygx0Rh5zS1+iO7sYfyUUH7ABH5EZiJFaxpDWzFBOUlETkDCOUN+gYYJiL/wB7MAOWBF0VkLpaCI7zw1TRVTVPVTCy1RKPgdy8L+20jwvb/G3BHcD8mY2LSINg2XlVDNQt+AC4NYkSHqtUtcfZyXCCcZOBpzJdfJawtg+DfZ+AmCS+3uSvsc2bY90zsDT9EzjwyiuW1+aeqtg+WxqoaEpjtedgXKd1yNOS6fpAY7hagl1qVs7FYbyADK1jzPlZ85zMAVb0SuAfL5jlLRGpgLrl1WJW8zuR9b3Zj9yM/+wU4M+x+NFDVUKXAPfdDrcDNMViP7XURuSjKe+CkMC4QTsIJ3lJHkr2k5nKgU/C5H/bWHCv9RaRMEJdogrlwxgFXiaVXR0SaixUSyo+pwLEiUjMIYJ8LTIni+l3FsgeXAc4GvsZcatuBLSJyIEHdB7FaIPur6ifADViiQUTkEFWdqqr3AhsxodgfWBP0Ei4kq2eRFwuBJmLFqAhsCTEO+GdYrKJDpBOISEOsDsiLWObhEquL7CQOH53gJAtPANeGfX8RGCUi07BsoXm93efHIuxBfiBwparuFJGXMLfLj8FDcQMFlPJU1TUicieWfluAT1Q1mtTb3wGPAocCXwIfqmqmiMwE5mNur2+CfffFfm+l4BqhwP1/RKRZ0DYRmA38F3hfRPoHNuV7b1R1h4hcDXwmIhvJntb6QawHNye4H8ux2gk5OQ649f/buUMjBKIYCKCJpyiaoQP6QCEoDEUbOMQi7mQsHMy9V0HU39lk5nf3q5bbkQaxA35zhQ/o7mNVnZNMj+3XdfchyXMNgWtVPZJctp6L32bFBPtwWg/R91pWVLeN5+EPaBAAjDQIAEYCAoCRgABgJCAAGAkIAEZvSAySwzH9EwUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f234c63f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, Y, color = 'blue') \n",
    "plt.plot(X, lin2.predict(poly.fit_transform(X)), color = 'red') \n",
    "plt.title('Polynomial Regression') \n",
    "plt.xlabel('Number of passangers') \n",
    "plt.ylabel('Month') \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}