TSAF_HospitalityEmployees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task:-> Hospitality Employees Prediction.\n",
    "## Dataset:-> Data  from year 1990 to 2018.\n",
    "## Model:-> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./Data/HospitalityEmployees.csv\",index_col='Date',parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>1064.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>1074.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>1090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>1097.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-01</th>\n",
       "      <td>1108.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Employees\n",
       "Date                 \n",
       "1990-01-01     1064.5\n",
       "1990-02-01     1074.5\n",
       "1990-03-01     1090.0\n",
       "1990-04-01     1097.4\n",
       "1990-05-01     1108.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.plot(figsize=(12,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1452.506897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256.604914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1064.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1238.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1436.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1586.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Employees\n",
       "count   348.000000\n",
       "mean   1452.506897\n",
       "std     256.604914\n",
       "min    1064.500000\n",
       "25%    1238.050000\n",
       "50%    1436.200000\n",
       "75%    1586.300000\n",
       "max    2022.100000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1990-01-01', '1990-02-01', '1990-03-01', '1990-04-01',\n",
       "               '1990-05-01', '1990-06-01', '1990-07-01', '1990-08-01',\n",
       "               '1990-09-01', '1990-10-01',\n",
       "               ...\n",
       "               '2018-03-01', '2018-04-01', '2018-05-01', '2018-06-01',\n",
       "               '2018-07-01', '2018-08-01', '2018-09-01', '2018-10-01',\n",
       "               '2018-11-01', '2018-12-01'],\n",
       "              dtype='datetime64[ns]', name='Date', length=348, freq='MS')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "results = seasonal_decompose(df['Employees'],model='mul');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gA4WASBhzzvH+4i089p9V7NxfyGldE7hqUCIjuiVQv04MxSWlfLhsK4/MWMXoF77ljesGc+IJoTPscu22XJ76dA1n9mzF6IHt/C4nKikERMJUxs79PPD+cr5J30n/xKb8e0wK/ROb/WiZ2jG1uKh/OwYkNuPKCXO57tVUPvzNMOIb1fOp6v8pKinljneW0Khebf54cW9NEe0TjcESCUMz07I475mvWbZ5L49c2IspNww9LADK6tCiIROuSWHX/kLum7KMUDgW+PRn60jbksPjF/UOiVCKVgoBkTBSXFLKEx+v5oY3FtE5oSEzbz+NXwzpUKGDvr3axnH3Wd34bFU2HyzZUgPVHt3X63bw/FfpXDqwHWf30pXC/KQQEAkTO/cVcM3E+bw4ez1XDU7knRtOrvSB1LGndGRgh2b8fvpKsnPyq6nSY8vOyee2t5fQOaERD4860Zca5H8UAiJhYMXWvVzw7NekZuzmydF9ePyi3tSrXfmJ1WJqGU+O7kN+UQkPvJ9W47uF9h4oYszLC9hfUMzzVw0gtq4OS/pNISAS4r5ak81lL36HA6beOJTLUtqX+5xj6ZzQiLtGduOzVdv4OO2H4BRZATn5RVz7ygLSs3N58RcD6da6cY39bDk6hYBICJs0fxPXvppKhxYN+eDmU4I2q+bYU5Lo0aYJj85YSV5hcVDWeSxpW/Zy/jNfs3jzHp6+oj/Du2pesFChEBAJQc45nvpkNfdPXc6wLvG8c8PJtGpSP2jrrx1Tiz+MOpGte/N5/sv0oK33UM453pq3iYtf+JbC4lLeHjeEc3u3qbafJ5WnHXIiIaaguIR731vGB0u2csVJ7Xnkwl7UqYYZNU9Kas5F/dvyrzkbGT2wPR3jGwZ1/XmFxfz2/TSmLt7Cqcnx/P3yfrTQUNCQoy0BkRCyN6+IMRPn88GSrdx9Vjf+eHHvagmAg+4/pzt1a9fi4Q9XBPUg8dptuYx67hveX7KF28/oyitjBykAQpS2BERCRNbeA4yZOJ+NO/bz98v7cWH/6r/4Xssm9bntjGQe/c8qPluVzZk9Wx3X+g4UlvDcl+uYMGcDTerX4fVfDWZYcnyQqpXqoBAQCQHp2blc89J8cvKLeXXsIIZ2qbk3zjFDk3h7wWbGT0tjUFJz4mLrVGk9X67J5qFpaWzedYCLB7TlgXN76EzgMKDdQSI+W7xpN6Nf/I7CEsfkcUNqNAAA6sTU4qlL+5KdW8C9VZhSoqiklN9PX8HYlxdQJ6YWb10/mL9e1k8BECYUAiI++nJNNlf9ax5xDeow5caTfbuwer/2Tbnn7G7MXPEDr32XUeHnZefmc/W/5vHKt99z7bCOfHzrqQztrN0/4US7g0R8MmVhJvdMWUb31o15ZewgEhr7+8n5umGdmLdhF3+YsZJ2zRrw0x7HPj7w5Zps7n1vGTn5RTx9RT9G9av+YxgSfNoSEPHBhDnrufPdpQzp1JzJ44b4HgAAtWoZT1/Zn55tmnDDGwt5b2HmEZfbsH0ft7+9hLEvL6BpbB3ev+kUBUAY0+UlRWpQaanjjx+v4l//3ch5fdrw18v6VmkOoOq090ARN76xkG/X76Rv+6ac3KkFnRIakp2Tz7frd/Ldhp3UjanFtcM6cusZySFXv1Tu8pIKAZEaUlhcyt3vLWXakq2MObkD4y84MWSv+1tcUsqb8zbx3sJMVv+QQ1FJ4H2ie+vGnNGjFWOGJoXE1oscma4xLBJi9hUUc8PrC/k6fQf3nN2NG4d3DukradWOqcWYoUmMGZpEQXEJ2/YWENegTpWHj0roUgiIVLPtuQWMfWU+q7JyeWp0Hy49zllAa1q92jEktoj1uwypJgoBkWq0fvs+xr68gO25Bfx7TAo/6dbS75JEfkQhIFJNZqb9wF3vLqVu7VpMGjeEfu2b+l2SyGEUAiJBVlxSyp8/XcuLs9fTt31TXrh6ACdU8jKQIjVFISASRDv2FfCbtxbz3YadXD04kYcu6KkhlBLSFAIiQbJo025uemMRu/MKw/IAsEQnhYDIcXLO8frcDB6ZsZLWcfWZetNQTjzBnzmARCpLISByHPYXFPPA+8uZtmQrp3dvyd8u66ex9BJWFAIiVVBcUso7qZk88/k6snPzufuswAlgoXoGsMjRKAREKmnnvgJuenMR8zbuYkBiU569qj8nJTX3uyyRKlEIiFRC2pa9/Pr1hWzfV8CfL+3LJQPahvT0DyLlUQiIVNC0JVu4d8oymsXW5b0bTqZPO538JeFPISBSjpJSx5MzV/PPORs4KakZ/7h6oGbQlIihEBA5hr15RdwyaRH/XbeDnw9J5KHzT6RubV2LSSKHQkDkKNZuy+X611LZuucAf7y4N1cOSvS7JJGgK/cjjZlNNLNsM0s7wmN3mZkzs3jvezOzZ8ws3cyWmdmAMsuOMbN13m1McLshElyfrPiBi57/hrzCEiaPG6IAkIhVke3aV4CzD200s/bAmcCmMs3nAMnebRzwgrdsc2A8MBgYBIw3s2bHU7hIdSgtdfxt1lp+/fpCurRqzIe3DGNgBw3/lMhVbgg45+YAu47w0N+Ae4Cy16ccBbzmAuYCTc2sDXAWMMs5t8s5txuYxRGCRcRPWXsPcM3E+Tz9+TpGD2zH2+OG0Dquvt9liVSrKh0TMLOfAVucc0sPGSPdFthc5vtMr+1o7SK+yy8q4fXvMnj2i3UUlzoev6g3Vw5qr/H/EhUqHQJmFgs8CIw80sNHaHPHaD/S+scR2JVEYqL2w0r1KC11pG3dy4xlWby/eAvbcws4NTmeR0b1Iim+od/lidSYqmwJdAY6Age3AtoBi8xsEIFP+GXnz20HbPXaRxzS/tWRVu6cmwBMAEhJSTliUIhUVnZuPlMXbWH5lr3s3l/IqqwcducVUbuWMaJbAtcO68TJnVv4XaZIjat0CDjnlgP//0KpZvY9kOKc22Fm04FbzGwygYPAe51zWWb2CfB4mYPBI4H7j7t6kXLk5BcxYfYGXvp6IweKSujQIpaERvU4vXsrhiW3YHjXljRvWNfvMkV8U24ImNkkAp/i480sExjvnHvpKIt/BJwLpAN5wFgA59wuM3sEWOAt9wfn3JEONosERV5hMW/O3cQ/vkpnd14R5/dpwx1ndqVTQiO/SxMJKeZc6O5xSUlJcampqX6XIWEkOzefN77L4LW5GezJK+LU5HjuOas7vdvpIi8SPcxsoXMupSLL6oxhCXvOOb5O38GEORv4On0HAGf2aMWvh3dmYAedjiJyLAoBCTubdubx/c79bMvJJzu3gI+WZ7Fiaw4tG9fj/05P5mf9TqCzdvuIVIhCQMLCum25/Gd5Fh8tz2Lttn0/eqxHmyY8eUkfRvU/gXq1Y3yqUCQ8KQQkpKVt2cvvp68gNWM3ZnBSh+aMv6AnvdrG0bJxPZo1rEuT+rqmr0hVKQQkJBWXlPL8l+t59ot1NI2ty+/O78kFfdrQsommcRAJJoWAhJy9eUXc9NZCvknfyYX9TuDhn/UiLlaf9kWqg0JAQkrGzv386pUFbNqVx5Oj+3BZSvvynyQiVaYQkJCxMGM31726AAe8fu1ghnTSNA4i1U0hICFhZloWt05eQpu4+rw8dhAdNYmbSI1QCIivCopLeHTGKl6fm0G/9k15aUwKLRrpIu4iNUUhIL7JzsnnxjcXsTBjN9cO68jdZ3Wjfh2N8xepSQoB8cWSzXv49eup5Bwo5vmrBnBenzZ+lyQSlRQCUuOmLMzk/veX07JxPabcOJSeJzTxuySRqKUQkBqTV1jMIzNWMWn+JoZ2bsFzVw3QXP4iPlMISI1YlrmH2yYvYePO/dwwvDN3jexK7ZhafpclEvUUAlKtSkodL85ez99mrSWhcT3eum6ILuMoEkIUAlJtMnfnccc7S5m/cRfn92nDYxf21vQPIiFGISDV4r/rtnPLW4spKXX89bK+XNS/LWbmd1kicgiFgARVaaljwn838OTM1SS3bMyEawbSoYXO/hUJVQoBCZrtuQXc+e5S5qzdzrm9W/PU6L40rKc/MZFQpv9QCYo5a7dzxztLyc0v4tELe3H14ETt/hEJAwoBOS5FJaX8+dM1/HP2BpJbNuLN6wbTrXVjv8sSkQpSCEiVpWfncue7y1i6eQ9XDU7kd+f1pEFdzf0jEk4UAlJphcWlvPDVep7/Mp3YejH84+oBnNtbc/+IhCOFgFTK5l15/Pr1hazMyuFnfU/goQt6Eq+pn0XClkJAKmz+xl3c8MZCiktK+dc1KZzZs5XfJYnIcVIISLmcc7wxN4M/zFhJ+2ax/HtMCp0SGvldlogEgUJAjml7bgH3TlnGF6uzGd41gWeu6K+pH0QiiEJAjmjzrjw+XbmNf3yZzr6CYn5/QU/GDE3S2H+RCKMQkP9v654DvLcwk6mLMvl+Zx4Ag5Ka8+hFvejaSmP/RSKRQiDK5BUWM2NpFos372H5lj1k7j5A3Zha1KtTiy27D1DqYGjnFvxyaBJDu8TrzV8kwikEosS+gmImz9/Ei7PXs2NfIU3q16ZPu6b0a9+U4hJHflEJF/Vvx+gB7UhsEet3uSJSQxQCEWx/QTH/XbeDb9J38MGSLeTmF3NKlxa8eEZXBnZopv37IqIQiFTTl27l4ekr2Lm/kPp1anFGj1Zcd2on+rVv6ndpIhJCFAIRprC4lMf+s5JXv8ugf2JTnr2yPylJzalbW9fzFZHDKQQiyNY9B7jpzUUs2byHcad14u6zulFHF3MXkWMo9x3CzCaaWbaZpZVpe8rMVpvZMjN738yalnnsfjNLN7M1ZnZWmfazvbZ0M7sv+F2Jbl+v28H5z35NevY+Xrh6AA+c20MBICLlqsi7xCvA2Ye0zQJ6Oef6AGuB+wHMrCdwBXCi95x/mFmMmcUAzwPnAD2BK71l5Tg553j+y3R+MXEeCY3qMf2WUzhHM3qKSAWVuzvIOTfHzJIOafu0zLdzgdHe/VHAZOdcAbDRzNKBQd5j6c65DQBmNtlbduVxVR/lCotLuW/KMqYu3sKofifwx4t7E1tXe/hEpOKC8Y7xK+Bt735bAqFwUKbXBrD5kPbBQfjZUSu/qIRxry8MXNbxzK785vQuGvIpIpV2XCFgZg8CxcCbB5uOsJjjyLud3FHWOQ4YB5CYmHg85UWs4pJS/m/SYuas3c4TF/fmikH6PYlI1VT5yKGZjQHOB652zh18Q88E2pdZrB2w9Rjth3HOTXDOpTjnUhISEqpaXsQqLXXc894yPl25jd9f0FMBICLHpUohYGZnA/cCP3PO5ZV5aDpwhZnVM7OOQDIwH1gAJJtZRzOrS+Dg8fTjKz36OOd48IM0pi7ewl0ju/LLUzr6XZKIhLlydweZ2SRgBBBvZpnAeAKjgeoBs7z90HOdczc451aY2TsEDvgWAzc750q89dwCfALEABOdcyuqoT8Rq6TU8dsP0pg0fxM3jejMLacn+12SiEQA+9+enNCTkpLiUlNT/S7Dd/lFJdw2eQkzV/zAjSM6c89Z3XQQWESOyswWOudSKrKsxhOGuJz8Iq5/NZV5G3fxu/N7cu0w7QISkeBRCISwrXsOcO2rqazblsvTV/RjVL+25T9JRKQSFAIhamHGLn79+iLyi0p46ZcnMbyrRkqJSPApBEKMc463F2zmd9PSaNu0AZOuH0yyru4lItVEIRBC9uYV8cAHy/nPsixOTY7nuSsHEBdbx++yRCSCKQRCQGmpY8qiTP40czV78oq49+zujDutEzG1NAJIRKqXQsBHBcUlfLg0i3/OXs+67H0MSGzKK2N70attnN+liUiUUAj4YMueA7w1L4O3F2xmx75CurduzNNX9OOCPidQS5/+RaQGKQRqiHOOr9N38Np3GXy+ahsAp3dvxTUnd+DU5Hid/CUivlAIVLNd+wuZuiiTt+ZtYsOO/TRvWJdfD+/M1YMTadcs1u/yRCTKKQSqQWFxKd9t2Mk7Czbz6cofKCpx9E9syt8u78u5vdtQr3aM3yWKiAAKAfYVFLN40272FxQDRpu4+iTFNySuQcWHZubmF7EqK5eVW/fyzfqdfJu+g/2FJTSLrcMvhiRx+Unt6dZaY/1FJPREXQg451iZlcPstduZs3Y7qd/vprj08En0kls2okebJrRqUo+XTTAgAAAIlklEQVSWjevT0vvaqF5t1mzLZe22XLbl5LN22z5W/5DDwXn42jVrwIX92zK8awLDuyXoU7+IhLSoCYHNu/J4Y24GM5ZlsWXPAQB6tGnCdad2YliXeFo0qktJqWPrngOs3ZZLasZulmzew7acfAqKSw9bX93atWjdpD4dWsTyf6cn07d9HD3aNKF1k/o6yCsiYSOiQ8A5x+ZdB3h97ve8+m0Gpc5xanI8t56RzPCuCbRqUv+w5/RqG8fIE1v/aB05+cVsz81nW04BOQeKSG7ViE7xjTScU0TCXkSGQHZOPtdMnM+mXXnkFZZgBqMHtOPOkd1oHXf4G/+xmBlxDeoQ16AOXVpqv76IRJaIDIG42Dq0axbL0M7xJMXH8pNuLWnfXMMxRUQOFZEhUK92DP8eU6GL6oiIRLUqXWheREQig0JARCSKKQRERKKYQkBEJIopBEREophCQEQkiikERESimEJARCSKmXOHz6AZKsxsO5BRgUXjgL0VXG0oLJsIbPK5hoouG6n9quyy4dS3SO1XZZcNp74Fu18dnHMJFVqbcy7sb8CEMFt2ewjUUKFlI7Vfkdy3SO1XJPetuvpVkVuk7A76MMyW3RMCNVR02UjtV2WXDae+RWq/KrtsOPWtuvpVrpDeHRSpzCzVORdxkxtFar8gcvsWqf2CyO1bsPsVKVsC4WaC3wVUk0jtF0Ru3yK1XxC5fQtqv7QlICISxbQlICISxRQCQWBmE80s28zSyrT1NbPvzGy5mX1oZk289rpm9rLXvtTMRhxhfdPLrstPweqbmV1uZsvMbIWZPelDV37EzNqb2Zdmtsqr6VavvbmZzTKzdd7XZl67mdkzZpbu9WPAIetrYmZbzOw5P/pTpo6g9cvM/mRmad7tcr/6VKaeyvatu/d3WmBmdx1hfTFmttjMZtR0Xw6pI2j9MrNbvddrhZndVqECgjnUKFpvwGnAACCtTNsCYLh3/1fAI979m4GXvfstgYVArTLPuxh4q+y6wr1vQAsC45oTvMdeBX7qc7/aAAO8+42BtUBP4EngPq/9PuBP3v1zgY8BA4YA8w5Z39Pe6/ZcJPQLOA+YReDCUw2BVKBJmPWtJXAS8Bhw1xHWd4f3ms2IhH4BvYA0INZ73T4Dksv7+doSCALn3Bxg1yHN3YA53v1ZwCXe/Z7A597zsgkM90oBMLNGBP4wH63mkissSH3rBKx1zm33lvuszHN84ZzLcs4t8u7nAquAtsAoAiGF9/VC7/4o4DUXMBdoamZtAMxsINAK+LQGu3BEQexXT2C2c67YObcfWAqcXYNdOUxl++acy3bOLQCKDl2XmbUjEHT/roHSjymI/eoBzHXO5TnnioHZwEXl/XyFQPVJA37m3b8UaO/dXwqMMrPaZtYRGFjmsUeAvwB5NVloFVS2b+lAdzNLMrPaBP6Y2xMizCwJ6A/MA1o557Ig8M9J4FMXBP4pN5d5WibQ1sxqEXjN7q6peivqePpF4LU8x8xizSwe+Anh95ody9+Be4DSaiqxSo6zX2nAaWbWwsxiCWzllfuaKQSqz6+Am81sIYFNvEKvfSKBf7RUAn+I3wLFZtYP6OKce9+PYiupUn1zzu0GbgTeBv4LfA8U13DNR+RtfU0BbnPO5Rxr0SO0OeAm4CPn3OYjPO6b4+2Xc+5T4CMCr+Ek4DvC7zU72vPPB7KdcwuDXtxxON5+OedWAX8isHU+k0CQl/uaReSF5kOBc241MBLAzLoS2PTE20y7/eByZvYtsA4YDgw0s+8JvC4tzewr59yImq28fFXoG865D/HOijSzcUBJzVZ9ODOrQ+Cf7k3n3FSveZuZtXHOZXm7RbK99kx+/KmqHbAVOBk41cxuAhoBdc1sn3PuvprpxeGC1C+cc48R2O+Mmb2F91r6qZJ9O5pTgJ+Z2blAfaCJmb3hnPt59VV+bEHqF865l4CXvHU+TuD1PSZtCVQTM2vpfa0F/BZ40fs+1swaevfPJPBJeaVz7gXn3AnOuSRgGIF96CN8Kb4cle3bIc9pRuDTs6/7Ys3MCPyzrHLO/bXMQ9OBMd79McC0Mu3XeKNphgB7vX25VzvnEr3X7S4C+9f9DICg9MsbOdPCW2cfoA8+H/OoQt+OyDl3v3OunfeaXQF84XMABKVf3roO/p8lEhhkMqncAqrzqHe03LxfdBaBAzWZwLXArQSO8q8FnuB/J+YlAWsIHPz5jMBsf4euL4nQGR0UlL5561np3a4IgX4NI7A7ZxmwxLudS2Ak0+cEPvV+DjT3ljfgeWA9sBxIOcI6f4n/o4OC0i8Cn5APvl5zgX5h+Jq19v5mcwgMUsjkkBFOwAj8Hx0UtH4R2N26ksCuoAqNwNMZwyIiUUy7g0REophCQEQkiikERESimEJARCSKKQRERKKYQkCkDDMrMbMl3iyMS83sDu98iGM9J8nMrqqpGkWCSSEg8mMHnHP9nHMnAmcSGK89vpznJAEKAQlLOk9ApAxvyodGZb7vRGDq7HigA/A6gamVAW5xzn1rZnMJzOC4kcBsj88QOIluBFAPeN45988a64RIJSgERMo4NAS8tt1AdyAXKHXO5ZtZMjDJOZdigYvn3OWcO99bfhzQ0jn3qJnVA74BLnXObazRzohUgCaQEynfwZk26wDPeTO+lgBdj7L8SKCPmY32vo8DkglsKYiEFIWAyDF4u4NKCMzgOB7YBvQlcDwt/2hPA37jnPukRooUOQ46MCxyFGaWQGCG1OdcYL9pHJDlnCsFfgHEeIvmEriuwkGfADd60wNjZl0Pzq4qEmq0JSDyYw3MbAmBXT/FBA4EH5ze9x/AFDO7FPgS2O+1LyNwYaClwCsErjecBCzypgnezv8u5ygSUnRgWEQkiml3kIhIFFMIiIhEMYWAiEgUUwiIiEQxhYCISBRTCIiIRDGFgIhIFFMIiIhEsf8HPFql74kqGVUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.trend.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.seasonal.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.resid.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADF Test\n",
    "Let's Check the Staionarity of our data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series,autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic        0.901284\n",
      "p-value                   0.993107\n",
      "# lags used              17.000000\n",
      "# observations          330.000000\n",
      "critical value (1%)      -3.450322\n",
      "critical value (5%)      -2.870338\n",
      "critical value (10%)     -2.571458\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that p>0.05 so,\n",
    "can't reject the null hypothesis. And also\n",
    "data has a unit root and is non-stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's apply differencing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a differenced df\n",
    "\n",
    "def difference(dataset, interval=1):\n",
    "    diff = list()\n",
    "    for i in range(interval, len(dataset)):\n",
    "        value = dataset.iloc[i] - dataset.iloc[i - interval]\n",
    "        diff.append(value)\n",
    "    return diff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let take first difference of the data\n",
    "\n",
    "df_first_diff = difference(df['Employees'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Data frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -3.068279\n",
      "p-value                   0.028992\n",
      "# lags used              17.000000\n",
      "# observations          329.000000\n",
      "critical value (1%)      -3.450384\n",
      "critical value (5%)      -2.870365\n",
      "critical value (10%)     -2.571472\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "# Let's again do  adf test\n",
    "\n",
    "adf_test(df_first_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # BOXCOS TRANSFORM\n",
    "<ul>\n",
    "    <li> lambda = -1. is a reciprocal transform.</li>\n",
    "    <li>lambda = -0.5 is a reciprocal square root transform.</li>\n",
    "    <li>lambda = 0.0 is a log transform. </li>\n",
    "    <li>lambda = 0.5 is a square root transform. </li>\n",
    "    <li>lambda = 1.0 is no transform. </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.stats import boxcox\n",
    "# from matplotlib import pyplot\n",
    "# dataframe = pd.DataFrame(df.values)\n",
    "# dataframe.columns = ['Employees']\n",
    "# dataframe['Employees'], lam = boxcox(dataframe['Employees'])\n",
    "# print('Lambda: %f' % lam)\n",
    "# pyplot.figure(1)\n",
    "# # line plot\n",
    "# pyplot.subplot(211)\n",
    "# pyplot.plot(dataframe['Employees'])\n",
    "# # histogram\n",
    "# pyplot.subplot(212)\n",
    "# pyplot.hist(dataframe['Employees'])\n",
    "# pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ACF and PACF Plot for p,d,q values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "plot_acf(df['Employees']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B/HGp+RGxJiI6IqKjvb09haqZmVkprSksoxuYXfD8TKCnuJCkS4AvAL8eEa+nsF4zMxulNHr+G4H5kuZJagOWA+sKC0g6H/hL4PKI6E1hnWZmVoWqwz8ijgDXAg8B24D7I2KLpC9Jujwp9sfAFOABSZslrRtmcWZmNgbSGPYhItYD64um3Vzw+JI01mNmZunwHb5mZhnk8DczyyCHv5lZBjn8zcwyKJUTvpZdA4PBhu29bOk5wHmzprL43BnkWkrd92dm9cThnwG1CuiBweDKOx9nc9d+DvcPMLktx6LZ01i76iLvAMzqnMO/ydUyoDds72Vz134O9Q8AcKh/gM1d+9mwvZeLF8xMo/pmViMe829yhQEdHB3Q1drSc4DDSfAPOdw/wNaeA1Uv28xqy+Hf5GoZ0OfNmsrkttxR0ya35Vg4a2rVyzaz2srUsM+jz+07ZtqBw28MO68Z5CTaWlt4/cjgm9PaWltokare5kmtOeZNP5EtP38Bcq1MnNDKvOknMqk117Tvp9lYeNfZp9V8He75N7lFs6dxzowpcKQfYpCJrS2cM2MKi2ZPK1l+cDB4cvdL/N2T3Ty5+yUGB0t+OzcALS3ixqULmLL1u0z+2T/xmX8/nxuXLqDFJ3vN6l6mev5ZNBTQ1/zOdQxMmcm1n1zNotnTSgb04GBw64Pb6Ow9SP+RQdqSHcVIgd7SItr2dcK+Ti6Yc32tN8ca0OBgsLlrP7v2vcrc004c9vNnY8vhnwHlBvTmrv109h58c4jo9SODdPYeZHPXfi6Yc8pYVdeayGg6FDY2POxjb9q171X6C84NAPQfGWTXvlfHqUY2VioZ7qtEYYciOLpDYePLPX9709zTTix5cnjuaSeOY62s1mrZOx+pQ+GjyfHlnr+9qdKTw9Ycatk7H+pQFHKHoj44/O1Nvnonm2o53OcORf1y+NtRhk4OT979Ey6Yc4qDPwNq2Tt3h6J+OfxtzNTqpKJVZzS980rvB3GHov74hK+NCV/yV78quRcE3JbNwj1/GxP1dMlfox6BVFrvWvXOa92Wjdo+jSaVnr+kJcCfATngjoj4ctH8icA9wL8F9gFXRMSuNNZtjaFeLvlr1F5rpfVu1Ms3a90+ldxtXMs7k+vhrueqw19SDrgduBToBjZKWhcRWwuKrQJeiohzJC0H/jtwRbXrzqp6+OBUql7uIaj1Xcy1aptK613L7axlW9ay3pXsWGq5E6qXDkgawz4XAp0RsTMi+oH7gGVFZZYBdyePvw1cLKm+06pODX1wbntkB99+opvbHtnBrQ9uq/tD43q55K+WlzXWsm0qrXejXr5Zy3pXMlxVy6GtehkCVUR1H0xJHwaWRMTHk+dXAhdFxLUFZZ5JynQnz59Lyrww3HJPnbMgLr3xrlHVafNTmwFY9I5FR00/8Nobx5TdsfUZAOYvfNuo1jXWXnntCM/vP0xhs0lwxrTJnDRp+AO5Sraz0vek3PIRwbOdOyHXxqxZpzNlYo6x7gOM9v1rtGWPpi6VtHulbVnusmv5Hva98jovHOw/Znr7lDamnzRx1GVrUY+pkyaMevn3f/LdT0REx/HKpTHmX6rFi/co5ZRB0mpgNcCU088edYWKQ38klYR+rT7wlZR/7Y0BivfXEfD6GwMj/ueoZDsr3RGWW14S586vrF3T3rFMmZhj8oQch5P3UYLJE3JMmZgrsdTK6jHatiln2ZXWezTbWUm7V9qW5S670npX8n9y0oQcEsfsWCZOOHbZlZQtVE5bjnbZaUsj/LuB2QXPzwR6hinTLakVOBl4sXhBEbEGWAPQ0dERf3PNu1Ko3i9V8wMjQ4f0g5OmQa6Vvlde5+TJI59wu+ahOxmYMpMP/Jv3lTX2+6lvfR6Amz+3btgyT+5+idse2XHUeOvE1haufve8pvyulHLek9G0TaXj8rVqm0o+J5XWuxHPDUH59R5Nu9d6zL+Sz+tIy67mx1zu/2R55dII/43AfEnzgOeB5cBHi8qsA1YCjwIfBh6JasebxtjQOB2tbcDIJ6KGGvfgwg9BrpXbHtlx3A/O4GDQf9o5DEyZyZO7Xxr2Az803lr8wWnG2+XLfU8qaRvIX9Z4wZxTUt9ZVto2lX5OKq13rbaz1sqt92ja/calC8rasVRSdjTbV6tlV6Lq8I+II5KuBR4if6nnXRGxRdKXgE0RsQ64E1grqZN8j395tesda5Vc3lbph7KSEKiXD06tVfKe1MtlpJW2TaWfEzvaaNq9kh1iLXee9bBjTuU6/4hYD6wvmnZzwePXgN9KY13jpZLL2yr9UNZLz7WeVPKe1MtlpFBZ29TLTqtR1VO7NyLf4VumoUP6ia0tCEa8vK3SL8ryj6gcq5L3pJK2qSf+uuPqNGq71wt/t0+ZKjmkr3Ts1z2YY1XynjTqUFiWzt/UQqO2e71w+Feg3EP6Sj+UDoFjVfqeNOJQmMOreo3Y7vXC4V8jlZ5YcggcLSvvicPLxovDv044BI7l98SsdnzC18wsgxz+ZmYZ5PA3M8sgh7+ZWQY5/M3MMihTV/tU8015lm1TJ+e/X92fIRtJI31O3PM3M8sgh7+ZWQY5/M3MMsjhb2aWQQ5/M7MMcvibmWWQw9/MLIMc/mZmGeTwNzPLIIe/mVkGOfzNzDKoqvCXdKqkH0rakfw95ieXJC2S9KikLZKelnRFNes0M7PqVdvzvwF4OCLmAw8nz4sdAq6KiPOAJcCfSsruL5ObmdWBasN/GXB38vhu4EPFBSLi2YjYkTzuAXqB9irXa2ZmVag2/GdGxB6A5O+MkQpLuhBoA54bZv5qSZskberr66uyamZmNpzjfp+/pP8L/EqJWV+oZEWSTgfWAisjYrBUmYhYA6wB6OjoiEqWb2Zm5Ttu+EfEJcPNk7RX0ukRsScJ995hyk0Fvg/cFBGPjbq2ZmaWimqHfdYBK5PHK4HvFReQ1AZ8B7gnIh6ocn1mZpaCasP/y8ClknYAlybPkdQh6Y6kzEeAXwOulrQ5+beoyvWamdWVgcHg0LS3sP+Md/Hwtr0MDNb3yHVVv+EbEfuAi0tM3wR8PHn8TeCb1azHzKyeDQwGV975OH3zP0i0tPLpe3/KotnTWLvqInItGu/qleQ7fM3MqrRhey+bu/YTuTZQC4f6B9jctZ8N20ueBq0LDn8zsypt6TnA4f6Bo6Yd7h9ga8+BcarR8Tn8zcyqdN6sqUxuyx01bXJbjoWzpo5TjY7P4W9mVqXF585g0expnNCWQ8AJbTkWzZ7G4nNHvO91XFV1wtfMzCDXItauuogN23vZ2nOAhbOmsvjcGXV7shcc/mZmqci1iIsXzOTiBTPHuypl8bCPmVkGOfzNjqPRbt4xK4eHfcxG0Ig375iVwz1/sxE04s07ZuVw+JuNoBFv3jErh8PfbASNePOOWTkc/mYjaMSbd8zK4RO+ZiNoxJt3zMrh8Dc7jka7ecesHB72MTPLIIe/mVkGOfzNzDLI4W9mlkEOfzOzDFJEfX5JlaQ+YHcVi5gOvJBSdepVFrYRsrGd3sbmMd7bOSci2o9XqG7Dv1qSNkVEx3jXo5aysI2Qje30NjaPRtlOD/uYmWWQw9/MLIOaOfzXjHcFxkAWthGysZ3exubRENvZtGP+ZmY2vGbu+ZuZ2TAc/mZmGdR04S9piaTtkjol3TDe9akVSbsk/YukzZI2jXd90iDpLkm9kp4pmHaqpB9K2pH8PWU865iGYbbzFknPJ+25WdJl41nHakmaLelHkrZJ2iLpd5LpTdOeI2xjQ7RlU435S8oBzwKXAt3ARmBFRGwd14rVgKRdQEdENM1NM5J+DTgI3BMRb0umfQV4MSK+nOzMT4mI68ezntUaZjtvAQ5GxJ+MZ93SIul04PSIeFLSScATwIeAq2mS9hxhGz9CA7Rls/X8LwQ6I2JnRPQD9wHLxrlOVqaI+DHwYtHkZcDdyeO7yf/namjDbGdTiYg9EfFk8vgVYBtwBk3UniNsY0NotvA/A+gqeN5NAzVGhQL4gaQnJK0e78rU0MyI2AP5/2xAM/9+4rWSnk6GhRp2OKSYpLnA+cDjNGl7Fm0jNEBbNlv4l/ptveYZ1zraeyLiAmAp8KlkKMEa158DZwOLgD3AV8e3OumQNAX4W+B3I+LAeNenFkpsY0O0ZbOFfzcwu+D5mUDPONWlpiKiJ/nbC3yH/JBXM9qbjK0OjbH2jnN9aiIi9kbEQEQMAn9FE7SnpAnkQ/GvI+LvkslN1Z6ltrFR2rLZwn8jMF/SPEltwHJg3TjXKXWSTkxOMCHpROB9wDMjv6phrQNWJo9XAt8bx7rUzFAgJv4DDd6ekgTcCWyLiK8VzGqa9hxuGxulLZvqah+A5LKqPwVywF0R8UfjXKXUSXoL+d4+QCvwrWbYTkn3AovJfyXuXuCLwHeB+4GzgJ8DvxURDX2ydJjtXEx+mCCAXcA1Q2PjjUjSe4F/Av4FGEwm30h+TLwp2nOEbVxBA7Rl04W/mZkdX7MN+5iZWRkc/mZmGeTwNzPLIIe/mVkGOfzNzDLI4W9mlkEOfzOzDPr/KD1SePJ1NvgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(df['Employees']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Emp_1d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-01</th>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-01</th>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Emp_1d\n",
       "1990-02-01    10.0\n",
       "1990-03-01    15.5\n",
       "1990-04-01     7.4\n",
       "1990-05-01    11.3\n",
       "1990-06-01    14.8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.date_range('1990-02-01','2018-12-01', freq='MS')\n",
    "df_first_diff = pd.DataFrame(df_first_diff, index=idx, columns=['Emp_1d'])\n",
    "df_first_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(df_first_diff['Emp_1d']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(df_first_diff['Emp_1d']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "nobs = 12\n",
    "train, test = df_first_diff['Emp_1d'][:-nobs], df_first_diff['Emp_1d'][-nobs:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-02-01    10.0\n",
       "1990-03-01    15.5\n",
       "1990-04-01     7.4\n",
       "1990-05-01    11.3\n",
       "1990-06-01    14.8\n",
       "Freq: MS, Name: Emp_1d, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -40.4\n",
       "2018-02-01    27.7\n",
       "2018-03-01    17.7\n",
       "2018-04-01    22.7\n",
       "2018-05-01    16.6\n",
       "Freq: MS, Name: Emp_1d, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:963: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:975: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:963: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:975: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\base\\model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>348</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 3)</td> <th>  Log Likelihood     </th> <td>-1360.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Feb 2020</td> <th>  AIC                </th> <td>2734.969</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:25:36</td>     <th>  BIC                </th> <td>2761.914</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>2745.697</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 348</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.7014</td> <td>    0.131</td> <td>    5.337</td> <td> 0.000</td> <td>    0.444</td> <td>    0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    1.7316</td> <td>    0.001</td> <td> 1329.158</td> <td> 0.000</td> <td>    1.729</td> <td>    1.734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>     <td>   -0.9998</td> <td>    0.001</td> <td>-1777.587</td> <td> 0.000</td> <td>   -1.001</td> <td>   -0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -2.1871</td> <td>    0.043</td> <td>  -50.402</td> <td> 0.000</td> <td>   -2.272</td> <td>   -2.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    1.8041</td> <td>    0.075</td> <td>   23.980</td> <td> 0.000</td> <td>    1.657</td> <td>    1.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>     <td>   -0.4704</td> <td>    0.043</td> <td>  -10.934</td> <td> 0.000</td> <td>   -0.555</td> <td>   -0.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>  145.6796</td> <td>   10.765</td> <td>   13.533</td> <td> 0.000</td> <td>  124.580</td> <td>  166.779</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>778.33</td> <th>  Jarque-Bera (JB):  </th> <td>65.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>0.00</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>1.24</td>  <th>  Skew:              </th> <td>-0.77</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.25</td>  <th>  Kurtosis:          </th> <td>4.47</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  348\n",
       "Model:               SARIMAX(2, 1, 3)   Log Likelihood               -1360.484\n",
       "Date:                Thu, 20 Feb 2020   AIC                           2734.969\n",
       "Time:                        23:25:36   BIC                           2761.914\n",
       "Sample:                             0   HQIC                          2745.697\n",
       "                                - 348                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.7014      0.131      5.337      0.000       0.444       0.959\n",
       "ar.L1          1.7316      0.001   1329.158      0.000       1.729       1.734\n",
       "ar.L2         -0.9998      0.001  -1777.587      0.000      -1.001      -0.999\n",
       "ma.L1         -2.1871      0.043    -50.402      0.000      -2.272      -2.102\n",
       "ma.L2          1.8041      0.075     23.980      0.000       1.657       1.952\n",
       "ma.L3         -0.4704      0.043    -10.934      0.000      -0.555      -0.386\n",
       "sigma2       145.6796     10.765     13.533      0.000     124.580     166.779\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                      778.33   Jarque-Bera (JB):                65.79\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.24   Skew:                            -0.77\n",
       "Prob(H) (two-sided):                  0.25   Kurtosis:                         4.47\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pmdarima import auto_arima\n",
    "auto_arima(df['Employees'],seasonal=False).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kishan\\AppData\\Roaming\\Python\\Python36\\site-packages\\statsmodels\\base\\model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "model = ARIMA(train, order=(2,1,3))\n",
    "model_fit =  model.fit(trend='nc', disp=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "first_predictions = model_fit.predict(start=start, end=end, dynamic=False, typ='levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -4.248242\n",
       "2018-02-01    6.855977\n",
       "2018-03-01   -2.601638\n",
       "2018-04-01    7.516900\n",
       "2018-05-01   -2.336344\n",
       "2018-06-01    7.623379\n",
       "2018-07-01   -2.293598\n",
       "2018-08-01    7.640530\n",
       "2018-09-01   -2.286707\n",
       "2018-10-01    7.643289\n",
       "2018-11-01   -2.285593\n",
       "2018-12-01    7.643730\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sltVmIYQQzsuWyfL5wFogAdU6DuARYDPwAxACpAGX6bqee6bHkmT53Om6Tv/n/mJ497a8enkfo8OxqL0ZhTw4fye7jhQyMSqIpyZH4u/dsJXzY4XlLNuVyZKEDLam5GLWoUvbFkyICmJCZDC92rVqkgny6chqsxBCCGcnQ0maiLScUoa/tJJnp0Zy9aBORodjcdUmMx+sTeb1Pw/Q3MOVJy+OYGqf9vVKbI/ml7F0VyZLEzKITctD1yEssCUTIoOZGBVMWGBLSZDPQlabhRBCOCtJlpuIX7Yf4d7vd7Dk7mFEtHPeFb+Dx4t5aP5O4tLyGd0jgOcuiSTYp9m/bpeeW8rSXRksSchkR3o+AD2DWzExMogJUUF0C/C2dehOQ1abhRBCOBNJlpuIJxbuYkHsYeJnj3OaTg2nYzLrfL4hhZeWJ+LmovHoRT25vH9HUnJUgrw0IZOEIwUARLX3OVFi0bltC4Mjdy6y2iyEEMIZSLLcRFz05lp8m7vz9X8HGR2KzaTllDJzQTwbk3MI8PbkeFEFAH1DfFUXi8hgOvo1NzjKpkFWm4UQQjgqSZabgNLKaqJm/87tI7ty/9gzduVzOmazzndb01mx7xhDurZlfGQQ7Xz/XZYhbENWm4UQQjgaSZabgI1JOVzx4SY+vb4/o3oEGB2OEICsNgshhHAM9U2W3WwRjLCOuLQ8QJUfCGEvWrfw4L/DunDT+Z1PrDZ/uzWdzzemymqzEEIIhyPJsgOLS82jq38LfJtbb8KdEA2laRoDu7RhYJc2PFlrtfnB+fE8/dseRoUHENGuFT2DW9Ez2JsAby+jQxZCCCH+RZJlB6XrOnFpeVzQM9DoUIQ4q9qrzVtT8vh2Sxqbk3P4defRE7dp29KjJnFWyXPP4FZ09W+Ju6uLgZELIYRo6iRZdlCHskvIK62iX6fWRociRL1pmsaAzn4M6OwHQH5pJXszitibUah+ZRby2foUKk1qGKiHqwvdAlqeSKAjapLp1i1kN0UIIYRtSLLsoOLS1MCNaEmWhQPzbe7B4K5tGNy1zYmvVZnMHMouYW9GIXsyCtmbUcSaA1ksiDt84jZBrbxOrD7//atz2xZO32tcCCGE7Umy7KDi0vLw9nKjm39Lo0MRwqLcXV0IC/QmLNCbKX3an/h6dnHFPyvQNavRaw9kU21WHX283F0IDzw5ge4R7E0rL3ej/ihCCCGcgCTLDiouNY++Ia1xkZU00US0benJsO7+DOvuf+JrFdUmDh4vZm9GEXuOqkR62e5MvtuafuI2HVo3O5E8R9SsRnds3Vx+doQQQtSLJMsOqKi8isRjRYyPDDI6FCEM5enmSq92PvRq5wP91Nd0XSezsPzECvSemtXov/Yeo2YRmhYervSodZCwZ3AregR509xD3hKFEEKcTD4ZHNDO9AJ0HaJDpF5ZiFNpmkawTzOCfZoxusc/3WLKKk0kHqt1mDCjkIXbj/LVprSa+0FomxYqgQ6qKeVo14p2Pl5omqxCCyFEUyXJsgOKTc1D06CPDCMRot6aebjSp6MvfTr+83Oj6zqH88pOrD7vzShk15FCliRknriNTzN3egR515RxqCS6e2BLvNxlqIoQQjQFkiw7oLi0PMIC5OCSEI2laRod/ZrT0a8543r9U9ZUVF5FYmZRTUcO9d/vt6ZTVmUCwNVFo0vbFif1hY4IboW/t6esQgshhJORZNnBmM0629PyuKh3sNGhCOG0vL3ciQn1IybU78TXTGad1JySk/pCb0vJPWmwSpsWarDKeR19uGJACB1aNzcifCGEEBYkybKDScoqprC8WuqVhbAxVxeNLv4t6eLf8qSL1boGq7y3Opn3ViczqXcwM4Z3UQcQhRBCOCRJlh1MXFoeIMNIhLAXdQ1WOZpfxifrDvHtljQW7jjKsO5tuWV4V4Z2ayNlGkII4WBcjA5AnJu41Hx8m7vTpW0Lo0MRQpxGO99mPDYpgg2zxvDQ+HD2ZRZx9cebuejNdSzccYTqmnHeQggh7J8kyw4mNi2P6JDWsjolhAPwae7O7SO7sW7mKOZOj6K82sQ93+1g5Mur+HT9IUorq40OUQghxFlIsuxACkqrOHi8mGhpGSeEQ/F0c+Xy/iH8+b8RfHhtDEGtvHhq0R4Gv7CCV35PJLu4wugQhRBCnIbULDuQ7ek19cpyuE8Ih+TionFhRCAXRgQSm5rL+6uTeXvlQd5fk8yl/Tpw87AudJYSKyGEsCuSLDuQuNQ8XDQ4r6OsLAvh6Pp18uODa/1Iyirmo7WHmB97mG+3pDEuIohbRnShr1wUCyGEXZBk2YHEpeXTI6gVLTzlZRPCWXT1b8kL06K478IwPt+QwhcbU1i2O5MBoX7cMqILo8IDcHGRMwpCCGEUqVl2ECazzo70fKI7yaqyEM7I39uTB8aFs+HhMTw+KYIj+WXc9Pk2xr6+hh+2plNRbTI6RCGEaJIkWXYQ+48VUVxRTT/pryyEU2vp6cZN53dm1YMjef3yPri7uvDQgniGzV3Ju6uSKCirMjpEIYRoUiySLGua9ommacc1TdtV62t+mqb9oWnagZr/SpbXCCeGkUgdoxBNgrurC1P7tmfJ3efzxY0DCAv0Zu6yfQyds4Lnl+wlo6DM6BCFEKJJsNTK8mfA+FO+Ngv4S9f17sBfNb8XDRSXmk+bFh6E+DU3OhQhhA1pmsbwMH+++u9AfrvrfEb3CODjdYcYNncl9/+wk8TMIqNDFEIIp2aRZFnX9TVA7ilfngJ8XvP/nwNTLfFcTVVcWh7RnWQYiRBNWWR7H968oi+rHhjJ1YM6sSQhg3Gvr+H6T7ewMSkHXdeNDlEIIerNbNbZc7TQ6DDOypptFQJ1Xc8A0HU9Q9O0ACs+l1PLLankUHYJ/4npaHQoQgg70NGvObMn9+KeMd35alMqn21I4YoPN9G7gw+3DO/K+MggXKWDhhDCDmUXV7D2QBarErNYeyCb3JJKNswaTTvfZkaHdlqG9yDTNG0GMAMgJCTE4Gjs0/YT9crSCUMI8Y/WLTy4a0x3bh7ehQVxh/lo7SHu+CaOEL/m3DysM5f260gzD1ejwxRCNGHVJjPb0/NZnZjF6v1ZJBwpAKBNCw9GhPkzMtyfVs3cDY7yzKyZLB/TNC24ZlU5GDhe1410Xf8A+AAgJiZG9hDrEJuah5uLRu8OkiwLIf7Ny92VqwZ24v/6h/DHnkzeW53M4wt389qfB7h2cCeuHRyKXwsPo8MUQjQRGQVlrNmvkuO1B7IpKq/G1UUjOsSXB8aGMSIsgF7tWjlMD3lrJsu/AtcBc2r+u9CKz+XU4tLyiGjXSlaIhBBn5OqiMT4ymHG9gtiakscHa5J4/c8DvLc6if/EdOS/53chpI0cEhZCWFZFtYltKXms3p/F6sQsEo+pg8dBrbyYGBnMiHB/hnZri4+dryCfjkWSZU3TvgVGAm01TTsMPIlKkn/QNO0mIA24zBLP1dRUm8zsTC/g8v5SryyEqB9N0xjQ2Y8Bnf04cKyID9cm8+2WNL7alMqEqGBuGd5FdqqEEI2SllPK6v3HWb0/iw1JOZRWmnB3Ve890/v1YERYAGGBLZ2iMYFFkmVd1684zbfGWOLxm7J9mUWUVZmIlmEkQogG6B7ozYuXnsf9Y8P5dH0KX29KZXF8BoO7tOGWEV0YEebvFB9mQgjrKqs0sSk5R60e78/iUHYJAB39mjE9ugMjwvwZ3LUNLTwNPw5ncc73J3IycXK4TwhhAYGtvJg1oQd3jOrKd1vS+XjdIa7/dCs9gryZMbwLF5/XDndXGeoqhFB0XScpq5hVNQfzNh/KpbLajKebC4O7tuHawZ0YGR5AaJvmTn/BLcmynYtLzSPA25P2dtxSRQjhOLy93Ll5eBeuGxLKop1H+WBNMvf9sJOXlidy49DO/N+Ajnh7OWZdoRCicYrKq9iQlMOqxCzW7M/iSL6aFNotoCXXDOrEiDB/BnT2w8u9aZ2hkmTZzsWm5dFPhpEI4Th0HcryoLmf0ZGckYebC9P7dWBadHtW7c/i/dVJPLdkL2+uOMBVAztx49BQAlp5GR2mbZXWzNay89dO1CH3EPh0AFe50DsXuq6zJ6PwxMG82NQ8qs06LTxcGdqtLbeP6srw7v50tNb0YFM1HFoF3S6wzuNbiCTLdiyrqIL03DKuHRRqdCjC1jJ3weo50GMS9L4c5GLJMeQkwa93Q9pGGHIXjJwF7va9K6RpGqPCAxgVHsDO9Hw+WJPMB2uS+GTdIS7p256bh3ehW0BLo8O0Ll2HHV/D8kdAB8Y9C32vkZ87R1BeAH88AbGfQWAkTH4L2kcbHZVdyy+tZO2B7BO1x1lFFQD0DG7FzcPVOYbokNZ4uFm5LCszARbeCRk74Nb1EBRp3edrBM2exqPGxMTo27ZtMzoMu7F8dya3fBnLgtsG06+TrHQ0CaYqWPsqrHkJ0MFcra64J70GvjK0x26ZTbDpHVjxHLh6QOdhsO838OuqPrxDhxod4TlJzSnh43WH+GFbOuVVZi7oGcgtI7oQ44y7XHmpsOgeSF4JIUNAc4HUddB5BFz8Bvh1NjpCcTqJy+C3/0FxJvS5Cg7+CcXHYPCdMOoRu79QtRWTWSfhSAGrElXnip3p+Zh18GnmzrDubRkR5s/wMH8CbbWTVF2hPuPWvQbNWsPElyBiqiEXp5qmxeq6HnPW20mybL9eWLKXT9enED97bMPrg47tVm/+AT0tG5ywvIx4WHi7utqOvBTGz4HdP8GfT6nvXzAb+v8XXOQQll05vhcW3gFHYiF8Ilz0KrQKhuRVapU5P1W9bhfMBk9vg4M9N7kllXyxMYXPN6SQV1pF3xBfbhnelbERgQ4zTOC0zGbY+qH6+dI09frE3KS+F/sp/PEk6CYY/TgMvAVcmlaNpl0ryYalM2HXfAiIgClvQ/t+UJavVpnjPnfYC1VLySqqqDUUJIu80io0DXp38GVEmD8jwvzp09EXV1v/HKdvhV/vhKx90Pv/YPwLhpY9SbLsBC57bwPVZp2fb2/AD7vZDOtfUytd6DDwNhj1sMN9WDcJ1ZWw9hVY+zI084NJr0LPi//5fn4aLLoXkv6CjgPVB4B/uHHxCqW6Uq2MrHkJvFrBhBchcvrJqyOVJbDiWdj0LrRqDxe/Dt0vNC7mBiqrNPFjbDofrT1EWm4pMZ1aM2d6b8ctz8g+oLZ/0zdB1zHqdTl156bgsFq1PPA7dBigEjL5uTOWrsOuBbD0ISgvhOEPwPn3gdsp0ymTV8Ovdzn0heq5qjKZ2Z6Wz+r9x1mVmMXuo4UAtG3pwfCa5HhYd3/jJnlWlqh8ZNM70KodTHodwsYaE0stkiw7uMpqM1Gzl3P1oE48Pini3O5cmAE/3wKHVkOvS8DLV62UtGoPE+aqOlhn20p1VBk74Zfb4dguiPqPen3qusrWdYj/HpbNUm86wx+Coff8+0NC2MbR7SrZOrZL7QJMmAst2p7+9na2mtJQJrPOgrjDPL9kL6UVJu4a3Y1bRnS1fm2jpZiqYMObsGqu2qIfPwfO+7/Tvx/qOsT/AMtmqp+7EQ/B0HvlEJkRCo/C4vshcQm0i4Yp8yDwDJ+NJyVnjnuheiZH88tOHMxbfzCbogo1UrpfSGtGhKsEOSLYDkZKJ6+GRXdDXoravblgtlpgsAOSLDu4nen5TJm3nneuimZiVHD977h/OfxyG1SVqQ/wvw+ppG9Rq5PHd0PYBJj4otTAGunvmq21r6oka9Jr0OOis9+vOEutquz+CQJ6wZS31PajsI2qMlg1Bza8BS38a163ifW7b3UFrHkZ1r1qeJ1eY2UVVfDUot38Fp9BjyBv5kzvTZ+Odt4LPiNelctkxkPPyTDxZfAOrN99i7Ng6YOw+2cIjFKrzO36WDdeoei6Kqv4/XF1sTP6URh0e/3LYpzkQhXUSOmth/JOTM3bf6wYgGAfL0aE+TMy3J8h3drSyl5aP5YXwO+PQdwX4NelpizmfKOjOokkyw7uk3WHePq3PWx6eAxBPvUouq+uUDV2m99Vb+aXfvzvLUNTldoOXvWC+v3IWepNR1ZJbOtInPrQPr4HzrsCxj1/7m/e+5bA4vvUYZZBt8OoR8HDSq19hJK6UX3o5hxUF6Fjn4VmDUipPIDuAAAgAElEQVQQM3ep1z9jh9rluegV8A6yfLw28OeeYzy+cBeZheXcMKQzD4wLo7mHnTVZqipXF6brX1dlThe9DBFTGvZYe39Tq5slWQ7T7cSh5R5SK5KH1kDoMHXgsk3Xc3+cf12ovgy9plo+XitZmXicLzemsjEph7IqEx6uLgzo7Kdqj8P96R5ghyOlE5fWHL607wOXkiw7uDu/iSMuNY8ND9djYnj2AZh/gzoYNuAWuPBpcD9Dgp2fBksegv1L1erkxa9DxwGWC17UrboCVs+Fda9DywBVsxU+vuGPV16gLpBiP4XWoXDxm9BlhMXCFTUqiuGvp2DLh+DbUf09dx3VuMc0VcOmebDyeXDzVBdMfa5yyFXmovIqXlyWyJebUunQuhnPXRLFiDB/o8NS0reoC5Ps/XDelTDuucavKpblqdWy7V9Bm24w+W3oNNgy8QrFbILN78OKZ0BzhbFPQ/T1jT/cXLtVWc+La3YX7PtC9atNqTy+cBftfJoxpmcAI8L8GdTFjkdKn3T40v53PyVZdnBD56ygb4gvb195hn6Rf/cGXfIguHnB1HcgfEL9nkDXYd9itaVfeBT6XQ8XPKmuuoXlHYlVtclZ+1RSNO45y/1dp6xTh1lykxu34in+LWkF/HoPFKTDgBkw5gnwtOChtuyDauUsdT10GalWzlqHWu7xbWhrSi4zF8STnFXCtL7teXxSBK2NPEz01zOw+T01qGLS69DdwkMPklaq1y4/DfrfrN4/nfwQmU0c36d2cA5vhe5jVamTTwfLPb6pGja+rXZY3Txh3AvQ50q7u1DVdZ23Vhzk1T/2M6ZHAG9fGU0zDzvuyPKvw5cPwvn/s/tzNZIsO7DMgnIGvfAXT0yK4MbzT9Pjs7wAfrtPXb2FDoNpH6gTpueqoghWvqDKN5q3UStcUZfZ3RuHw6oqV2/KG96ElkEw+U3rHDI5tZb2opdP7qghzk1ZHix/DHZ8BW26qxrVkEHWeS6z+eRWZWOehAE3O2SrsvIqE/NWHuTdVUn4NHPniYsjmHxeO9tuEdsyia0oVt1O/k7KL37d7ieR2S1Tldp1W/MieLRUZ26s+VmUfVAtMqRtgK6j1QVV607Wea5zZDbrPP3bHj7bkMK06PbMnd4bd1c7PkRbeFSVXOxfplaRJ7995sOXdkSSZQe2JCGD27+O45c7htZ9aObwNph/o2ptNOph1TqnsR+sGTvVAcCjcWqF66JXG1YbJv5xeJtaTc5OVCu+454DLx/rPufRHWpVJjNB1WVOeKn+h5iEsm+xuhAtyVIdR0bMPHNZk6UUHFY/gwf/cPhWZfsyC5m5IIGd6fmM7hHAs1Mjaedr5XrFsvya8ogvVY/dKW9DpyHWfc6/pW1WP3eWLPdoSmp3l+k1TbVhbGmDUh6zGbZ9DH/OViujF8w2vJd9ZbWZB+fvZOGOo9w8rDMPT+hpfDeL0/nX4cvHYNBtDnWhL8myA3v2tz18uSmVhNnjTm7JZDbDhjfUSoZ3O5j+EYQMtNwTm02w7RP462lVXzvsvpptFE/LPUdTUFWmalE3vg3ewWo12ZarTae2xxr3nMPWw9pUSbYqadr9k3EdD5yoVZnJrPPZhhReXp6IiwYzJ/Tg6oGdrPPBf+LA63HjDt79fZBw3Wtql64xBwmbin91l3m1fl2BLO2kXvaD1M9+2+42D6O0sprbvopj9f4sZo7vwa0jutjfwb2/5SaroUspaxt3+NJgkiw7sEveWY+bi8aPt9ZaFSnKVL2Tk1epN+CL37ReXWpRJix7WCUNbbqpVWY5OFY/6VvUanLOAYi+DsY+Y/3V5NPJPqDezNI2qN2CSa/L6N666DokzFe1dpXFqof1+QYnqE7Uqiw9t5RHfk5g7YFs+nVqzdzpUXQLsFBZxEkXOJE1f099LfPYDdWYFnVNSeoGVQbR2O4ylqLrsPM71cu+qgxGzoQhd9vsfSC/tJIbP9vKjvR8nr8kiv8bYKetXc0m1VVrxbPq7+bCp9VnnYNOlpVk2UFVVJuIevJ3bhgaysMTa0ZUH/gDfr5VrTRNmKP+YdriavPgn6pNUl4K9L4cxj5nm60xR1RZCiufg43zamoX34Bu9ehkYm2n1sOOfgwG3upQ22RWdVKtXYwadBDQw+io/uEkrcp0XeenuCM8s3gPpRUm7hjVjdtGNmKYSV0XOPY0pMdUpVZLV82pGX7ygmoTaa+rhLZUUaRGjG/9UPX6t0R3GUsqPg5LHoA9CyGot3pPCO5t1afMLCjn2k82k5JdyptX9GF85DnMVrCl43tVucyRbRA2Xi2k+bQ3OqpGkWTZQcWm5jH93Q28f00/xoW3Vm8qm+apFiyXfmL7D/KqMtWfcv0b4NECLnwK+l7rsFeRVpG6Ua0k5SZBzI1wwVN2M53ohILDqg73wHKVFE5+y2EOYFiFrqtG+b8/Zv+1drVrcR28VVl2cQVPL9rDrzuPEhbYkjnTexMdco5dYQqOqJILe73AqS1rv1o9PdNY7abk4J+q3KHgsLpoH/2YZbvLWNKeX9WFalmuKoUa/qBVzi4kZxVzzcdbKCir4oNr+zGk6xkmgRqlulL1KV/9ojosO+FFiLrUKS7+JFl2UB+uSea5JXuJu60zfstuVQfv+t+stqhsccjodLIS1Qpc6npV0zXptaadbIFaTV7xjNqS8umo+kl2GWl0VKd3amufYffBsPubXk16XooqTzm02rFq7ZyoVdlfe4/x2C9qmMn1Q0J5YGz42fvG6jrEfgZ/PKEucMY87hi7JGYzbP1IHSLTNHWILOamprXgUJoLyx+Fnd9A2zB1wWfJ8zbWUpqrLlR3fG2VuBMOF3D9p1sA+PzGAUS2N6hk70yOxKkLvmO7IHI6jJ/rVDvMkiw7qNu+3Eb79IU8pn+sthSnzDPmwENddB12fKPePCoKYfAdqlOARwujI7O91A01q8nJ6vT0BbMdJ3EpyYHlD0P89+DfQ30AdOxvdFTWZzbBlg/UAVZLDjqwpcoSVSu46V3r9Q+2kaLyKl5aroaZtPNpxvPTzjDMpPZhos7D1da9o9Xf56XConsgeSWEDFa7OwYcIrO5PQth8QNQmqMOjFtphdaqTloRvwVGP97oFfENSdnM+CIWn2bufHnTALr429kKe+2D6i0DVclFj4lGR2Vxkiw7IL28gGUvXs0E8xroNBSmfWif9UAlOWp1Z8dX4BOiTn2HjTM6KtuoLFHJ1ub31XbqlLfVh7cjOvCH+gAoPGL/W6KNlbVftfZK32ydQQe2lr5F1Q5mJzZ8ZLqd2FYzzCSpZpjJY5Mi8Pt7mMmph4nGPmO7MxvW8PeCw/KHVfeMUQ/D4LvA1U6nsTVG0TFV+7v3V5vV/lpVRZF679/yQaNrrZftyuDub3cQ2rY5X9w4kCAfO7t4SFlfM+gqCaKvhQufcdpBV5IsO5ojsVT/cCNafirx3W+j75XP2v/2Ysp6VZqRnagGYIyfa5/JvaWkrFOryXkpaqy4pae5GaH2YRufkJqhCnZwMNFSarfR82gO4+eow6qOmmzVVl3xT6uyZq1V14VeU42OqkEqqk3MW5nEu6sO4u3lzpMXRzA5uADt1zvV9EsnOUx0QtExWHI/7F0EweepRDIoyuioLEPXYee3qqNSVZk6lDrkLodsf1in1I3qwjvnIPS9Wh18P4dE8tstaTz6cwJ9Q1rz8XUx+Da3k0OpUPN5MFuVDfl2Um1Pu4w0OCjrkmTZUZjNsPEt+OtpSj39uTb/Zmbf+V/7rF2qS3WlSkbWvAQubmp1csAM+0/0z0VFcc0byIfQurNaTQ493+ioLCttk1pJcKahCrVbeEVMUclkywCjo7K8zAS1ypyxQ120TnwZvIOMjqpBEjOLeHR+LEMyvuRu91/QmrXCdeJLqlbSGS5wTrX7F7X6Wpb3T4mCI58hOLVf8eS3wD/M6Kgsr6oMVs+F9W/Wuz+0ruu8syqJl5YnMjLcn3ev6mdf46tr7zQOuk19ljeBEktJlh1B0TH45VZIWgE9L2aO+x18vr2AhNljcbPn0ZZ1yU1WdWlJf6mVkkmvQ/too6NqvOTVahUhP12VKox53HnfQP4eqrD+9ZqVypcgYqrjJSnVFerU9vrXoZkfXPQKREw2OirrMlWr2sJVL6hka9zzjjmI5kgc+sI70I7vYbF5CC9wAzePH8A1g6w0zMQelObC8kfUamzbcHUx3nGA0VGdGzuchGcTR7fDwrvgWAL0ukRNTK3j8JvZrPPckr18vO4QU/u046XLzrOf8dWluWoXIP47x/331wiSLNu7g3+q3skVRaoHZ78bmDxvPc09XPluhmO2hULX1RCFZbNUr8oBN6urU6OGcjRGRZHqTbztY/DrorZJbTU612i1VyrDL1LJZis77ft5qvSt6uIma5/D1/I2SPZBtUOQtgG6jFKdPlp3Mjqqs6vjMFF6wEge/WUXa/ZnER3iy9zpveke6CCHaBvi1DMEjnJhnn1Q/cylbXSsf3OWYqr6p62aR0uYMBeiLjtxoVplMjNzfjw/bT/C9UNCeWJShP1c+DnbzkYDSLJsr6orYcXTqmF9QERN7+SelFeZiHxyOTOGd+Gh8XbaM7S+ygvUgZwtH6oPvglzHGuFMnmVWi0oSIdBt9dsRzU3OirbMlXDpnfUoBVXz5rOEXZ8sKqytKZLxDvQqr2qve5+odFRGeNfq3xPqlZz9rrKd4bDRLqu88uOIzy9aA8llhhmYu8cqWbUVK1KCFe+oLpbjHsB+lxpv+8R1nZ8n7poOLwVuo+DSa9S1iyYO76JY8W+49x/YRh3ju5mH+OrizJrDl86Yc38OZJk2R7lJMGCm9TWTcxNqi60ZhrXlkO5/Of9jXx8XQxjejrJaNQjsWqlJDMeul2otvXtud1TeaHq8hH7qRr+MOUdx+gFak05SardVcpa++1JfGiNSrbyUtTP1QWz7W8ojBHy0+G3e9UuVseBNfWj4UZH9Y9zSAyziyt45rc9LNzRiGEmjqT2BYQ9jII+VWaCOg+QsRN6TFK7Tw5aJ29RtdpT6poLH3jewNzsgTw9pTdXD7KD1fam1I2lniRZtjc7v1dTp1zcVE1Qz4tP+va7q5KYu2wfcY9f+E/bJGdgqlZvHiufA3M1jHhI/XDay1javx38S/VxLTqq+kePetQhxwpbxYlpd4+DqQJGPQKD7jD+Dbb2xY1fF5UMOtvBy8bSddVPe9ks1fZwxEw1FtrozgQH/lQXYed4mGjFvmM89vMuMgrLuW5wKA+Oq8cwE0dVVaZq0De8ZT99bp2oA4s1ZaftI/2LGfSt3klO2/60ueJ94xcZ8lLVxXPSiqbV5/ss7CZZ1jRtPPAG4Ap8pOv6nNPd1imT5YoidfAt/jv1D3Tah+Db8V83u/mLbRw8XszKB0baPkZbKDgCy2aqbR//HuoAoD2M7C0vUENW4r5QE5qmvNM0BnQ0RGGG2rrb9xsE91EXfUZt3e3/Xb3xF2WoUplRjza9UplzUXwcljwIe35Rr9mUeWr71dYscJituKKal5bt44uaYSbPXRLJyHAn7HLyt9oT1HpNU6OGjZiglr5VrSY7QW9va0rJLuGaTzaTU1zBwsGH6L5jTs0iw6NqIcbWnaJkguQZ2UWyrGmaK7AfuBA4DGwFrtB1fU9dt3e6ZPnodph/o9oeHv6QKp6vYzVO13X6P/cnI8ICeOU/BnyA2VLiMpVwFaSr7cULnzbuDffAn2p8cFEGDLkbRj7seJOlbE3X1USuvw+FDL1H/du21d9baa5aJY3/Hvx7qqSvQz/bPLcz2LsIFt8PJdkw9G4YMct2r52FDxPFpuYyc0ECB48XM7VPO564uJdz7crVVl0J69+ANXUfIrOq2lMjm/p5gLPYfbSA6z7ZilnX+fT6/pzX0VctMiy+DxKXQLto9Z4VGGGbgLIPqMPa6Zug6xj12vmG2Oa5HYS9JMuDgdm6ro+r+f3DALquv1DX7Z0mWTabYdM8NeyhZYBaTQ4detqbp+aUMOKlVTx3SSRXDbSDuiZrqyxRPSo3vK3q8MY+q1YqbHXwoSwflj+qJhD691CryZJwnZvSXLUiv+NraNNdbelZe6egdrI17H71q4md3LaIsjz12m3/StXmT37buq/dqQM4Jr9tsUluFdUm3lmZxDs1w0yemBTBlD7t7OMQlTUc36dWd49sqzlE9pp1B7Ukr1YLCnIe4Kw2Jedw8+fb8PZy44ubBtItoNbAKl2H3T/BkofUbubwB+D8+6xXjmiqUuU7q+aocsLxL9j2M9aB2EuyfCkwXtf1/9b8/hpgoK7rd9a6zQxgBkBISEi/1NRUq8VjE8XH4Zfb1KGaHpNUEnGWldOftx/mf9/vZOk9w+gZ3ITeiI7tVgcAD2+BTuerN35rN7Dfv1zVShYfg6H3qhpOWU1uuIN/qXKI/DTVV3XMk5b/MD0p2epTc3I70rLP0RQlrVSJUH66avM45gnwtGBrtjonud1tlVr3xMwiZi6IZ0d6PiPD/Xl2aiQdWjtpWY7ZBJvfhxXPgOZa06nmestuq5flwx+Pq/I0v6415wFOv+DT1P2+O5M7v91OiF9zvrxpAME+pznvUpKjdsYSflDdsKa8De0tvFBTexhTz8k1Q4qcpGmAFdhLsnwZMO6UZHmArut31XV7h19ZTloBP92irhzHP6+uxOtxJff4L7v4efsRdj45Fld76b9oK2YzbP9CHdSqLIXz71UrhpY+XFeWB8segZ3fqO37qfMs/ybVVFUUqwOcf2/TTnoNwsY2/nFPTbZGPQKD7zT+YKEzqShWW+yb3wOfDjXjzi9o/OOeOsltyttWP0xkMut8sTGFl5YnAvDQuHCuGRzqvO+puYfUxc6hNZbtVLNviSobKD6mxlSPfFgOO5/BD9vSmbUgnt4dfPn0+v60rk8p0P7l6uejONNyZy5OGirlBxe9rCaXijOyl2S5aZRhVFfCymdVTZl/D9U7ObBXve8+8Y21+LXw4Kv/NuE2ZcVZ8Pujqha1dWfViqjbGMs8duJS9cZUkqVqJUc8JNv31lB7IEjUf2D8HGjRpmGPlZ+udgCS/qo5uf02tO1m2XjFP9I2q9eusePO7WCS2+G8Uh79eRer92fRt2aYSZizDjM50anmMbX1PvpRlXw15BBZSTYsfQh2LYCAXjWrnk4whdWK3l+dxAtL9zGse1veu7rfuXVmKS9Qg68s0c0nfUvN4ctG/vw2QfaSLLuhDviNAY6gDvhdqev67rpu75DJcm4yLPiv6inc7wZ1QvgcrhBLKqqJmr2cO0d1476xdtQD1SjJq+C3+1R/0cjpqtF9Q7eQao/xDOilVpPb9bVouOIU1RWw9lVY+4oqx5jwonod61srZwfJVpPV2JUpO5rkpus6C3cc5alFuymuqOb2kd24fVRXPN1s3InAVgqPqvfN/UvVjtnkt+t/iEzXVYK89CHVjnH4g2pRwd7ae9oRXdd5Yek+PliTzKTewbz6nz4NH5RzaI1qW5p3CGJuhAueqn8pm7V2hpoQu0iWawKZCLyOah33ia7rz53utg6XLMf/CL/9T32QX/xmg/pNbkjK5soPN/PZDf2du/3RuagqVx/Ya18Bt2ZwwRPQ78ZzS5j2LVavTWlOzWGwB+TN35aO71WnsE8cRHpVvZmfSU6SapGVut7wZKtJy4hXSW/GTtUPfuIrZ75gteNJbjk1w0x+2XGU7gFqmEm/Tk46zORfSW89DpEVHlXvk/uXqSR7yjwI6Gm7mB1QtcnMwz8l8GPsYa4Z1InZk3s1vtSnsrSmlO0d8A5WrVXPVsp24sxBmprQecGTlj1z0ETYTbJ8LhwmWa4oVj1Ld36j6vGmf9jgdixvrzjAy7/vZ+cTY/FpbvCgAHuTfRAW/09debePUbWwZztFX5qrPiwSfoTAKLWabEQ/WfHvg0gXzq77oqf2aG03T7U70+cqu0i2mqyTEuBmNa9JHQlwZoK6KMrYYdeT3FbuO86jPyecGGbywLhwWjrrMJOS7JpDZD+e/hCZrkPc5zWDhqpgzOMw8Fbb9wB2MOVVJu76djt/7DnGvRd0554x3S3beeVwrCqnyNp7+lK2snxVsrj9K3X4csrb0GmI5WJoYiRZtpajO1Tv5NxktV01YmajDhzd+NlW0nJL+fO+ERYM0onoOsT/oAYZlOWpaV8jHwbPlv++7Z5f1cGUsjzV+1e2Eu1DXoqqP05eBSFD1Fjjvw97HdujPhyOxkH4RSrZahVsZLSituwDarU/bSN0Ha1WvFp3qnuSW8QUu77AKa6o5uXliXy+MYV2Ps149pJIRjnzbl7iUlWaUZyphmGMfESVCOYmq23/v0fYT35T1cyKMyosr+K/n29ja0ouT03uxbWDQ63zRNWVald17cvg5QsTX4Jel6ifrX2L1WtaklVz+HKWHL5sJEmWLU3X1erXH09CC3+Y9gF0HtbIh9Tp+8wfjIsIYu6lluk76rTK8lQda+xnquPChBeh5yT1vZJstdK/+ycI6g1T3zFuspyom67Djm/URU9VmTpkaTaphMvLBya+qKaT2XGy1WSdWkc+9G7Y/bM6yOmAk9xiU/OYuSCeg8eLmdKnHU9MiqBNSyc98FteUDMS/jOVEPe6BDa+o8adj30Goq+Tn7l6yCqq4LpPtnDgeBGv/KcPk89rZ/0nPba7ZiFhu9q1cfVQn3GBUTDlLTl/YyGSLFtScRYsvB0O/A7hE9XhiYae8q8lKauYMa+sZu70KC7vL1N16iVts6qxO75bvRbhE9Twl/ICtcp//r3qg0DYp6Jjqkxmzy/q91GXwfi5Fvl5ElZWux1cqw4OPcmtotrEu6uSmLfyIC093Xji4gim9mnvvMNMDq1ROwR5KRA2Hi561brDTJxIWk4p13yymeOFFbx/TT+Gh9lw1HjtEjXdXLNjKp9xliTJsqUkr4KfZqg6oXHPqZP5FnpD/XFbOg/Oj+eP/w2nu7O2NrIGU5V6A1k1B6pKVU3y1HfPqV2fMNiBP0BzsVx7QGEbuq5KMgIjnWKS2/5japjJ9rR8RoT589wlTjzMpLIUju9R9cvOelFgYXszCrn2ky1Umcx8en1/+oYYdDg0Px3M1eDX2Zjnd2KSLDeWqUpdza17XdVXXvqJxbf2H/4pgcXxR9nxxFhcnLVxvjXlp6mV5l5T5UpbCNEgJrPOlxtTeLFmmMmzUyOZFn2Wzi3C6W1NyeXGz7bSwsONL28aIAtaTqq+ybKTHgdupLwUmH+TansVfa06kerRwuJPE5eaR9+Q1pIoN5RvSIO7kAghBICri8b1QztzYa8g/vf9DmYtSKB3Bx+6BUhy1FT9tfcYt38dR3vfZnxx0wDn3W0Q9Sad/k+VMB/eG6ZOgV/6qZqqY4VEubC8iv3Hi4g2altHCCHECe19mzHvymiae7oyc0ECZrP97LoK21kQe5gZX8YSHuTNj7cOlkRZAJIs/6OyBH65AxbcpEZW37oWIqdZ7el2puej6zhvg3whhHAw/t6ePH5RBLGpeXy5KdXocISNfbQ2mft/3MmgLn58c/Mg5+2SIs6ZJMugJla9PwJ2fK0mvd2wxOqTw2JT89A0OK+jj1WfRwghRP1Ni27P8DB/Xly2j8N5pUaHI2xA13VeXLaPZxfvZWJUEJ9c3995h9aIBpFkOXMXfDQGKorg2oVqkpENDovFpeUTHuiNt5ccTBNCCHuhaRrPXxKJDjz68y7s6RC8sDyTWeeRnxN4Z1USVw4M4a0rovF0k0mG4mSSLAf2UhPhbtsAXWwzRc9s1tmelke0lGAIIYTd6dC6OQ+OC2f1/ix+2XHE6HCElZRXmbjj6zi+3ZLOXaO78dzUSFzlwL2ogyTLmgbD7rPpUISDWcUUlVfL4T4hhLBT1w4OJTrEl6cX7SG7uMLocISFFZVXccOnW1m2O5MnL47g/rHhzjuURjSaJMsGiEvNAyA6xNfgSIQQQtTF1UVj7vTelFSYeGrRHqPDERaUXVzBFR9uYmtKLq9f3ocbhsqwD3FmkiwbIC4tj9bN3enc1vIt6YQQQlhG90Bv7hzdjUU7j/LnnmNGhyMsID23lMve28jB48V8eG0MU/vK2G9xdpIsGyA2NY/okNay5SOEEHbu1hFdCQ/05rFfdlFYXmV0OKIREjOLuPS9DeQUV/DVTQMZ1SPA6JCEg5Bk2cbySytJyiqRw31CCOEAPNxcmHtpb44XlTN36T6jwxENFJuay3/e34iuww+3DiYm1M/okIQDkWTZxran5wPI4T4hhHAQfTr6cuPQzny9OY1NyTlGhyPO0crE41z10WZaN3dnwW1D6BHUyuiQhIORZNnG4lLzcHXRZBiJEEI4kPvGhhHi15yHf0qgvMpkdDiinhbuOMLNn2+jq39L5t82hI5+Mr5anDtJlm0sLi2PHkHeNPeQ6UBCCOEomnu48cK0KA5ll/D6nweMDkfUw2frD3HPdzuICW3NdzMG0VbGV4sGkmTZhkxmnR1p+fSTemUhhHA4Q7u15fKYjny4NpldRwqMDkechq7rvPp7IrMX7WFcr0A+u2GATMsVjSLJsg0lZhZRUmmSemUhhHBQj0zsiV8LDx6aH0+VyWx0OOIUJrPOY7/s4s0VB7k8piPzrozGy13GV4vGkWTZhuLS/h5GIsmyEEI4Ip/m7jwzpRd7Mgr5cG2y0eGIWiqqTdz97Xa+3pzGbSO7Mmd6FG6ukuaIxpN/RTYUl5ZH25aedPRrZnQoQgghGmh8ZDATIoN4/c8DJGcVGx2OAIorqrnps20sTsjg0Yk9mTm+h8wyEBYjybINxaXmER3iKz/AQgjh4J6a0gsvNxdmLUjAbNaNDqdJK6mo5qqPNrMxOYeXLzuPm4d3MTok4WQkWbaRnOIKUnJKZRiJEEI4gQBvLx6bFMGWlFy+2ZJmdDhNltmsc+/3O9h1pIB3r4rm0n4djA5JOCFJlm1ke5oaRiKdMOInyBQAACAASURBVIQQwjlc1q8D53dry5yl+8goKDM6nCbp5d8T+WPPMR6/qCdjewUZHY5wUpIs20hsWh5uLhpR7WUYiRBCOANN03j+kihMZp1Hf96Frks5hi39sv0I76xK4ooBIVw3JNTocIQTa1SyrGnaZZqm7dY0zaxpWswp33tY07SDmqYlapo2rnFhOr641Dx6tWslLWyEEMKJhLRpzv1jw1ix7zi/7jxqdDhNxva0PB5aEM/Azn48NbmXnAUSVtXYleVdwDRgTe0vapoWAfwf0AsYD7yjaVqTzRKrTGbiDxdIvbIQQjihG4Z25ryOvjy1aA+5JZVGh+P0MgrKmPFlLEGtvHjv6n54uMkmubCuRv0L03V9r67riXV8awrwna7rFbquHwIOAgMa81yObF9GEWVVMoxECCGckauLxovTe1NUXsXTi3YbHY5TK6s0cfMX2yirNPHRdTG0buFhdEiiCbDW5Vh7IL3W7w/XfK1JOjGMRFaWhRDCKYUHeXP7yG78suMoKxOPGx2OUzKbdR74cSe7jxby5hV9CAv0Njok0UScNVnWNO1PTdN21fFrypnuVsfX6jz5oGnaDE3Ttmmati0rK6u+cTuUuLQ8glp50c7Hy+hQhBBCWMnto7rSPaAlj/6UQHFFtdHhOJ03VxxgcUIGD0/owegegUaHI5qQsybLuq5foOt6ZB2/Fp7hboeBjrV+3wGo8+SDrusf6Loeo+t6jL+//7lF7yBiU/OI7iTDSIQQwpl5urkyZ3pvMgrLeXHZPqPDcSqL4zN4/c8DTI/uwM3DZOiIsC1rlWH8CvyfpmmemqZ1BroDW6z0XHbteFE5h/PKpF5ZCCGagH6dWnP9kFC+3JTK1pRco8NxCruOFHD/jzvo16k1z0+LlIUnYXONbR13iaZph4HBwGJN05YD6Lq+G/gB2AMsA+7Qdd3U2GAdUVyqGkYi9cpCCNE0PDA2nHY+zZi5IJ7yqib50WcxxwvLufmLbbRp4cl7V/fD063JNtYSBmpsN4yfdV3voOu6p67rgbquj6v1ved0Xe+q63q4rutLGx+qY4pLy8PD1YVe7VoZHYoQQggbaOHpxgvTokjOKuHtFQeNDsdhlVeZmPFlLPmlVXx4bQz+3p5GhySaKGlOaGVxqXlEtm8lV8NCCNGEDA/zZ3p0B95bncSeo4VGh+NwdF1n1oJ4dqTn89rlfYiQBSdhIEmWraiy2kz8kQL6SQmGEEI0OY9P6olvc3dmLoin2mQ2OhyH8u7qJH7ZcZQHxoYxPjLI6HBEEyfJshXtPlpAZbVZDvcJIUQT5Nvcg6cmR5JwpICP1x0yOhyH8fvuTF5ansjk89pxx6huRocjhCTL1hSXJof7hBCiKZsYFcTYiEBe/WM/KdklRodj9/ZmFHLv9zvo3d6HFy/tLZ0vhF2QZNmK4tLyaO/bjMBWMoxECCGaIk3TeGZqJB5uLsz6KR5dr3M+lwCyiyv47+fb8PZy44NrY/Byl7M+wj5IsmxFcal5sqoshBBNXGArLx6d2JNNybl8tzXd6HDsUkW1idu+iiW7uIIPr42RRSZhVyRZtpKMgjIyCsqJDvE1OhQhhBAGu7x/RwZ3acPzi/eSWVBudDh2Rdd1Hvt5F1tT8nj5svPo3UE+N4V9kWTZSv4eRiKdMIQQQmiaxgvToqg0mXl84S4px6jl43WH+DH2MHeP7sbF57UzOhwh/kWSZSuJTc3Dy92FnsHSG1IIIQSEtm3B/WPD+GPPMZYkZBodjl1YmXic55fsZUJkEPdeEGZ0OELUSZJlK4lLy6N3e1/cXeWvWAghhHLj0M5EtffhyV93kVdSaXQ4hjpwrIi7v9lOj6BWvPKf83Bxkc4Xwj5JJmcF5VUmdh8tkMN9QgghTuLm6sLc6b3JL63i2cV7jQ7HMHkllfz3i214urvy4XUxNPdwMzokIU5LkmUr2HWkgCqTLof7hBBC/EtEu1bcOqIrC+IOs3p/ltHh2FyVycztX8eRkV/O+9f0o71vM6NDEuKMJFm2gri0PECGkQghhKjbnaO70dW/BY/8lEBJRbXR4djUU4t2szE5hznTo+QQvHAIkixbQVxqPp3aNKdtS0+jQxFCCGGHvNxdmTu9N0cLynhpeaLR4djMFxtT+GpTGreO6Mq06A5GhyNEvUiybGG6rhOblkd0iFwtCyGEOL2YUD+uHdSJzzemEJuaZ3Q4VrfuQDZPLdrDBT0DeHBcuNHhCFFvkixb2OG8MrKKKqReWQghxFk9OL4Hwa28mLUgnopqk9HhWM2h7BJu/zqWbv4tef3/+uIqnS+EA5Fk2cKkXlkIIUR9tfR047lpURw4Xsy8lUlGh2MVBWVV3PT5VlxdND66LoaWntL5QjgWSZYtLC41j+YeroQHehsdihBCCAcwKjyAS/q2591VB9mX+f/t3Xl8XHW9//HXu/veQhfoTguF2pbSDRBFBUGvIFAQFRAFVMAF5eIVleVy4Ypc+Skq4obF5QIqilKuiAgFLlwUF2wHukGhtZC0tEBKk+5Nm+Tz+2NO6LQmTZOZ5Mxk3s/HYx6ZOefMOZ9vTmbOJ9/zXTamHU5B1dU38Nm7nmbV+q3c+uGZjN6/T9ohmbWak+UCy1TWcMSoQXTzZCRmZraPrjllEv17dedL9yymvqHzTIV9wwPP8cQLVXzl9CkcPX5w2uGYtYkzugLauqOOZ9du9FA4ZmbWKvv37cF1p01m4aoafvrki2mHUxB3PVXJT598iY+9dRxnHTkm7XDM2szJcgEtWr2B+oZgxlh37jMzs9Y5depwTpg4jJvmPU/l61vTDicvf135Otf8zxLefuhQrjp5YtrhmOXFyXIBNXbumz7aNctmZtY6kvjKGVPo1qULV967iIjSbI6xav1WPvWzBYwZ3IfvnDPdzRKt5PkvuIAyFTWMH9qX/fr2SDsUMzMrQcMH9uaKkyby5IrX+fX81WmH02qbtmdHvmgI+PH5RzKwd/e0QzLLm5PlAokIMp6MxMzM8vSho8Zw1Lj9+crvn+W1jdvTDmef1TcEl/3yGf5RtYXvfWgG44b0TTsks4JwslwgFa9vZf2WHU6WzcwsL126iBvfdzjb6xq49r6laYezz7720DIeXfYa1506iWMnDEk7HLOCcbJcII3tlT0ShpmZ5Wv80H587sRD+cOSV3hwydq0w2nRPQtW88P/W8mH3zyGjxxzUNrhmBWUk+UCWVBRTf+e3ZgwrF/aoZiZWSdw0dvGMXnEAK757VI2bN2ZdjjNWlBRzZVzF3PM+MFce+rktMMxK7i8kmVJX5e0TNIiSfdKGpSz7kpJKyQ9L+lf8g+1uGUqa5g2ZhBdPN+9mZkVQLeuXfh/Z05l/ZYd3PDAs2mH06SXa7bxiTvnM3xQL75/7gy6e+QL64Ty/at+GJgSEVOBF4ArASRNAs4GJgPvAb4vqWuexypam2vreP6VjW6vbGZmBTVl5EAufvt47p6/midXrEs7nN1sqa3jwtvnU7uzgR+fP8sjQVmnlVeyHBHzIqIueflXYFTyfDbwy4iojYgXgRXAUfkcq5gtXFVDQ8AMt1c2M7MC+9cTJjBuSF+umLuIrTvqWn5DB2hoCD5/90Kef2Ujt3xoOocM6592SGbtppD3Sz4G/CF5PhJYlbNudbKsU8pUZDv3TRvtmfvMzKywenXvyo3vO5xV67fxzXkvpB0OADc/8gIPLn2Fq05+E8cfNiztcMzaVYvJsqRHJC1p4jE7Z5urgTrg542LmthVk1MRSbpY0nxJ86uqqtpShtRlKqs59IB+HnzdzMzaxdHjB3Pu0WP4yZMv8syqmlRj+d3CNdzyvyv44KxRfPzYcanGYtYRWkyWI+LEiJjSxOO3AJLOB04Bzo1dc3OuBkbn7GYUsKaZ/c+JiFkRMWvo0KH5lSYFDQ1BprLG7ZXNzKxdXXHSRA4Y0Isv/WYRO+oaUolh4aoaLv/1Qo48aD+uP30Kkju1W+eX72gY7wG+BJwWEVtzVt0HnC2pp6RxwATgqXyOVaxWrtvChm07nSybmVm76t+rO185fQrPv7qJHzz+jw4//isbtnPRHfMZ0q8nP/jwTHp267T99s12k2+b5e8C/YGHJT0j6VaAiFgK3A08CzwIXBIR9Xkeqyg1Tkbizn1mZtbeTnjTAZx2xAi++9hylr+6qcOOu31nPRffOZ8ttXX8+IJZDOnXs8OObZa2fEfDOCQiRkfEtOTxyZx1N0TEwRFxWET8YW/7KWWZimoG9u7O+CF90w7FzMzKwLWnTqJfz2586Z5F1Dc02R2ooCKCL/xmEYtf3sDNZ09n4oED2v2YZsXEo4fnKVNZzXRPRmJmZh1kcL+eXHvqZDKVNdzxl5fa/Xjfe2wFv1u4hi/8y2G8a9IB7X48s2LjZDkPG7btZPlrm5np9spmZtaBZk8bwXGHDeXrDz3PqvVbW35DGz24ZC03zXuB06eN4FPvOLjdjmNWzJws5+GZVTWEJyMxM7MOJokbzjgcAVfdu5hdg1EVztI1G/jcrxYybfQgbjxzqke+sLLlZDkPmYpqugiO8GQkZmbWwUYO6s2XTprIH5evY27m5YLuu2pTLRfdPp9Bfboz57yZ9OrukS+sfDlZzkOmsprDDhxAv57d0g7FzMzK0IePHsussfvx5fufpWpTbUH2WVtXzyfunM/6rTu47bxZDOvfqyD7NStVTpbbqKEheKayhhljXKtsZmbp6NJF3HjmVLbtqOe63y3Ne38RwZVzF5OprOEbH5jGlJEDCxClWWlzstxGy1/bzKbaOk9GYmZmqTpkWD8uPeEQfr9oLfOWvpLXvuY8sZK5mZe57MQJvHfq8AJFaFbanCy3UeNkJDPduc/MzFL2iXcczMQD+3PNb5ewcfvONu3j0ede5cYHl/Hew4dz6TsnFDhCs9LlZLmNFlRUs3/fHowd3CftUMzMrMx179qFr71/KlWbavnqA8ta/f7nX9nEpXc9zeQRA7jpA0d47gCzHE6W2yhTWc2MMYM8lI6ZmRWFqaMGcdHbxnPXU5X85R+v7/P71m/ZwYV3/J0+Pbtx23mz6N3DI1+Y5XKy3AbVW3awsmqLx1c2M7OictmJhzJ2cB+unLuIbTvqW9x+R10Dn/rZAl7dWMucj8xk+MDeHRClWWlxstwGT6/Ktld25z4zMysmvXt05avvO5yXXt/KzY+8sNdtI4Jr71vC315cz9fOnMp0X9PMmuRkuQ0yFTV07SKmjvKQOmZmVlzecvAQzjlqNLf9cSWLVtc0u91///kl7npqFZ8+7mBOnz6yAyM0Ky1OltsgU1nNpOED6NPDk5GYmVnxueKkNzG0f0+++JtF7Kxv+Kf1T7xQxfX3P8u7Jh3A5e8+LIUIzUqHk+VWqqtv4JlVnozEzMyK18De3bl+9hSWvbKJOU+s3G3dP6o2c8kvMhx6QH9uPmuaR74wa4GT5VZ6/tVNbN1R7859ZmZW1N49+UDee/hwvv3Icla8thmAmq07uPD2+fTo2oUfnT+Lvj19h9SsJU6WWylTmW3/5c59ZmZW7K47bTK9e3TlyrmL2FHXwCW/yLC6eiu3fmQmo/bzPAFm+8LJcitlKqoZ2r8no/bz8DpmZlbchvbvyTWnTOLvL1Uz+3tP8uSK17nh9MM58qD90w7NrGQ4WW4lT0ZiZmal5MwZI3nbhCE8t3YjFx47jg8eOTrtkMxKihsrtcK6zbVUvL6Vc48ek3YoZmZm+0QS3zprGo88+yofmOVE2ay1nCy3QqbCk5GYmVnpGdKvJ2cf5Yoes7ZwM4xWyFTW0L2rmDLSk5GYmZmZlQMny62Qqaxm8oiB9OreNe1QzMzMzKwDOFneRzvrG1i0usZNMMzMzMzKiJPlffTc2o1s39nAjLGeuc/MzMysXDhZ3keNnftmeuY+MzMzs7LhZHkfLaisYfjAXgwf6MlIzMzMzMpFXsmypOslLZL0jKR5kkYkyyXpFkkrkvUzChNuejIV1W6vbGZmZlZm8q1Z/npETI2IacD9wH8ky08CJiSPi4Ef5HmcVL26cTsv12xjhptgmJmZmZWVvJLliNiY87IvEMnz2cAdkfVXYJCk4fkcK027JiNx5z4zMzOzcpL3DH6SbgDOAzYAxyeLRwKrcjZbnSxbm+/x0pCprKZHty5MHuHJSMzMzMzKSYs1y5IekbSkicdsgIi4OiJGAz8HPtP4tiZ2FU0sQ9LFkuZLml9VVdXWcrSrTGUNU0cOpEc394c0MzMzKyct1ixHxIn7uK9fAL8HriVbkzw6Z90oYE0z+58DzAGYNWtWkwl1mmrr6lm8egMXvPWgtEMxMzMzsw6W72gYE3JengYsS57fB5yXjIrxZmBDRJRkE4ylazayo77B7ZXNzMzMylC+bZZvlHQY0ABUAJ9Mlj8AnAysALYCH83zOKnZ1bnPI2GYmZmZlZu8kuWIOLOZ5QFcks++i0WmsppR+/Vm2IBeaYdiZmZmZh3MPdZakKmoca2ymZmZWZlysrwXa2q28crG7cz0ZCRmZmZmZcnJ8l4scHtlMzMzs7LmZHkvMpXV9OrehYnD+6cdipmZmZmlwMnyXmQqazhi1CC6d/WvyczMzKwcOQtsxvad9Ty7ZgMz3F7ZzMzMrGw5WW7G4pc3sLM+3F7ZzMzMrIw5WW7GrslIPHOfmZmZWblystyMTGU1Bw3uw+B+PdMOxczMzMxS4mS5CRHBAk9GYmZmZlb2nCw3YXX1NtZtrnXnPjMzM7My52S5CZlKT0ZiZmZmZk6Wm7Sgopq+Pbpy2IGejMTMzMysnDlZbkKmspppYwbRtYvSDsXMzMzMUuRkeQ9bd9Tx3NpNboJhZmZmZk6W97Rw1QbqGzwZiZmZmZk5Wf4njZ37pnsyEjMzM7Oy52R5D09XVnPw0L4M6tMj7VDMzMzMLGVOlnNEBJlKT0ZiZmZmZllOlnO89PpW1m/ZwUxPRmJmZmZmOFneTaYimYzEybKZmZmZ4WR5Nwsqq+nfqxuHDO2XdihmZmZmVgScLOfIVFQzfcx+dPFkJGZmZmaGk+U3bNq+kxde3cQMDxlnZmZmZgkny4mFqzbQEHgkDDMzMzN7g5PlRKayGgmmuWbZzMzMzBJOlhOZymoOHdafAb26px2KmZmZmRUJJ8tAQ0OQqahmxljXKpuZmZnZLgVJliVdLikkDUleS9ItklZIWiRpRiGO015WrtvMxu11bq9sZmZmZrvJO1mWNBp4F1CZs/gkYELyuBj4Qb7HaU+ZihrAk5GYmZmZ2e4KUbP8LeCLQOQsmw3cEVl/BQZJGl6AY7WLBRXVDOrTnfFD+qYdipmZmZkVkbySZUmnAS9HxMI9Vo0EVuW8Xp0sa2ofF0uaL2l+VVVVPuG0Waaymhlj9kPyZCRmZmZmtku3ljaQ9AhwYBOrrgauAt7d1NuaWBZNLCMi5gBzAGbNmtXkNu1pw7adLH9tM7OnjejoQ5uZmZlZkWsxWY6IE5taLulwYBywMKmRHQVkJB1FtiZ5dM7mo4A1eUfbDmp31vORN4/lbROGph2KmZmZmRWZFpPl5kTEYmBY42tJLwGzImKdpPuAz0j6JXA0sCEi1uYbbHsYNqAX158+Je0wzMzMzKwItTlZbsEDwMnACmAr8NF2Oo6ZmZmZWbspWLIcEQflPA/gkkLt28zMzMwsDZ7Bz8zMzMysGU6WzczMzMya4WTZzMzMzKwZTpbNzMzMzJrhZNnMzMzMrBlOls3MzMzMmuFk2czMzMysGU6WzczMzMyaoez8IcVBUhVQkdLhxwCVKR27vQ0ENqQdRDvpzGWDzl0+l610debyuWylqzOXz2VrH2MjYmhLGxVVspwmSVX78gsrRZLmRMTFacfRHjpz2aBzl89lK12duXwuW+nqzOVz2dLlZhi71KQdQDv6XdoBtKPOXDbo3OVz2UpXZy6fy1a6OnP5XLYUuWY5IWl+RMxKOw4zMzMzKx6uWd5lTtoBmJmZmVlxcc2ymZmZmVkzXLPcCUiql/RMzuOgvWx7nKT7Oy66/EgKSXfmvO4mqaqUytASSWck5ZyYdiyFUg7nDUDS5rRjaG8tlVHS45JKpglbZ/y85ZJ0taSlkhYl14Oj046pkCSNkvRbScsl/UPStyX12Mv2l0nq05Extlby9/iNnNeXS7ouxZAKKidHWSppoaR/k1RS+WdJBWvN2hYR03IeL6UdUAFtAaZI6p28fhfwcmt2IKlbwaMqrHOAPwFnt+ZNkrq2TzgFkfd5M2snbfq8lQJJxwCnADMiYipwIrAq3agKR5KAucD/RMQE4FCgH3DDXt52GVDUyTJQC7xP0pC0A2knjTnKZLLXgpOBa1OOqVXKKlkuh1qgRpK6Svq6pL8nNQyfyFk9QNK9kp6VdGsJ/If3B+C9yfNzgLsaV0g6StKfJT2d/DwsWX6BpF9L+h0wr+ND3jeS+gFvBT5OcvFOav+faOocSdos6cuS/gYck17k+6Qt5+2PkqblbPekpKkdGnUr7Xm3RtJ3JV2QPH9J0n9KykhaXKq1mXsrYynZy+etufN3sqRlkv4k6ZYSuDMyHFgXEbUAEbEuItZIminp/yQtkPSQpOHwxl2Bm5PP4BJJR6UafcveCWyPiJ8CREQ98DngY5L6Srop+ZwtkvRZSZcCI4DHJD2WYtwtqSPbb+pze66QNFbSo0mZHpU0RtLA5Lul8brQR9IqSd07OvDWiojXgIuBzyir2VxF0heT87lQ0o3pRV1myXIn1lu7mmDcmyz7OLAhIo4EjgQukjQuWXcU8HngcOBg4H0dHnHr/BI4W1IvYCrwt5x1y4C3R8R04D+A/8pZdwxwfkS8s8Mibb3TgQcj4gVgvaQZyfLmzlFfYElEHB0Rf+rwaFunLeftR8AFAJIOBXpGxKIOi7h9rIuIGcAPgMvTDqbMNfd5+yfJ3+0PgZMi4ligFMbhnweMlvSCpO9LekeSQH0HeH9EzAR+wu41sX0j4i3Ap5N1xWwysCB3QURsJDuh2IXAOGB6Uqv+84i4BVgDHB8Rx3d0sK30PeBcSQP3WP5d4I7GMgG3RMQGYCHwjmSbU4GHImJnh0Wbh4hYSTb/HEYzuYqkk8h+Xo+OiCOAr6UWMGWYLEvql/x31ljTMztZfpCk5yTdpmy7mnnadQu52OU2wzgjWfZu4DxJz5BNUgYDE5J1T0XEyuS/8ruAYzs+5H2XJEsHka2dfGCP1QOBX0taAnyL7Jdpo4cjYn2HBNl255BNKkl+npM8b+4c1QP3dGyIbdPG8/Zr4JTkAv8x4L87JNj2NTf5uYDs78PS09znrSkTgZUR8WLy+q69bFsUImIzMJNszV0V8CvgE8AU4OHkevDvwKict92VvPcJsncdB3Vo0K0joKlRCQS8Hbg1IuoASuC7fzdJ0n8HcOkeq44BfpE8v5Nd14JfAWclz89OXpcSJT+by1VOBH4aEVsh/fNZ7G0528N24IyI2Khs+6C/SrovWTcBOCciLpJ0N3Am8LO0As2TgM9GxEO7LZSO45+/bEphSJT7gJuA48h+mBpdDzwWEWco27Hx8Zx1WzootjaRNJjsbcUpkgLoSvZcPEDz52h7kkCXiladt4jYKulhYDbwQaAUOo7VsXvFQ6891tcmP+sp3e/clspY9PbyebuPpssmSlDy/fA48LikxcAlwNKIaK7ZVildD5aSvS6/QdIAYDSwkuKOfV/cDGSAn+5lm8Yy3gd8VdL+ZP9B+t92jq1gJI0n+334Gs3nKu+hiM5n2dUskz0x/yVpEfAIMBI4IFn3YkQ8kzwv9Vqgh4BPNbZhknSopL7JuqOS2xxdyP5nWuy38yF7e/DLEbF4j+UD2dVx7IIOjSh/7yd7e21sRBwUEaOBF8nWHJTiOWpKW87bj4BbgL+nXZuwjyqASZJ6JrdQT0g7oHbQGcrY3OcNmi7bMmC8do0udBZFTtJhkibkLJoGPAcMVbbzH5K6S8q9A3dWsvxYsrfDN3RYwK33KNBH0nnwRifnb5C9AzUP+KSSDt1JEgmwCejf8aG2XvJ9dzfZpgmN/syuzqjnklwLkrsITwHfBu4vlUoUSUOBW4HvRnbs4uZylXlk26L3SZbv39w+O0Kp1nLk41yybc9mRsROSS+xqyahNme7eqBUmmE05Udkk/2MJJG9JXd6su4vwI1k28M+Adzb1A6KSUSsJvulsKevAbdL+jdK6D/rxDlkz0Oue4BPUYLnqCltOW8RsUDSRvZeu5K65KJcGxGrkjtRi4DlwNPpRlY4nayMzX3ePkQ2QdmtbBGxTdKngQclrSObmBS7fsB3kqYUdcAKsk0y5gC3JP8MdCNbg7k0eU+1pD8DA8g2fSpaERGSzgC+L+kashV+DwBXkb1mHwoskrQTuI1se985wB8krS2BdsuQTf4/k/P6UuAnkr5A9jr+0Zx1vyLbdO24DouubXonzSy6k/27vBP4ZrKuyVwlIh5UtrP3fEk72HWeU1FWk5IoOxrG1cAhEfFZSceTvVA3dny7PyKmJNteDvSLiOtSCdbKVtJU5vKIOCXtWNIgaQTZ28gTI6Ih5XCaJekI4LaIKPYRBNqsHMq4N5L6RcTm5CL+PWB5RHwr7bgKRdLjZL9r5qcdi1kxK5tmGI01JGR7k86SNJ9sLfOyVAMzszckt1f/Blxd5InyJ8l2jPr3tGNpL+VQxn1wUVIjtpRs06EfphyPmaWgbGqWy72GxMzMzMxaryxqll1DYmZmZmZtUTY1y2ZmZmZmrdVpa5YljZb0WDLRyFJJ/5os31/Sw5KWJz/3S5ZPlPQXSbVJ577cfX0u2ccSSXcpO7OTmZmZmXVynTZZJjs8yecj4k3Am4FLJE0CrgAejYgJZMdsvCLZfj3ZIVpuyt2JpJHJ8lnJSBld2TXmoZmZmZl1Yp02WY6ItRGRSZ5vIjsw+0iyM4Pdnmx2O8nYwxHxWkT8HWhqbvVuZMcJ7Ab0ITvXvJmZmZl1ZWfEfQAAAURJREFUcp02Wc6VzMA0neyQVAdExFrIJtTAsL29NyJeJlvbXAmsJTvD0bz2jNfMzMzMikOnT5Yl9SM7S9NlEbGxDe/fj2xt9DhgBNBX0ocLG6WZmZmZFaNOnSwnc43fA/w8IuYmi1+VNDxZPxx4rYXdnAi8GBFVEbETmAu8pb1iNjMzM7Pi0WmT5WR60h8Dz0XEN3NW3Qecnzw/H/htC7uqBN4sqU+yzxPItn82MzMzs06u046zLOlY4I/AYqBx2tyryLZbvhsYQzYR/kBErJd0IDAfGJBsvxmYFBEbJf0ncBbZETaeBi6MiNqOLI+ZmZmZdbxOmyybmZmZmeWr0zbDMDMzMzPLl5NlMzMzM7NmOFk2MzMzM2uGk2UzMzMzs2Y4WTYzMzMza4aTZTMzMzOzZjhZNjMzMzNrhpNlMzMzM7Nm/H+hODVw/+RI5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize=(12,5),legend=True);\n",
    "first_predictions.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(2,1,3) RMSE Error: 18.00535155\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "error = rmse(test, first_predictions)\n",
    "print(f'ARIMA(2,1,3) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5833333333333335"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# StepWise Fiting of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 1) RMSE=19.545\n",
      "ARIMA(0, 0, 2) RMSE=19.433\n",
      "ARIMA(0, 1, 2) RMSE=19.314\n",
      "ARIMA(0, 1, 3) RMSE=19.220\n",
      "ARIMA(1, 0, 4) RMSE=18.698\n",
      "ARIMA(1, 1, 3) RMSE=17.576\n",
      "ARIMA(2, 1, 2) RMSE=17.238\n",
      "ARIMA(4, 0, 2) RMSE=16.658\n",
      "ARIMA(4, 0, 4) RMSE=15.799\n",
      "Best ARIMA(4, 0, 4) RMSE=15.799\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "import numpy\n",
    "\n",
    " \n",
    "# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\n",
    "def evaluate_arima_model(X, arima_order):\n",
    "    history = [x for x in train]\n",
    "    # make predictions\n",
    "    predictions = list()\n",
    "    for t in range(len(test)):\n",
    "        # difference data\n",
    "        model = ARIMA(history, order=arima_order)\n",
    "        model_fit = model.fit(trend='nc', disp=0)\n",
    "        yhat = model_fit.forecast()[0]\n",
    "        predictions.append(yhat)\n",
    "        history.append(test[t])\n",
    "    # calculate out of sample error\n",
    "    mse = mean_squared_error(test, predictions)\n",
    "    rmse = sqrt(mse)\n",
    "    return rmse\n",
    " \n",
    "# evaluate combinations of p, d and q values for an ARIMA model\n",
    "def evaluate_models(dataset, p_values, d_values, q_values):\n",
    "    dataset = dataset.astype('float32')\n",
    "    best_score, best_cfg = float(\"inf\"), None\n",
    "    for p in p_values:\n",
    "        for d in d_values:\n",
    "            for q in q_values:\n",
    "                order = (p,d,q)\n",
    "                try:\n",
    "                    mse = evaluate_arima_model(dataset, order)\n",
    "                    if mse < best_score:\n",
    "                        best_score, best_cfg = mse, order\n",
    "                        print('ARIMA%s RMSE=%.3f' % (order,mse))\n",
    "                except:\n",
    "                    continue\n",
    "    print('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))\n",
    " \n",
    "# load dataset\n",
    "#series = read_csv('dataset.csv', header=None, index_col=0, parse_dates=True, squeeze=True)\n",
    "# evaluate parameters\n",
    "p_values = range(0, 5)\n",
    "d_values = range(0, 3)\n",
    "q_values = range(0, 5)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "evaluate_models(train, p_values, d_values, q_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARIMA(train, order=(4,0,4))\n",
    "model_fit =  model.fit(trend='nc', disp=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "sec_predictions = model_fit.predict(start=start, end=end, dynamic=False, typ='levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01    -9.516999\n",
       "2018-02-01     8.759876\n",
       "2018-03-01     9.124954\n",
       "2018-04-01    11.208395\n",
       "2018-05-01    21.641787\n",
       "2018-06-01    10.712034\n",
       "2018-07-01     1.265752\n",
       "2018-08-01     2.643458\n",
       "2018-09-01   -12.261231\n",
       "2018-10-01   -19.348445\n",
       "2018-11-01   -10.283413\n",
       "2018-12-01   -14.041566\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize=(12,5),legend=True);\n",
    "sec_predictions.plot(legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(4,0,4) RMSE Error: 15.23944333\n"
     ]
    }
   ],
   "source": [
    "error = rmse(test, sec_predictions)\n",
    "print(f'ARIMA(4,0,4) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SARIMAX MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                   <td>y</td>                <th>  No. Observations:  </th>    <td>348</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(1, 0, [1], 12)</td> <th>  Log Likelihood     </th> <td>-1098.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Fri, 21 Feb 2020</td>         <th>  AIC                </th> <td>2210.612</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>00:35:12</td>             <th>  BIC                </th> <td>2237.558</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                          <td>0</td>                <th>  HQIC               </th> <td>2221.341</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 348</td>              <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.0004</td> <td>    0.005</td> <td>    0.081</td> <td> 0.935</td> <td>   -0.010</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    0.9225</td> <td>    0.046</td> <td>   20.245</td> <td> 0.000</td> <td>    0.833</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -0.9439</td> <td>    0.070</td> <td>  -13.421</td> <td> 0.000</td> <td>   -1.082</td> <td>   -0.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    0.1317</td> <td>    0.060</td> <td>    2.192</td> <td> 0.028</td> <td>    0.014</td> <td>    0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th>  <td>    0.9977</td> <td>    0.001</td> <td>  717.676</td> <td> 0.000</td> <td>    0.995</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>   -0.7569</td> <td>    0.042</td> <td>  -17.884</td> <td> 0.000</td> <td>   -0.840</td> <td>   -0.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>   29.1930</td> <td>    1.701</td> <td>   17.161</td> <td> 0.000</td> <td>   25.859</td> <td>   32.527</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>32.10</td> <th>  Jarque-Bera (JB):  </th> <td>66.50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.81</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.87</td>  <th>  Skew:              </th> <td>-0.09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.45</td>  <th>  Kurtosis:          </th> <td>5.14</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                                    y   No. Observations:                  348\n",
       "Model:             SARIMAX(1, 1, 2)x(1, 0, [1], 12)   Log Likelihood               -1098.306\n",
       "Date:                              Fri, 21 Feb 2020   AIC                           2210.612\n",
       "Time:                                      00:35:12   BIC                           2237.558\n",
       "Sample:                                           0   HQIC                          2221.341\n",
       "                                              - 348                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0004      0.005      0.081      0.935      -0.010       0.011\n",
       "ar.L1          0.9225      0.046     20.245      0.000       0.833       1.012\n",
       "ma.L1         -0.9439      0.070    -13.421      0.000      -1.082      -0.806\n",
       "ma.L2          0.1317      0.060      2.192      0.028       0.014       0.249\n",
       "ar.S.L12       0.9977      0.001    717.676      0.000       0.995       1.000\n",
       "ma.S.L12      -0.7569      0.042    -17.884      0.000      -0.840      -0.674\n",
       "sigma2        29.1930      1.701     17.161      0.000      25.859      32.527\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       32.10   Jarque-Bera (JB):                66.50\n",
       "Prob(Q):                              0.81   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.87   Skew:                            -0.09\n",
       "Prob(H) (two-sided):                  0.45   Kurtosis:                         5.14\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df['Employees'],seasonal=True,m=12).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "model = SARIMAX(train ,order=(1,1,2),seasonal_order=(1, 0, 1, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                <td>Emp_1d</td>              <th>  No. Observations:  </th>    <td>335</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 2)x(1, 0, [1], 12)</td> <th>  Log Likelihood     </th> <td>-1071.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Fri, 21 Feb 2020</td>         <th>  AIC                </th> <td>2154.004</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>00:37:55</td>             <th>  BIC                </th> <td>2176.871</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>02-01-1990</td>            <th>  HQIC               </th> <td>2163.122</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 12-01-2017</td>           <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>   -0.9662</td> <td>    0.142</td> <td>   -6.793</td> <td> 0.000</td> <td>   -1.245</td> <td>   -0.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.0326</td> <td>    0.073</td> <td>   -0.448</td> <td> 0.654</td> <td>   -0.175</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>   -0.9666</td> <td>    0.210</td> <td>   -4.609</td> <td> 0.000</td> <td>   -1.378</td> <td>   -0.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>    0.9944</td> <td>    0.003</td> <td>  386.059</td> <td> 0.000</td> <td>    0.989</td> <td>    0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.7023</td> <td>    0.054</td> <td>  -12.966</td> <td> 0.000</td> <td>   -0.808</td> <td>   -0.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>   32.2360</td> <td>    2.793</td> <td>   11.541</td> <td> 0.000</td> <td>   26.761</td> <td>   37.711</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>95.72</td> <th>  Jarque-Bera (JB):  </th> <td>49.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.16</td>  <th>  Skew:              </th> <td>-0.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.44</td>  <th>  Kurtosis:          </th> <td>4.69</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                               Emp_1d   No. Observations:                  335\n",
       "Model:             SARIMAX(1, 1, 2)x(1, 0, [1], 12)   Log Likelihood               -1071.002\n",
       "Date:                              Fri, 21 Feb 2020   AIC                           2154.004\n",
       "Time:                                      00:37:55   BIC                           2176.871\n",
       "Sample:                                  02-01-1990   HQIC                          2163.122\n",
       "                                       - 12-01-2017                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.9662      0.142     -6.793      0.000      -1.245      -0.687\n",
       "ma.L1         -0.0326      0.073     -0.448      0.654      -0.175       0.110\n",
       "ma.L2         -0.9666      0.210     -4.609      0.000      -1.378      -0.556\n",
       "ar.S.L12       0.9944      0.003    386.059      0.000       0.989       0.999\n",
       "ma.S.L12      -0.7023      0.054    -12.966      0.000      -0.808      -0.596\n",
       "sigma2        32.2360      2.793     11.541      0.000      26.761      37.711\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       95.72   Jarque-Bera (JB):                49.13\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.16   Skew:                            -0.42\n",
       "Prob(H) (two-sided):                  0.44   Kurtosis:                         4.69\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = len(train)\n",
    "end   = len(train)+len(test)-1\n",
    "third_predictions = results.predict(start,end,typ ='level')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01   -33.810982\n",
       "2018-02-01    21.299246\n",
       "2018-03-01    19.486786\n",
       "2018-04-01    24.797410\n",
       "2018-05-01    20.739604\n",
       "2018-06-01    17.497465\n",
       "2018-07-01     7.972724\n",
       "2018-08-01     2.473089\n",
       "2018-09-01   -17.993886\n",
       "2018-10-01    -3.319375\n",
       "2018-11-01    -7.461183\n",
       "2018-12-01     3.944933\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize=(12,5),legend=True)\n",
    "third_predictions.plot(figsize=(12,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARIMA(2,0,2) RMSE Error: 6.229925182\n"
     ]
    }
   ],
   "source": [
    "error = rmse(test, third_predictions)\n",
    "print(f'SARIMA(2,0,2) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5833333333333335"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>third_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-33.810982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>21.299246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>19.486786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>24.797410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>20.739604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>17.497465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>7.972724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2.473089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>-17.993886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>-3.319375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>-7.461183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.944933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            third_pred\n",
       "2018-01-01  -33.810982\n",
       "2018-02-01   21.299246\n",
       "2018-03-01   19.486786\n",
       "2018-04-01   24.797410\n",
       "2018-05-01   20.739604\n",
       "2018-06-01   17.497465\n",
       "2018-07-01    7.972724\n",
       "2018-08-01    2.473089\n",
       "2018-09-01  -17.993886\n",
       "2018-10-01   -3.319375\n",
       "2018-11-01   -7.461183\n",
       "2018-12-01    3.944933"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.date_range('01/01/2018', periods=12, freq='MS')\n",
    "df_forecast = pd.DataFrame(third_predictions, index=idx, columns=['third_pred'])\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_difference(history, yhat, interval=-13):\n",
    "    yhat['Employees_org']= history['Employees'].iloc[interval] + yhat['third_pred'].cumsum()\n",
    "    return yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_org = inverse_difference(df,df_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>third_pred</th>\n",
       "      <th>Employees_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-33.810982</td>\n",
       "      <td>1923.389018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>21.299246</td>\n",
       "      <td>1944.688263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>19.486786</td>\n",
       "      <td>1964.175049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>24.797410</td>\n",
       "      <td>1988.972459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>20.739604</td>\n",
       "      <td>2009.712063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>17.497465</td>\n",
       "      <td>2027.209528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>7.972724</td>\n",
       "      <td>2035.182252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2.473089</td>\n",
       "      <td>2037.655341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>-17.993886</td>\n",
       "      <td>2019.661455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>-3.319375</td>\n",
       "      <td>2016.342080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>-7.461183</td>\n",
       "      <td>2008.880897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.944933</td>\n",
       "      <td>2012.825831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            third_pred  Employees_org\n",
       "2018-01-01  -33.810982    1923.389018\n",
       "2018-02-01   21.299246    1944.688263\n",
       "2018-03-01   19.486786    1964.175049\n",
       "2018-04-01   24.797410    1988.972459\n",
       "2018-05-01   20.739604    2009.712063\n",
       "2018-06-01   17.497465    2027.209528\n",
       "2018-07-01    7.972724    2035.182252\n",
       "2018-08-01    2.473089    2037.655341\n",
       "2018-09-01  -17.993886    2019.661455\n",
       "2018-10-01   -3.319375    2016.342080\n",
       "2018-11-01   -7.461183    2008.880897\n",
       "2018-12-01    3.944933    2012.825831"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2198f79e198>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[-12:].plot(figsize=(12,5))\n",
    "df_forecast['Employees_org'].plot(figsize=(12,5),legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_forecast['test_org'] = df[-13:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>third_pred</th>\n",
       "      <th>Employees_org</th>\n",
       "      <th>test_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>-33.810982</td>\n",
       "      <td>1923.389018</td>\n",
       "      <td>1916.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>21.299246</td>\n",
       "      <td>1944.688263</td>\n",
       "      <td>1944.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>19.486786</td>\n",
       "      <td>1964.175049</td>\n",
       "      <td>1962.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>24.797410</td>\n",
       "      <td>1988.972459</td>\n",
       "      <td>1984.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>20.739604</td>\n",
       "      <td>2009.712063</td>\n",
       "      <td>2001.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>17.497465</td>\n",
       "      <td>2027.209528</td>\n",
       "      <td>2015.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>7.972724</td>\n",
       "      <td>2035.182252</td>\n",
       "      <td>2022.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2.473089</td>\n",
       "      <td>2037.655341</td>\n",
       "      <td>2019.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>-17.993886</td>\n",
       "      <td>2019.661455</td>\n",
       "      <td>1992.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>-3.319375</td>\n",
       "      <td>2016.342080</td>\n",
       "      <td>1984.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>-7.461183</td>\n",
       "      <td>2008.880897</td>\n",
       "      <td>1990.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>3.944933</td>\n",
       "      <td>2012.825831</td>\n",
       "      <td>2000.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            third_pred  Employees_org  test_org\n",
       "2018-01-01  -33.810982    1923.389018    1916.8\n",
       "2018-02-01   21.299246    1944.688263    1944.5\n",
       "2018-03-01   19.486786    1964.175049    1962.2\n",
       "2018-04-01   24.797410    1988.972459    1984.9\n",
       "2018-05-01   20.739604    2009.712063    2001.5\n",
       "2018-06-01   17.497465    2027.209528    2015.3\n",
       "2018-07-01    7.972724    2035.182252    2022.1\n",
       "2018-08-01    2.473089    2037.655341    2019.1\n",
       "2018-09-01  -17.993886    2019.661455    1992.5\n",
       "2018-10-01   -3.319375    2016.342080    1984.3\n",
       "2018-11-01   -7.461183    2008.880897    1990.1\n",
       "2018-12-01    3.944933    2012.825831    2000.2"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SARIMAX(2,0,2) RMSE Error: 8.440533999\n"
     ]
    }
   ],
   "source": [
    "error = rmse(df_forecast['test_org'], df_forecast['Employees_org'])\n",
    "print(f'SARIMAX(2,0,2) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1986.125"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast['test_org'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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