ARIMA MODELLING.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn  as sns\n",
    "import matplotlib\n",
    "plt.style.use('fivethirtyeight')\n",
    "matplotlib.rcParams['axes.labelsize'] = 14\n",
    "matplotlib.rcParams['xtick.labelsize'] = 12\n",
    "matplotlib.rcParams['ytick.labelsize'] = 12\n",
    "matplotlib.rcParams['text.color'] = 'k'\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('Data/airline_passengers.csv',index_col=0,parse_dates=True)\n",
    "df1.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('Data/DailyTotalFemaleBirths.csv',index_col=0,parse_dates=True)\n",
    "df2.index.freq = 'D'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pmdarima\n",
      "  Downloading pmdarima-1.8.0-cp37-cp37m-win_amd64.whl (609 kB)\n",
      "Requirement already satisfied: Cython<0.29.18,>=0.29 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (0.29.2)\n",
      "Requirement already satisfied: pandas>=0.19 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (1.1.3)\n",
      "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (0.12.2)\n",
      "Requirement already satisfied: urllib3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (1.25.10)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (1.6.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (1.19.5)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (1.0.0)\n",
      "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (41.0.1)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in c:\\programdata\\anaconda3\\lib\\site-packages (from pmdarima) (0.24.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=0.19->pmdarima) (2020.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.22->pmdarima) (2.1.0)\n",
      "Requirement already satisfied: patsy>=0.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n",
      "Installing collected packages: pmdarima\n",
      "Successfully installed pmdarima-1.8.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 20.3.3; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the 'c:\\programdata\\anaconda3\\python.exe -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jLsoaoB7SlYigAqQoijLE2dke+wGfKRdctBTsHtJVDUEFSFEUZYizoz12plvGXHBRUrB70BiQoijKCGEwLKDIZnTu3t/TtRhVBUhRFGWIU9MR2wLaXVlwmoSgKIoywujwGZq6Yq+zyZQLbrtVa648V11wiqIoI4qdcawfSK0jaTy2WgI0IT/cBadZcIqiKHs8NVb8Z0qB2/E6UxaQXeJnfF4cARqgCKoAKYqiDGFqrAy46UUePGE5AO1+M+BSOLHoDhh2WMI3Lq4AqQWkKIqyx7HTSgYYkxtZkbohzRWxt7f5CZe0Mbkust19qqcCpCiKMgKwXXBjciMrUtd1pFeA4rnfgIimdJqGrSiKsgdiu+DKo1hAqfTkiYadgDA+3ylARdmCK8wN2OIzdPmTdwOqACmKogxhErGA0r0WaItlAU2wLCCXSKQVNAARVAFSFEUZwtgWUEU0CyjNAtSfBQSR1RAGMgYVIEVRlCR5d2cX173XxOKtkQ3i0o2dhFDudVOWaQFqdV4vugClPoaEBEhEXhWRDhFpCf2sDtt3nohsEJFWEXlcRMrC9pWJyGOhfRtE5LykR6goijKE+GBXF6c+u4sblzVz+vO1vLatM6P3sy2gaFlw6Reg+EkIAKO8zm0DaQ2ejAX0LWNMQehnFoCIzAXuAL4KVABtwG1h59wKdIX2fRm4PXSOoijKsOQrL9fRHhZwf2p9e8bu1ek3NIaV4XFJsDOp7f5K92LUeFUQetir0LltbZMv6ft4kj7DyZeBp4wxrwGIyE+AlSJSCASAM4F5xpgWoEpEniQoVleleF9FUZTdzvu7uthsfdN/q6YrY/ezrZ/RXhdul2TUBecPGLZZAjQuigU0o8gpH5kWoF+JyPXAauDHxphXgbnA0p4DjDFrRaQLmElQgHzGmDVh11gGHBXrBtXV1UkMZ89hpM4bRvbcYWTPfzjO/WcrsrEfm3mBjqTnkujxy5tdgLf3dbHLR3V1Na0Nzu1b6luprq5Lagyx2Nkp+E1u3z09hs2ffhJxnNca2/KaFqqrax3HVFZWxr1XogL0Q2AFQXfaOcBTIrIAKAAarWMbgULADzTF2BeV/ga7J1JdXT0i5w0je+4wsuc/HOe+rLaL1+p2Rmzv8niprJyc8HWSmfvaTe1An7BMKs6lsnISrbu64OO+sXS4c5IaQzyadnYBfdeeVJRNZeXEiONyWnywfEfv6y1dWUmPIaEYkDHmLWNMszGm0xhzH7AEOBloAYqsw4uA5n72KYqiDCtuXBb90ZWpfjwQuQaopyVCmTc9lQiiEbkGKLpMTMx3E9ajjrrOQNLvxUDTsA0gwHJgfs9GEZkG5ABrQj8eEQmX+vmhcxRFUYYNrd0BntkYPeU63XXYwrE7oY7JDT7xI0rxpFGAElkDBMHFqNMKU4sD9StAIlIiIieKiFdEPCLyZeBI4DngfuBUETlCRPKBnwOPhqylVuBR4Ociki8iC4HTgb8kNUJFUZRBZmOLn1iVZho6DQGTmZ48O+wU7JDlU+ARR0XsDj9pq4idSAp2D9OsRIRPGtMsQEAWcB1Bp+Au4NvA540xa4wxy4FLCQpRDcH4zuVh514O5Ib2PQhcFjpHURRl2LCpJfYaFwNxO5amQoQFFBIDEYlww6XLFZioBQSpZ8L1m4RgjNkJHBRn/wPAAzH21QGfT2pEiqIoQ4yNLfEfrA1dAUpy0l9YpqYjugUEUJrtcsSI6joDccUiUSJiQHGuOb04wy44RVGUkU48CwjSmwQQjm0Blef2iUHGLKAkXHDTd4MLTlEUZUSzsR8BylQmXLQyPD3Y1ajTkYgQMFEWocazgCwBWtfkwyQRD1MBUhRF6YdNrc5v9nYpnExkwnX6DQ1WGZ5RYW6+iK6oaRCg2o4A4VMpyhYKs2LLREWui4KwbIgWX2Qr73ioACmKMizpDhh8gcwE/21sC2jfsmzH64bO9I9jZ4wyPD1kohzPm1ZZIbsPkI2IRGTCJRMHUgFSFGXYsK3Nz80fNfO5Z3cy7s9bmfTXbTz4SVtG79nuM45gv0tgTqnzoZvujqQAm6xYzNhcpxikey1Qa3eAH73lLGwztyyr3/NmpJCIkGoxUkVRlN3CivpuTvznTpq7+6wNn9/w47cb+eK0XDwuiXP2wNlsud/G57kdyQCQmSSEDc1OAZpaGF+AUrWArv+g2VFo1SXw7XkF/Z4XYQElkYigFpCiKMOCe1a1OsSnh7rOANvb4icJpIKdATepwB2RAJCJJIT1zc4H+VSr6kA6XXAf1nZx2/IWx7bL5hQwf1R2jDP6sNcCfaIuOEVR9jQ+ru+Ouc9ePJlO7PjPpAL3bklC6E+AbAvI7pyaDL98v9lR6WFivpsf7RezbrSDVFxwKkCKogwL1jTEfrBta8tcPbZN1iLUyQWe3WIBbWiJ74KbaKVH2+NMFGMMS7c7u7recGgxBXGy38KxG9Nt7idlPRwVIEVRhjy1HX5q4zzk7dX76cS2gCYXuCNToDNQiqc/C2higfPBv60tQFesgnVx2NYWoCnMtVmYJZw0yRvnDCdlOS7C9bjFZ2jpTkyQVYAURRnyrI5j/UDk6v10YseAJhe4I8rupDsJod1nHFadSyItnhy3MDZsYaphYEK8usHp2pxV4kEk8YQOEemt0t3DjgQtUhUgRVGGPGv6yazKbAzIee9J+ZEuuHQLkH3PCflust2RojC5wGkV9VexIRqrLHGfWdx/6rVNRa7z/bCreMdCBUhRlCGP/S398ApndlamLKAuv4mIL00scFOULYTLQYvP0J3GRbHr7RTsgugLQidZ2/srmhoN+72dXZL86pwKa8GqCpCiKHsMtgV0zPgcx+stGbKAtrT6CZeVsbkuctyCS4QSOxMujVaQHf+ZUhhdFGwBshevJsJq672dVZK8BWQvkt2uLjhFUfYU7BjQMROcQfJtrf6MNIWLTEDoE4IIN1waU7E3tMRPQIg2HoCNzclZQMYYVtZHxoCSpSJPXXCKouyBtHQHIlbo71OWRUl2nwXiM5GtC9JBRPwnzOJIdyWCcCJccIWJueCStYB2dgQcGXy5bmFyDHdfPCItIBUgRVH2AOweM1ML3OS4JaL5WiYSEewHevjDOTIRIZ0xoEQtIDsGlNx7EJGAUOLBlUQGXA+RFpC64BRF2QOwYxQzQzEKu1JzJtYC2S6tSeEuONsCSpMLzhjTbx24HuzU7K2t/qQqhEdLwR4ItgWkLjhFUfYI1tgPyVDplwgLKAMCFJEBlx/bBZeuJIRdHQFafX0iUuARRx+gcPKzXIwO64zqN8lZgnZsbfYAEhCAqOuAEskKVAFSFGVIYz8kZ5bEEKAMuODsjqThrqbwGBSkLwZkx3+mFLrjLgyNiAMl4YZbFUPck2VMrsuRll7bGWDOQ9v7PU8FSFGUIY2dgt2TJjw+L/MW0HZLgMJdTRHVENLkgks0/tNDKnGgdFlAHpc4LDFIrDiqCpCiKEOW7oBhnVVduTL0LX2CZQGley1Qp99Q3+lsiR3+kM1UNYRkBWhSvnN/okVJazv8DpHIcQetrYFiL0ZNBBUgRVGGLOuafISFQxiX56I49ODPdAzIdr+VWy2xI9Kw01SQtL8q2Da2BRTPBWfC1krZ1s+MIk9KTf3G5iYvJypAiqIMWeyHaXj3zQgXXJvf8YBNlRorldgOtNsuuMY0WUB224kpBf1YQAm44Da2+DjmqRqmPrCN//uwGUif+60HtYAURckord0B7lvdypPr29P6sI/FNsutFp56XZTtojCr7xt7pz/YHTVd2Isp7W/4pRnoCdTQGeDfu7oc2+aU9hcDiu+CCxjDRa/W8f6ubhq7DP/z7yZW1nfzzk7nfQZSAy6cgVhAqd1RUZQRgzGGM16o5a2a4IPr7Om5/OGI0qRK9yeLLUDjrG/Z4/PcjnVCW1r9jPIOPI4RToQFlBffAkpHEsIrWzscnUn3LvEwMUkLaHOoLFHPgtKH17Xz7s6+bDcDvLC5gyVWE7pDKpz19ZLFthATQS0gRVESYnWjr1d8AB5a284v32/O6D3topZjbQHKYCp2ZAac83EZLQ07VavwX5udonD8xP4bwxVluxxj6Qr0VSJo7Q7wP+82Rpzz4CdtjlhTtgsOKs+OOC4Z7L9NIqgAKYqSEMvruiO23bCsmQeqWzN2z34toIhEhPS54OwkBPsbfp5HHJ1AuwLQnmRH0hX13ZzzYi1ffbmW9c0+XtzS4dh/QgICBM4KDdBXweGmj1vYGqUytV2C54DybHI9qVmydk+gREjqDBGpFJEOEflr2LbzRGSDiLSKyOMiUha2r0xEHgvt2yAi5yU9QkVRhgQr6iMFCOA7Sxr4tCn5PjSJYAvQWKvmWCbXAtnWV4UlQCISWY4niXpwxhguXlzHc5s6eGpDBwv+scPh9ivwCIeOScwqibYWaEebn5s/SsxCXZii+w12jwV0K/BOzwsRmQvcAXwVqADagNus47tC+74M3B46R1GUYcby+ugi4zPw0Nq2jNzTTgSwLSB7LdDm1vQJYbwqCD2kkohQ1xlgRYz3FOCo8TlRu6BGw14r9EmTj+c3d9CRoB4vHJua+w0iBToREhYgETkHaABeCtv8ZeApY8xrxpgW4CfAGSJSKCL5wJnAT4wxLcaYKuBJgmKlKMowI5YFBLA2AxaQL2Co6YgfA4oUoPRZQHZF52gPWDv12F5EGo9orrFwPpOg+w36Fuf2UN3oiyizM6MoejKDR+DgBC2teOR6hKLs5Nx4CWXBiUgR8HPgWODisF1zgaU9L4wxa0WkC5gJBACfMWZN2PHLgKNi3ae6ujrxke9BjNR5w8ieOwyf+bf4YGNLXsz9K2paqK6uTeqa/c29plMImNze18Uew8Z1nziO8bcK0HfMuvqOtLynxsCOtlwIq3DWvPVTqnc4jys3WUDf+pk3121nVlf/IlRdXc27dS4gtshM795GdXViLr3cZue1Pq5pZVu2AfoE8vTRbfymKVJo9i7ws3X92oTu0x+lbi9NSTjWEk3Dvha4yxiz2Uq5LADsFItGoBDwA00x9kWlsrIyweHsOVRXV4/IecPInjsMr/m/taMT2NX7Ot8jjorNW7s9VFZOTvh6icy9eWcXsLP39cTCLCorJzqOGdcdgPe39b7e2e1i2vQZjooFA6G+M0D3kr7rFniE+bMjx3tgVwuPbO97BNZnlVBZWRr32j1zX7K6laBTKZI5pR4WzZuR8HiL2/3wUV/xz82dbtpwEXwMB/nCPhN5tq6OFVYCwnFTi6msLE74XvGY9MlONrR39X9giH6lSkQWAMcDv4uyuwUosrYVAc397FMUZRhhxyqOn5hDTpj3qb7TpK0WWg/9ZcABFGS5KM3pE5vuQOLN0OJhx56ixX8g0vVlN8+LR7z+RadOyY25LxrlXpfD/dXmMxFdZGcUeThqfGSywcKxqScg9JBsIkIittLRwFRgo4hsB64EzhSR94DlwPyeA0VkGpADrAn9eEQk/GvD/NA5iqIMI+z4z7zSLKZaqb+fJhH/SISIdTgxHm4DLcYZj/5SsHuIFntJFFtgve6gkJwy2ct35hUkfB0IZuRVxojxAEwpcOP1CEePd7r8XAKHpCH+00OyiQiJCNAfgenAgtDPH4B/AicC9wOnisgRoaSDnwOPGmOajTGtwKPAz0UkX0QWAqcDf0lqhIqiDDrLLQGaU5rFVOuBl+5U7G3Wmp5oFhBE6YeThkSE7ZYVZXf8DL93eOGFus4AtQmmntkp4/ccXUb1ueO4/7hR5Gclv6ZmRpxePj1dZBeOzXZU9D52fA5F2elbDjqtKM0CZIxpM8Zs7/kh6FrrMMbsNMYsBy4lKEQ1BOM7l4edfjnBCGEN8CBwWegcRVGGCcaYCAGaW5bFNKtK87rm9GWgAWxr798FB6k1ZItFTZttAUV/VLpEHAVSIXEryK7aEGt+iVJZHLuYaE+juYIsF3cdVcZhFdmcMtnLjYeVpHRPmy9MzWV6SITGx3BbhpN0LThjzDXW6weAB2IcWwd8Ptl7KIoydNjaFqAxrNVAvkeYXOCOePDafXtSJaIYaIwHml0FIB0CFGEBxRGHymKPI0ZW3ejj0AQWdtoCZKeUJ4vtDgxnZlih0aPG53DU+PKU7hWLMq+b108fQ3Wjj+lxXII9aDFSRVHiYsd/9i714BJhr8LMxoC2tSZoAeXbFlAmYkCxv81XFmUBfSV0EklEaOkO0BQm6lkuGOVNzRUWT4BmxbGO0k2ex8X8UYnFlbQWnKIocbEFaG5p8GFmW0BpjwEl6IKLaMiWjhhQhPUVxwKy2hhUJ/A+RMvwc6VYVXxaoYdYV5iZYquFTKECpChKXKIlIEAw9hJeKWZ7e4DW7vSkYnf4Itthl8ewEKLFgFKtSt1fM7pw7OyzRCwgOwEhVfcbgDfkGrUZm9vXRXaoMTRHpSjKkMEOqu8dEqAsl0Q8/NenKRHBTsGuyHXFXFxaluMiL6ySc6vPpNwcrr9WDOHY2Wfrmnx0B+ILoF2GJ9UEhB6iueFmptjpNJOoACmKEhc7qL9XWPbbNCsOtC5NcaBEFqH2ICIRcaBobakTpcNnHEkXbokfnynKdjlaEfgMbOjnfbAtILuq90CJJkCz4sSGBhsVIEUZRrT7DL94r4mzX6zlqQ3tGb9fa3eAXWEFQT3iFIO9LPfT+jTFgZKJwUDqa4HCXXY7oiQg9Befsa2g/lKxIwQ2DS44iJ6KPVTjP6ACpCjDirtXt3LDsmae39TBRa/W8VGUJnHpxK4uPT7fjSfMFbZXxFqg9AhQsi6qga4FaukOcPaLtUz66zb+o6oef8AkFf/pIZE4kDGGj+q6afFFluGZkCYLKNpi1Jm7MQMuWYauNCqKEsFjn/b13ekOwI/eauDpz2ZmTQdEurLsB72dir2uKU0xoCQXaUauBUpMCO9d3crzm4Ip1PdXtzGvNIs2nzN+Ey/+00OlFWexM+ECxnDmC7W8srWTHFcunQFn59NxCSzaTISoLrghbAEN3ZEpihLBuzudFk/V9i4auwIZy3KyLYnJ1oM+IhV7ABbQ6oZufvthM0XZLn64oJDRXnfCi1B7iFwLlJgQ9ohPDz96uzFizc+iBIp12haQ7YJ7flMHr2ztBKAzEOnOs1uLD5SxuS6mFrp7k0GmFboH1Cp7dzF0R6YoioNOf/TMqr9nqBspwEbLkrAtILsT5+ZWf8xxRsMfMJz9Yi0PrW3nzpWtfO7ZXTR0BiKELGkXXIIxoGhdXu222F+dmd/vdWzLw64K8awldOEIA2tnHfVaIty6qJQ5pR7mlHq4ZVEpkuL6okyiFpCiDBNiZVbds7qVi2fnZ+RBE2kBOR+UuR5hfJ6rN2YTMEHRileXLJx1zT5H6vaqBh8LH69hS8ouuP4FqDtgaO5n3dIFs/Ipyen/e/qkAjceCWbAQbAlREt3gIIsFwFjIiytcMbkushKsX9ROAvH5rD08xVpu14mUQtIUYYJsQL8K+p9vF2TeBOwZIiwgPIjv7PamXCfJhEH2hBl3ZAtPnNKPMzuJ44xNtdF2FIg6jr7XxS7odlHvEM8ApfO6d/6AfC4IheBfhqa2we7uuP2KEqX+204ogKkKMOEeAH+u1e3ZuSetiUxpTDyYRmRiJBEHKi/9Tol2cLtR5b2mwbtdklENYH+3HBr+kmVPmtaLhMLEncSxSrO+tzm2NYPpG8R6nBEBUhRhgnxaq09s7GDQIrlZ2w6fMZRFVqIvmAylZpwtoUVTlG28PiJoxMubGnHgaJZV+H0t1bn2/MKE7pvD5GWYEiANsYXoHSlYA9HVIAUZZgQz7Jo7jasTXMx0M2tzuuNz3OT7Y60ROxqCMlkwsWygMq9Lh77zGgWjE68W6dd/n9NgzNjsKEzwO8/buan7zSyrskXYQGFC9ilc/KZW5bc+ploVSG2tPr5sJ+1WvlZQzdJINNoEoKiDBPszKoxuS5HxtYHu7oTDv4ngu1+sy2MHqbai1GTiAHZFtADx5VRluNiTmlW0p06Z1lrcVaHBMYfMNy3po3r3muiLlQj7skN7RRYXUf/7/AS9ir00O4zzClN/tEYzQX3gpV8cHB5Nm/vdMbremrrjUTUAlKUYUB3wERYC6dPzXW8Xlab3qoI9v2iVVqGSNfThhYf/n6Kcca6x9zSLA6tGFibaDtRYXVDN63dAU55dhdXvNHQKz4QLJr6sWWZVBZ7mFbkYW5Z1oAyCu121J82+Xluk7Nc0kmTvVw3q7P3dWGWcOJEb9L32lNQC0hRhgGbWvyEL68Zm+tiYUUOd67sSz5YVpveTLhELaDibBejclzUhh7w3YHgeqAphfEfL+0+Z9kbt6TWliDCAmrw8fC6dt5MIEMw1y1MTDEbbXKBB5cEU9EhmM1XZ1XlPnGil+xaP9MmlvFBbTdnT8tLKM17T2XkzlxRhhG2+22vIg/zRzkfuMvqulPugxOO7R6zqyCEE/HtP4E4kF0ux64zlyzj81wUhsVTmroN/1iX2CLdGcWelBvC5UQRsfawbw1jc129rr2TJ+fy3/sVMX0IV6reHagAKcowwBagaUUepha6KcoOe+B2mbT144HINOZYFhAMbC1Qoi6+RBGRiLpnVdsTswrTVS/NjgOFc+S4nCFdlWAwUAFSlGGAnQE3rdCDiDDfytT6II1uuI3NiQvEQNYCRQpQ6iIQr/KzR+ALVtysh2hFPAeCnQkXzqJx/deUG2moACnKMMBeW9Pj8rLXyKQrEaHLb9hm9cWZGKUKQt94kl8LFOniS309TLyKCfuMyuLMadEFaGaaBGivothzOFIFKAIVIEUZBqyzrJEei2PBaNsCSo8AbW3zE57IVpHrwuuJ7T4aSF+gdLvgIDIRIZyDyrM5dkIOuVHWMqUrfT2WBTSpwM2UNMxvT0MFSFGGOP6AYX1zZBICEJmIUNuVdCJCu89w1VsNnPjPnfz6gyZauwN8sMspZPHiPxBpAa1v9scchy+kbMkkOSRKvFjOwWOyyfO4OLQicnGrvYh1oMSKAR0xVuM/0RjZKRiKMgzY3Op3FM0c7XX19v+ZXuShwCO0hMow13caNrX6k3qY/2llC39YEUznfqumi7tWtVLX4UwfjlaENJxROcEMtObu4DjafIYd7QFHm4FOv+GKNxp4oLqN2QU5rGpxilw6LKDJBW5y3eLIPuvhoPKg8Jww0dvbm6eH3DjWXTLY7Sl6OELdb1FRC0hRhjh2SnO4m8clwj6WFWRbL/3xglUss6Y9gNUUlIVj45fEEZEo3VGd47767Ubur27DACtb3ITfItU1QD24RJgZxQqqyHX1Ctx5M/IcTee+Nbcg5fv2kOuRqLXdjujn/RupqAApyhDHLpppB7qjueGSYWVD/HjNN/bO58JZ/bcliChFEyac/1jXxp2rYlfsnpDiGqBwornhDirP7nWBleS4+Ntxozh3Rh7fn1/IDxYkV3S0P+y/z7RCd1JVtUcS+q4oyhDHFiA7YB7MhOt7uNslZuKxs93Pro7ovWrKclz8flEJJ0+OnjlmYycirA+tBVrd0M1/LmmIe2463G89zC7JApwlcA4e47RA9i/P5vbyzFgl04o8jvVH6n6LTUIWkIj8VUS2iUiTiKwRkYvD9h0nIqtEpE1EXhGRKWH7ckTk7tB520XkikxMQlF2N01dAboTrHeWKp9ECJDze+M8ay3Qx3WJV6NeYbWkLsoSrty3kO/PL2TJ58ckLD4QuRi1xwK64o0GWm2fnkU6EhB6mBUlpfrADIlNNE6a5Kztdta0vN127+FGon/1XwFfN8Z0ishs4FUReR/YADwKXAw8BVwLPAQcGjrvGqASmAKMBV4RkRXGmOfSNwVF2b385J1Gfv9xC6O9Lu47pozDx2b2G251U3wBmlXsIctFb6LCljY/9Z0BShOoMbbSallw8mQvVx9QNKBx2i64VfXd1HcGWGJVIyjOFhq7nIKUfguoD4/Afkm0dUiVz07y8suDi3l5SwenTc1VCygOCVlAxpjlxpietBET+pkOnAEsN8Y8bIzpICg480MiBXA+cK0xpt4YsxK4E7ggjeNXlN3KJ43d3PJxCwbY2RHgKy/XsTlOU7VUafMFHEVBhci1JtluiVhI+VGCbriV9c7j5qTQGmCede6qRh9v7nBmm+1d4uGmw0sjzu0vzTsZ9ipyOxaknjTJm7Yst0QQES6fW8A/PjOar81MrKX3SCXhJAQRuU1E2oBVwDbgGWAusKznGGNMK7AWmCsipcC48P2h3+emYdyKMii8u9P5wK7rDHDhq3V0RUn7TQdrmyIXa0ZbEBrphktUgJzimUpvmpIcl6M3UMDAA584i4HuNzqb06Z62SvPGXdKRfhsXCL87fhRXDgrj/+YW8BNC0vSdm0lvSTseDXGXC4i3wYOA44GOoECYKd1aCNQGNrX89reF5Xq6upEh7NHMVLnDcNv7lWfZgHOh+U7O7v5zosb+K9pyVch6G/+r+90A30unPFZXVHPGev3AH1upqXrazkhe1vcaxsDy+tyCdpVQbwNm6luH7iYTs/OZn3YY+WZje2O648PNLD2k138rNLFf3ycQ6tfOG6Uj/y6DVTXD/i2Ubm8PPhv3aYa6tJ76ZQZbp/7gVJZWRl3f1KRP2OMH6gSka8AlwEtgO0wLgKaQ/t6XndY+wY02D2R6urqETlvGJ5z37JuF8HvXk7+tjWLSw8Yn1QL6UTm/1hrE+H/ZeaPK6KysiTiuGPyO7hpfW3v642+XCorp0QcF86mFh+t/h29r4uyhEVzp6e0Yn9RezMv1Tb1vvYb57VOmD2eyoocqK5m9bnjqWkPxK0gvScyHD/3mWKg64A8BGNAy4H5PRtFJL9nuzGmnqCrbn7YefND5yjKsGR5fWwr51mr/XI6sDPgYlV7tl1wqxq6+83Si+Z+S7VcjL0mKRyXOMdZkOUaceKjOOlXgERkjIicIyIFIuIWkROBc4GXgMeAeSJypoh4gZ8CHxpjVoVO/zNwtYiUhhITLgHuzchMFCXD1LT7HR08bT7Yld6OpABrLAGaEaNq82ivm7Fhq/u7ApHrh3rY0OxjZX03Kywx3TsNPXHiCdDMYg/5Wbr2XekjkU+cIehu+wNBwdoAfNcY8ySAiJwJ/B74K/AWcE7YuT8Dbg+d0w78WlOwleHKciuwn+0KPuh7eL822JE0XUUnjTH9rgEKZ15ZFtu39LkHP67rdgT3A8Zw7b+buOnjFqIZR6kkIPQwyutmYr6bza2RDen2jSNOysik368jxpidxpijjDElxpgiY8w+xpg7w/a/aIyZbYzJNcYcbYxZH7av0xhzUei8CmPMbzM0D0XJOB9bFsMX9sp1lPavaQ+wrS22hZQs29sDvUVGAQo84rBybOJlwnX4DF9/tZ7ffRRdfCA9AgSxrSC7d5GiqD2sKAliW0DzR2Wzj/XQfz+NbjjbhTaj2BPXuoolQL6A4YwXdvHY+vZop/UypzQ98ZgFMQQo1nZl5KICpCgJ8rEVtJ9XlhXREO79NDWEg/5L8NhECFDIYnvwkzaW7ogvjKO9LkZ707MYNJalY4u1omgKijKs+bTJx++Xt1CSLXxnn8LePjnppjtgWG2VrZlX6mFLq7MQaDoTEaobnffrT4BmFHnIcUNnKPxS0x6gpt3Pa9ucaeMziz20+42jwoItXqlgi3LP2Ioy9LdRhi8qQMqw5ZPGbk74507qO4NBja1tAW4/IrLMSzqobvQ5msKNz3NR5nWzn20B7UotEWFXh5+XtnTybk0X/9zodJn1J0Ael7B3SZajLffHdd0R7RZuPKyEqYVuTn9uF582+3FJsOVCuhiT62ZcnssRD4uXHaeMXFSAlGFJbYefM16o7RUfgKc3tHProhJcGWh9bJe2mRsK2FcWecj3SG+159rOQNIdSQHe2NHJNe828XZNF7FW78yIsQYonHllTgFaVtsdYUnNKfUw2utm6ecreHFLB9MKPcxNs3ts31HZbGvrWxelAqREQ21iZdjR4TN8+aU6NrY4U32bu01EF850YScg9Dyw3S6JSC9OtiNpc3eAc1+s5a044lOSLcxIYNGm7Up7ekN7r0sOnLGeXI9w6pTctIsPBLuO9uB1w+lTE2/roIwcVICUYceNHzbzZk30WMsHaUwCCMdOwQ5/0NvZXR8k2ZH0zR1dNHTFrlpQmiP876ElCVV0tgXo35YYzk7DYtNEOG2Kl9uPKOXCWXn84zOjmVKozhYlEv1UKMOKgDH8dU3s1s7Lars5a1p672mM4f1d0V1w0NNrpm9M9rH9YaduTy5w88VpuSwYnc30Ig/TCj1RK2BHw26JYLN3ye5xhYkI587I49wZ2oxNiY0KkDKseLumi+1xyuEsy4AFVN3oo66z755FWc7+O5GJCF1JJSLYgnXl/MIB95EpyXHFrEQAMDtNa30UJR2oC04ZVjxhLabc33r4L6sNPvzTyVuWu+/A8mzcrj5xmV7koTCr73VDl+GTJGJRtssu1QWb8VKq7W6hijKYqAApw4aAMTy53llx+j/3KaQou+/h39hl2NAS/dv/QLEF6JAK50JLlwgHlju32efEYnub35GunONOvSROPAFKR8FRRUkXKkDKsOG9Xd1saesTlzyPcMLEHPYtSy0LrT9sMTl0TORK/0OsbW/1U3mgB9v6mVeaRZYrtTTyWBUHKnKDa5cUZaigAqQMG2z32wkTc8jzuFhglX5ZlmQWWjxqO/yOmmxugQPKExCgBC0gO/6zXxIN7WIRKxFB3W/KUEMFSEmZ5u4Aqxu60x57CccYEyFAp08Jri2xFzmmMxHBFpJ5ZVkUROlpc+CYbMINlzWNPuo6orsCO/3B9Uod/sjSPdHK2CTLXkVu8qNkze2uFGxFSRT9RCop8WFtF59/vpa6zgCLxmbzxImjHQH6dPHerm7HwlOvG06Y5AUiBeiDNPbledsSoIOjuN8ACrNczC3N4qOwBatv1XTx2cnOBZj1nQHOeGEX7+/qZpLXy6YOZ522/dLQssAlwpxSD+/stBrOpandgqKkC7WAlAHjDxgur2roTVGu2t7FcxloSw1w00fNjtfHTvBSGLJEZhR7KAj7xl/XGYiZhpwsicR/Yu2zxQvgV+839brdNnU4//vluoVZabJSoiUiqAWkDDVUgJQB85fqtogaadEeuoly+/IW5v19O/Mf3s4Zz+/iqrcaeltHP7nBKWxfm9m3wNElwj5RrKBkMMbw+Kft/O8HTWxsCcZ8Ov2G9ywXmR3rceyzsuPsag3rm33cszr2Itp9yrLwpMl6jC5AagEpQwv9SqQMiMauANe91xSx/d8DbEewrsnHf7/d2FsLbUOLn5e3dvKnla3MtL6571uWxYkTvRHb3gjLPHu3potTpyRef+ye1W1c8UYDAL//uIVnTy6nqTvgqKM2Md/NxDhFRm1xen9XF11+Q3aoa+qv3m9yVNS2SUf8pwc7EWFcnouSHP2+qQwt9BOpDIgbPmhmV0fk03RZbTf+WD2f4/DGjs6ohTh9BlZYjeB+sKAwIr5jP/wXWz1w+uPhdW29vzd1Gz77zE4ue70+7j1sJua7GZ/X91+qw9+XELGivpu/r43fkTQdGXA97F+ezaSCvpTrzyUhxoqyu1ABUpJmbaOPO1a2RN3X3G2oHkBF6pX1iZ0zt9TDyZO9EduPHJ/jeL2stpv6zjjmhsVHlsuuqduwvtkZR/rclMj7hiMiHDLGOY43a4JCeN17TQ6B3avQTZ7bKbkHR0nvHihZLuGpk0Zz2Zx8fnpAET8/sDht11aUdKECpCTNj99pjOtKem9n8m64lVa30cMrssmJsmbyBwuKovb7Ge11O+IeBiI6gcaiqStAiy++1faFqbkJtRSw40Bv7Oiipt3PMxudMaxfHFzML2d19lZxuHROPtP7aTiXLFMLPfzqkBKu2LcwoUrairK7UQFSkuKVLR0RmW5zrQKX7w2gEsFKq93Bbw4r4YkTR1MWFrfYb3QWp8axQo4e57Q+Fm9NTID66yF02hQvfzyqNKFGd4dbAlS1vZOXtzjHMbfUw2cneVlYFmDFl8ay+uyxXH9ISUJjVZQ9CRUgJWF8AcN/v93o2HbImGyu3r/Isc3OHOuPhs4AW8PqoWW5ggU+D63I4ZVTy/nm3vlcNiefh44fFVcEjrbccK9uTSwlfG0cATplspc/HVWWcHmceWVZjAoTzaYuw80fO1PIT5zk7Y1hFWS5qMjT8jjKyESz4JSEuXd1KysbnA/rXx1czMQC5wP0o7puOv2GHHdiD23b/VZZ5OnNHJtS6OHXh5YkdJ3DKrLJctHrHlzX7GdDs6/fZmh25eqLZ+dzYHk2BVnCyZO9SbX4dolw1PgcHv20L+HATqI4alz8WJKijBTUAlIS5vYVzsSDc2fksX95NmNy3UzM7xOh7kBkC+t42AkIA12xn5/l4qDy5LPh1jY67z+nNItzZuTxuSm5SYlPD7YlFo7X3X82naKMFFSAlISo6/CztqkvK8wj8NMD+lxvdl+eZNYD2fGfVErG2A//RBIRbBfc9KLUXGJHjostQIdW5CTc3VRR9nRUgJSEsAt8zinNYlxY7MKuEJ1MIsIKywWXSs+ayDhQJ4E4RVKNiWweN70oNc/01EIPUwuji5idKKEoIxkVICUh7NI2dgFQexHlvxNMxTbGRLjg5qRgAe0/OtvRnXRXR4A1jbGTDOo6AzR29QmU1w3j81NPCoglNPHcc4oy0uhXgEQkR0TuEpENItIsIh+IyGfD9h8nIqtEpE1EXhGRKda5d4tIk4hsF5ErMjURJbPYFpAtQAtGZRHuWFrT6GNFff9WUE17oLeYKQQLck6JYT0kgsclETGW8Pp0xhhe3drBt6rq+Y+qel60UqSnFXkGFPexOXp8ZKJBaY7EbBanKCORRCwgD7AJOAooBq4G/i4iU0VkNPAo8BOgDHgXeCjs3GuASmAKcAzwAxE5KW2jV3YbdpO3BZbFU5Ttioh93F/dRn/YGXCzS1MXgIMsAXqnpgtjDP9Y18bhj9fw+edr+Wt1G/dXt/HN15zldlJ1v/VwxLhs7FkcOS4nI60qFGW40u//NmNMK0Eh6eFpEfkUOAAYBSw3xjwMICLXALtEZLYxZhVwPnCBMaYeqBeRO4ELgOfSOYmRSG2Hn5+808R7u7owBkSCVsj1h5SkvehkQ2eAT8PK0rgF5kZxk32lMs+RdfbQ2jauObAo7hoaO0V57zRUbLZL2ryzs4vHPm3n4sX1Mc7oY0aaBGiU180+ZVl8GJYNqOnXiuIk6f9tIlIBzASWA5cBy3r2GWNaRWQtMFdEdgDjwveHfv98KgNWglz5RiOPWR1CVzX4aPUZ/nLsqLTe60MrpXpWsSdqaZfPTcmlKLuBplBMZVdHgOc3dTgKYQaMYVltNy9s7uDNjdl82uVM7d67NHUBOKA8aH30RHZWNfj4v4+i166zmZYmAQL4+ux8/nNpAwCjclycNlUFSFHCSep/m4hkAfcD9xljVolIAbDTOqwRKAQKwl7b+6JSXV2dzHD2GJKdd5MPntyQCxFOHnhqQwcvLPuEvfKSr0gdMPCvXW5qOoWTyv2U5wSv8eJmD9BnVeyV3RFzzCeUZfHI9j4r5o4PapjVFXTfvbzLzW/WZVHT1WOheQBnwc/ithqqq7cnPXabaXle1rb1WYK2iMbC27Sd6urEi5jG4zCB/5np5pNWF58d46N+0zpsG2ykfuZB5z4SqKysjLs/YQESERfwF6AL+FZocwtQZB1aBDSH9vW87rD2DWiweyLV1dVJz/uB6lb8piHm/iebR/H7+aVJj+W695q4cXXwz/NkrZvXThtDUbaLrVvrgD5r64i9RlFZWRD1Gt8q7eKRp/q+kyyt91A4YSJZLvifN3fQ1k/Rz8/Mm8rYNJSmWVRTz9o10WNQ+5ZlkZ8ljv5BPRw1dypjctNXGmfmzNj7BvK331PQuY/MudskFCyQYOGqu4AK4ExjTM/XyeXA/LDj8oHpBONC9cC28P2h35enYdwjmies7qB298uH1raxrS25ltQbW3zcHNb2en2zn99+GHzdXwZcOAtGZTEnzI3mN8HxPLupo1/xOW9GXlrEByITEcI5fWou39w7uoCWe3VlgqLsLhL933Y7sDdwqjEmPPDwGDBPRM4UES/wU+DDUAICwJ+Bq0WkVERmA5cA96Zn6COTxq4Ar2xxCtCti0rYq9BZCueOFYnFPHq4/v1muizP023LW/iorptPwtbRCMRNJRYRvlKZ79j20CdtPG2J5iFjsvnvGZ3cf2wZ9x9bxoufK+f3i0qSGnM84vXWOX2ql1NiVNW2G90pipI5ElkHNAX4JrAA2C4iLaGfLxtjdgJnAr8A6oFDgHPCTv8ZsBbYACwGbjDGaAZcCjy/qcMhFNOL3OxblsW35jm/0d+9qpUmW1FisKqhm7+tjXRXdQXgay/XOhqpVRZ7KMiK/7H54rRcwuuQrmjw8a/NTgG64dBivjDWzylTcjllSi4HlmenZf1NDzOKPZRkR15vTqmHGcVZZLmE/97PGY48Z7p2DVWU3Um/AmSM2WCMEWOM1xhTEPZzf2j/i8aY2caYXGPM0caY9WHndhpjLjLGFBljKowxv83gXEYET1iZb6dNyUVEOG9GvrMNQLdxVGSOx3X/biJWF+1Pra6g8dxvPZTnujlugnNNkD/s+pMK3BlfkOkS4cAoVtBpYRl5l84p6HUXet3B14qi7D7U4T3EeGRdG6c9t4ufvNOIz1KF5u4AL1rut54unbke4et7O11fT2/oX4Be3drB01a3znhxkP1HJ1bJ+ezpeTH3fW6yd7e4uqLFgcK7mhZlu3j+lHKeOHEUy84aG7G4VlGUzKICNIRY1dDNN16r57VtndzycQu/fL+pd1+bL8A3X6unM8wgmVzgdlgkZ+zldCEt3tYZ1w23tdUfsTjzgNFZ3H9cWdTjpxW6Oa8ytrCE89nJXkdNtnBOmbJ7XF12SZ6ZxR5mW4VOC7NcHDXeq03hFGUQUAEaQjy9ocPhqvrjilYaOgPsbPdz6rO7eMayVD4/NddhScwq9jhaCXQH4MXN0buCdgcMF71ax66OPoES4NqDijl4TA63LCxhTK6LcXkuzpmey91HlbLk8xUUZyf2kcnzuDg1itCU5bg4dDf1wzlibA4HhLWJuHr/Ik0yUJQhhHZETYGnNrTzlzWtvdWUS3JcXLp3PsdMGNiK9yXbnYUxW3yGW5e38M+N7RElaybkufnuPs6YhYhwyuRcbv64LwPunxs7OGOa02pp9xmueKOBN2uc62Cu2q+Qw8cGYzdfnZnPV2c6XXrJcvb0PB74xJnccNIkL57dVA/N7RL++dlyXtrSwV5FnpSqbCuKkn5UgAbIR3XdXPhKHfbSlle2dLD4tDFJN1XrDhhH1eYeblgWuW53bqmHv58wmjJvpNvoc1O8DgF6YXOHoz32qoZuLnq1LkLQjpuQw/fnxyxSMSCOGJfNhDw3W8LWJJ0yefeWo/F6ZLe5/BRFSQ51wQ2Q+6tbI8QHgqnLP367EROnCVo0PtjVTWs/CzUBjhmfw7MnlzMhRs+aA8uzGZPb92dt7jZUhSyrFfXdHPvUzqjW1B+PLE1rGjQEM9GuCkt13rcsixMmaj00RVGCqAANgIAxPLU+emwF4OWtnTwfI/YSC9v9Fo1ZxR7+cmwZRXHiMC4RTp7kfMj3ZMP99J3GiGoEUwvdPPyZUYyKYk2lg6/OzOfZk0fzhyNKefKk0WS7NQajKEoQFaAB8O+d3Q63Up5HOKzCGVj/8duNdPkTt4L6E6B8j/DnY8v6XQQKkVlmz2zsYEebn1e2Ou/xpWm5vHbamIzHRg6ryOGcGXlpbxOhKMrwRp8IA8BeDPqZiV7+99ASwmPra5v8/HFlYuVwfAETkRBgpxDftLCEWQn2yjlyXI4jBXpHe4DLXq93ZNjNLvFwx5Glca0pRVGUTKJPnyQxxvCktcDz9Kle9inL4mvWGpk7VrYmFAv6qK6b5u6+40Z7XTz6mVGcPT2XBaOy+OORpZw1LbH1NwA5bolYE/SyZf2cuVeupiQrijKoqAAlybLabja29LnfvG56A+s/3r+I8FDKphY/nzT57EtEUGW53w6vyCY/y8UdR5bx6mlj+FKcqgKx+GY/ZWXOTELQFEVRMsGwTsM2xvDerm5WNQTbBbhEOGB0FjPT0NY5Frb77fgJ3t64THmum8MrchzWxktbOqksjj0en4EXNjkTFhaOzYlxdOLMKc3iyHE5vLYtMra03+istHb+VBRFGQjD+in02w9buPa9Jsc2l8B9x5RFXYWfKsaYCAEKry0GcMwEpwC9sqUjZpHLTS0+Lvsohw+anPGfdAgQwDf3zo8qQLZ7TlEUZTAYti649c0+rv+gKWJ7wMB/vdGQcCuCZPikyce6sOrQ2S440Up5Ps6qgvD69i46o2TDPbG+nUVP1PBBkzP9eUKe29HQLRVOmuRlckFkevUXpqoAKYoy+AxbAfrl+010x9CYmvZAbzfPZGntDnDdv5v4rzcaWN3g7AT6r81Oa2Lh2JyILLK9SzyMy+vb1uYzvBWW4dbmC/DdJfWc/0pdbwmfHsbkurj76PQtCHW7hEusCtmHVWQzsWBYG76KouwhDEsBWl7XzcNrna6wOVaV49uWt/BpAgkANpdX1XPjh83ctaqVY57ayXOb+u5jN1WLtqpfRDhmvHP7y6EWCsYYLnyljnvXRDZ/O35CDktOH8MhFelxv/Vwwaz83vdGgB/tV5TW6yuKogyUYSlA177X5OjSOafEw3OnlDssj64A/OSdxqSu+3FdN0+EVTho8xnOe6mOP61sobU7ELFY9ISJ0cXiWKsZ20tbguct2dHF85YV5RHDdQcV8fcTRlGem/5qBIVZLp47pZwHjyvj/bMqOHJcegVOURRloAxJAeryG97f1UVDZ6SP7a0dnTxnZY395IAiirJd/OyAYsf2pzd28O7OyAKfsbjl40i3XcDAlW82cuGrdY5W2FML3cyIkUl2zPgcwp1oH9V1U9Pu59UtTvHZq9DNPfM7+Na8wrTXYQunKNvFZyfnMrVQXW+KogwdhpwANXQGWPREDcc8tZNZD23jP6rq+aiuLxbz6w+cInFweTYnhRIBvjQ919H/BeCuVa0J3Xdzi49H1sXuIPqCZbmcMCF2V89RXndE6+qXt3Ty6jancF61XxGzC5IrWqooirKnMOQE6LcfNrOmMRi76fTD/dVtHPFEDVe+0cDbNZ0RK/p/ckBfk7Fg9WVnjOOxT9uoj2JJ2dy+wlndelyeizxPbKukv6rOx1luuPvWtPLeLmdSw1HqDlMUZQQzpASotsMf02L506pWvvSvWse2hWOzOcJ6iB87PseRetzhhwc/iQz6h9PQGeC+1c77/mi/In57WEnU43PcsGhc/K6e9jqkN3Z0EbBqsY3VNtCKooxghpQA3ba8JW5PnAYrbfkH8yMzutwu4YJZztTje1bHr8n2mw+baQm7b0Wuiy9Ny+Ps6bmcOiXS0lk0Noc8T/y3bv6oLGYVx465qPWjKMpIZ0gJ0B9XOq2Qc2fkUe6NPsRDxmRzZAwr5Msz8gj3nlU3+liyI3oywoe1Xdy23Fm1+ptzCvB6BBHhd4eXOBq8QbD8Tn+ICGfPiF1v7ejxKkCKooxshpQA2RWhbzy0mL8dP4povdJ+sKAwZhJARZ6bz1kusLtDrr0dbX7OemEXBz6ygx+91cB3ljQ42hRMzHfzjbDFm6O9bv5wRGnvGMbluTivMrFCnl+cFr3igFvSV25HURRluDKkBCicb88rID/LxQHl2fzxyDJHWvNB5Vkc248FcaHlhntyfTubW3x8e0k9L27p5JMmH7evaOWDWmdiwI2HFUc0fTt2gpclp1dw3zFlvPmFCooT7KEzqcDDorGRVtqB5dnah0dRlBHPkHwKluYIF83uE5DTpuby52PLmFcWFJ4/Hzuq3142R47LZmZYDMZn4FtLGiLSqcM5bYqXkyZFt1qmF3s4fWpuwuLTw9lRWikcpe43RVGUoSdAHoGbF5ZSaFkhp07Jper0MTx64mjGJZA9JiL8x1xnFepXt8YWn8Is4fpDSgY05nicNjU3woV4tCYgKIqiDC0BKswSHj5hVNpaKZw9PS8igSCc6UVBZQiKXgnj89OfFl2c7eLrs/uEcJ+yLA4eEz+FW1EUZSSQkACJyLdE5F0R6RSRe619x4nIKhFpE5FXRGRK2L4cEblbRJpEZLuIXBHvPs+eXM4xCWSYJYrXI3xz7+i9eA4qz+LdMyr44KwK1pwzli/slbkOodccWMRNh5dw9f7Bmm8el7bCVhRFSdQC2gpcB9wdvlFERgOPAj8ByoB3gYfCDrkGqASmAMcAPxCRk2LdZF5Z+juZfn12PvlRKhr8YEGwgsLUQg9l0dLs0kiWSzh/Vj5Xzi9MyH2oKIoyEkhIgIwxjxpjHgdqrV1nAMuNMQ8bYzoICs58EZkd2n8+cK0xpt4YsxK4E7ggHQNPlJIcF1+b6bRu9hudxfETNA6jKIoymKQaA5oLLOt5YYxpBdYCc0WkFBgXvj/0+9wU75k0392nkPGhVg3ZLvjlwcX9ZtEpiqIomSXV+vwFwE5rWyNQGNrX89reF5Xq6uoUhxObe+bBu41uZhcEGN20kerIbt6DRibnPdQZyXOHkT1/nfueT2VlZdz9qQpQC2AXZCsCmkP7el53WPui0t9gU+WQjF59YFRXV2d83kOVkTx3GNnz17mPzLnbpOqCWw7M73khIvnAdIJxoXpgW/j+0O/LU7ynoiiKsgeQaBq2R0S8gBtwi4hXRDzAY8A8ETkztP+nwIfGmFWhU/8MXC0ipaHEhEuAe9M+C0VRFGXYkagFdDXQDlwFfCX0+9XGmJ3AmcAvgHqCXq5zws77GcGkhA3AYuAGY8xz6Rm6oiiKMpxJKAZkjLmGYIp1tH0vArNj7OsELgr9KIqiKEovQ6oUj6IoijJykHidQncHjY2NgzsARVEUJeMUFxdHLL5UC0hRFEUZFFSAFEVRlEFh0F1wiqIoyshELSBFURRlUFABUhRFUQaFtApQP43rLhaRT0SkRUSeE5HxUc7PFpGVIrLZ2n6qiHwcOnepiMxJ57jTxUDnLyLXiEh3aF/Pz7Sw/X8UkdUiEhCRC3bfjBInE3MXkdEiskREakWkQUTeEJGFu3lq/ZLBv7sRkdawfX/ajdNKiAz93Y+wtreE3oszd/P04pLBv/uweN6lg3RbQLEa1x0N/BI4nWDjuk+BB6Oc/32s6toiUgncD1wKlABPAU+GSgENNVKZ/0PGmIKwn3Vh+5YBlwPvZWjc6SATc28huIi5HCgFfg08NQT/9pn6uwPMD9t3cUZGnxppn7sx5vXw7cDnCH4WhloVlbTPfZg971ImrQIUp3Hd54CHjTHLjTFdwLXAkSIyvecAEdmLYJmfX1nnngi8boypMsb4CD6EJgBHpXPs6SCV+fdz3VuNMS/RV1V8yJGJuRtjOowxq40xAUAAP0EhKkvv6FMjU3/34cBumvv5wD9C/caGDBma+7B53qWD3RkDkii/zwvbdgvw3wTrzPV3rljnDgf6m/+pIlInIstF5LLdOK7dQUpzF5EPCYrvk8CfjDE1mRtq2kn17/6aiGwXkUdFZGrGRpkZUv7MS7DC/lnAfRkaY6ZIZe57wvMuIXaXAD0HfElE9hWRXIJVsw2QByAiXwDcxpjHopz7InCUiBwtItkERSq759xhQtz5A38H9iboaroE+KmInDsoI00/Kc/dGLMvwV5S5wFVu2vgaSDVuR8FTCVYa3Er8PQwcsWk6zN/BrCLYDHj4UIqc98TnncJs1sEKFSw9GfAI8D60E8zsDn0Ded/ge/EOHcVQRP89wT7C40GVgCbox0/FIk3/9D+FcaYrcYYvzFmKXATwW99w550zT3kjnsQuEpE5tv7hyKpzt0Y85oxpssY0wD8J7AXwQfXkCeNn/nzgT+bYbRgMZW57wnPu2TYbS64UByj0hhTQfAP4wE+BioJfst7XUS2A48C40Juh6mhc/9hjJlnjBlF8A87FXhnd409HcSZf9TDcZrhw5o0zz0LmBZn/5AizXMfVp+LVOcuIpOAown2FRtWpDL3PeF5lyjpTsOO2rgu9O88CTIZ+CNwkwl2Tf0YmAQsCP1cDOwI/b4pdN0DRMQtIuWhc580fU3vhgwDnD8icroEm/aJiBxM0Bp8Iuy62aHrCpAVut6QWsOVibmLyKEisig0/1wR+SFQAbw1OLOMTobmPldEFoQ+9wXAb4AtwMpBmWQMMvWZD/FVYKkxZu3unFOiZPD/+7B43qUFY0zafgj2DDLWzzUE0wk/BFqB7QQz3dwxrnE0sNnaVkXQhK0D7gDy0znuwZ4/wRTNWoKppquA71jXfTXKdY8e7Plmeu4EYyDLwv72i4EjB3uuu2nuxwKrQ+fWAI8DlYM91931mQ8dswr4+mDPcXfPnWHyvEvHj9aCUxRFUQaFIeXGURRFUUYOKkCKoijKoKACpCiKogwKKkCKoijKoKACpCiKogwKKkCKoijKoKACpCjDBBF5VUR+P9jjUJR0oQKkKP0gIvdKsCHaXVH2/Tq07+k03u/o0DVHp+uaijIUUQFSlMTYRLDCcX7PBglWpv4asHHQRqUowxgVIEVJjA+BauBLYdtOIdin6NWeDSLiEpGfiMgmCbZq/khETg/bPzVk3ZwpIv8SkTYRWSEiJ/TsB14JHb4zdOy9Yfd0icgvRWSXiNSIyI1DrS6goiSKfnAVJXHuItgivIeLgHsI1gDr4T8Jtpb/IbAP8BjwqIgssK71C+BmYD7BSsd/CxUd3QScGTpmLjAudM0evgz4gMOBbwHfBc5ObVqKMjioAClK4jwAHCgilSIyFjgJuNc65krgRmPMA8aYNcaYnwKvh7aH8ztjzFPGmGqCTcfKgAXGGD/BIpQANcaY7caYxrDzVhhjfhq69t8JWkvHpXWWirKbGC7dFRVl0DHG1IvIYwQtnwbgVWPMRpFgKxcRKQLGA0usU6uAk61tH4b9vjX075gEhvGh9XprgucpypBDBUhRkuNu4D6CpfR/msR5dtn57t4dxpiQiCXikei2XpsEz1OUIYd+cBUlOV4Cugi2Sn48fIcxpomgRbLQOmcRwbbKidIV+tc9sCEqyvBALSBFSYKQtbIvIMaYziiH3AD8XESqgX8DXwGOAPZP4jYbCFo2p4jIU0C7MaYlxaErypBDBUhRksQY0xxn981AIfC/BNuHrwbONMYsS+L6W0TkZwQz5f4E/Bm4YMADVpQhinZEVRRFUQYFjQEpiqIog4IKkKIoijIoqAApiqIog4IKkKIoijIoqAApiqIog4IKkKIoijIoqAApiqIog4IKkKIoijIoqAApiqIog8L/A9qMJTlYAcmlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(); # non-stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "df2.plot(); #stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=2650.760, Time=0.04 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=2565.234, Time=0.08 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=2463.584, Time=0.13 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=2648.768, Time=0.02 sec\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=2460.154, Time=0.26 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=2461.271, Time=0.32 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.75 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=2460.722, Time=0.30 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=2536.154, Time=0.16 sec\n",
      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=2462.908, Time=0.94 sec\n",
      " ARIMA(1,1,1)(0,0,0)[0]             : AIC=2459.074, Time=0.09 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0]             : AIC=2462.221, Time=0.05 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0]             : AIC=2563.261, Time=0.04 sec\n",
      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=2460.367, Time=0.16 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=2460.427, Time=0.31 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0]             : AIC=2459.571, Time=0.11 sec\n",
      " ARIMA(2,1,0)(0,0,0)[0]             : AIC=2534.205, Time=0.09 sec\n",
      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=2462.366, Time=0.68 sec\n",
      "\n",
      "Best model:  ARIMA(1,1,1)(0,0,0)[0]          \n",
      "Total fit time: 4.528 seconds\n"
     ]
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(df2['Births'],start_p = 0,start_q = 0,max_p=6,max_q = 3,seasonal=False,trace=True)\n",
    "# stationary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>365</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)</td> <th>  Log Likelihood     </th> <td>-1226.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 10 Apr 2021</td> <th>  AIC                </th> <td>2459.074</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>10:36:46</td>     <th>  BIC                </th> <td>2470.766</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>2463.721</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 365</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>    0.1252</td> <td>    0.060</td> <td>    2.097</td> <td> 0.036</td> <td>    0.008</td> <td>    0.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9624</td> <td>    0.017</td> <td>  -56.429</td> <td> 0.000</td> <td>   -0.996</td> <td>   -0.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   49.1512</td> <td>    3.250</td> <td>   15.122</td> <td> 0.000</td> <td>   42.781</td> <td>   55.522</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.04</td> <th>  Jarque-Bera (JB):  </th> <td>25.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.84</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.96</td> <th>  Skew:              </th> <td>0.57</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.81</td> <th>  Kurtosis:          </th> <td>3.60</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:                  365\n",
       "Model:               SARIMAX(1, 1, 1)   Log Likelihood               -1226.537\n",
       "Date:                Sat, 10 Apr 2021   AIC                           2459.074\n",
       "Time:                        10:36:46   BIC                           2470.766\n",
       "Sample:                             0   HQIC                          2463.721\n",
       "                                - 365                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.1252      0.060      2.097      0.036       0.008       0.242\n",
       "ma.L1         -0.9624      0.017    -56.429      0.000      -0.996      -0.929\n",
       "sigma2        49.1512      3.250     15.122      0.000      42.781      55.522\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):                25.33\n",
       "Prob(Q):                              0.84   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.96   Skew:                             0.57\n",
       "Prob(H) (two-sided):                  0.81   Kurtosis:                         3.60\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(0,1,0)(1,1,1)[12]             : AIC=1032.128, Time=0.28 sec\n",
      " ARIMA(0,1,0)(0,1,0)[12]             : AIC=1031.508, Time=0.03 sec\n",
      " ARIMA(1,1,0)(1,1,0)[12]             : AIC=1020.393, Time=0.14 sec\n",
      " ARIMA(0,1,1)(0,1,1)[12]             : AIC=1021.003, Time=0.20 sec\n",
      " ARIMA(1,1,0)(0,1,0)[12]             : AIC=1020.393, Time=0.04 sec\n",
      " ARIMA(1,1,0)(2,1,0)[12]             : AIC=1019.239, Time=0.48 sec\n",
      " ARIMA(1,1,0)(2,1,1)[12]             : AIC=inf, Time=2.73 sec\n",
      " ARIMA(1,1,0)(1,1,1)[12]             : AIC=1020.493, Time=0.45 sec\n",
      " ARIMA(0,1,0)(2,1,0)[12]             : AIC=1032.120, Time=0.27 sec\n",
      " ARIMA(2,1,0)(2,1,0)[12]             : AIC=1021.120, Time=0.58 sec\n",
      " ARIMA(1,1,1)(2,1,0)[12]             : AIC=1021.032, Time=0.69 sec\n",
      " ARIMA(0,1,1)(2,1,0)[12]             : AIC=1019.178, Time=0.40 sec\n",
      " ARIMA(0,1,1)(1,1,0)[12]             : AIC=1020.425, Time=0.15 sec\n",
      " ARIMA(0,1,1)(2,1,1)[12]             : AIC=inf, Time=2.20 sec\n",
      " ARIMA(0,1,1)(1,1,1)[12]             : AIC=1020.327, Time=0.47 sec\n",
      " ARIMA(0,1,2)(2,1,0)[12]             : AIC=1021.148, Time=0.43 sec\n",
      " ARIMA(1,1,2)(2,1,0)[12]             : AIC=1022.805, Time=0.78 sec\n",
      " ARIMA(0,1,1)(2,1,0)[12] intercept   : AIC=1021.017, Time=0.71 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,1)(2,1,0)[12]          \n",
      "Total fit time: 11.331 seconds\n"
     ]
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(df1['Thousands of Passengers'],start_p = 0,start_q =0 ,max_p=4,max_q = 4,seasonal=True,trace=True,m=12)\n",
    "# non -stationary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>144</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(2, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-505.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Sat, 10 Apr 2021</td>         <th>  AIC                </th> <td>1019.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>10:42:08</td>             <th>  BIC                </th> <td>1030.679</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>                <th>  HQIC               </th> <td>1023.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td> - 144</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>ma.L1</th>    <td>   -0.3634</td> <td>    0.074</td> <td>   -4.945</td> <td> 0.000</td> <td>   -0.508</td> <td>   -0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.1239</td> <td>    0.090</td> <td>   -1.372</td> <td> 0.170</td> <td>   -0.301</td> <td>    0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.1911</td> <td>    0.107</td> <td>    1.783</td> <td> 0.075</td> <td>   -0.019</td> <td>    0.401</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>  130.4480</td> <td>   15.527</td> <td>    8.402</td> <td> 0.000</td> <td>  100.016</td> <td>  160.880</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>4.59</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.92</td> <th>  Prob(JB):          </th> <td>0.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.70</td> <th>  Skew:              </th> <td>0.15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>3.87</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:                  144\n",
       "Model:             SARIMAX(0, 1, 1)x(2, 1, [], 12)   Log Likelihood                -505.589\n",
       "Date:                             Sat, 10 Apr 2021   AIC                           1019.178\n",
       "Time:                                     10:42:08   BIC                           1030.679\n",
       "Sample:                                          0   HQIC                          1023.851\n",
       "                                             - 144                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.3634      0.074     -4.945      0.000      -0.508      -0.219\n",
       "ar.S.L12      -0.1239      0.090     -1.372      0.170      -0.301       0.053\n",
       "ar.S.L24       0.1911      0.107      1.783      0.075      -0.019       0.401\n",
       "sigma2       130.4480     15.527      8.402      0.000     100.016     160.880\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                 4.59\n",
       "Prob(Q):                              0.92   Prob(JB):                         0.10\n",
       "Heteroskedasticity (H):               2.70   Skew:                             0.15\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.87\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stepwise_fit.plot_diagnostics(figsize=(12,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}