TS-Sample.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO9XHvLIvl+0HNKAwz+GXAo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/TS-Sample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --no-deps statsmodels\n",
        "!pip install tbats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3IwgLmJyK4C",
        "outputId": "29a29ac2-d336-49ca-a37f-7ac110bc3bfa"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Collecting statsmodels\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: statsmodels\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed statsmodels-0.13.2\n",
            "Collecting tbats\n",
            "  Downloading tbats-1.1.0-py3-none-any.whl (43 kB)\n",
            "\u001b[K     |████████████████████████████████| 43 kB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from tbats) (1.0.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.4.1)\n",
            "Collecting pmdarima\n",
            "  Downloading pmdarima-1.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 16.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (0.29.28)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (57.4.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.1.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.3.5)\n",
            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (0.13.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima->tbats) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->tbats) (3.1.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (21.3)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (0.5.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (3.0.8)\n",
            "Installing collected packages: pmdarima, tbats\n",
            "Successfully installed pmdarima-1.8.5 tbats-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot\n",
        "ap1=pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv')\n",
        "ap1=ap1.set_index('Month')\n",
        "ap1.plot()\n",
        "pyplot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "FyDGxIvGA9d6",
        "outputId": "bdf079b2-2cc1-4c02-de15-dd3200ffed3e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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dGTP9gSD+YChcV8bCqvmSrOC29frJTEsh3UyzjEZ5Xjoef9DoszriIjNZyApVQRCmlJYeHwClOa6hmi82eu4j68pYZLudhDR4/MGkxm/3DIyZKWNh5bqfzLi7iLsgCFNKi7lysyTbPdQhyUbPvdMzvCKkhVX2N9lzjVd6wMJKhxRxFwThjKE5wnPPcpmCa6Pn3jWO5w7JT9629Y5dNMxi5hQsZBJxFwRhSmk1PffibPfQhKqdnrs3uuduxfeTrQwZT1imKNNFmtNxUsU93kVMgiAIk0Jzjw93qoMctxOtQSl7UyGHioaNznOH5M7V0Oml3eOnLNc97n4Oh2JmrpsG8dwFQTiV6PL6ee2QkcpnNy29A5Rku1FK4XAostKctk6oNvf4SHEoCjOHe9c5NjTs+NnLNaQ6HHx0TUXMfU92rrt47oIgjElLj49vPbGbl/a3EAhq/vC5c3jfvCJbz9Hc46M0Z0h4s2yq+WLR1O2jNNs1qpxBsn1U6zu8PLq1gZvPmRWeMB2P8rx0Xj148upoiecuCMKYPLe3mef2NHPVcqOwa1OXz/ZzWJ67RZbL3p6jzT0+SqOETZKdUP2vlw7hcCi+eOH8uPafmZdOS+8AA4PJpV7Gi4i7IAhjUt/hJc3p4K5rlwFDqzHtpKVngJIIz93uhtJN3b6oMfGMtBRcTke4qmMitPcN8Kftjdx8zixKc8aPt1uUm959c/fJadoh4i4IwpjUd3ipyE8ny+XEneqwXdw9A4P0DQwO99xt7DmqteZEty+qACulKMlxhVMxE+Fwq4dgSHPhopK4jynKNrJ12j0i7oIgTDH1nV4q8zNQSlGU5aK9L3EvdzysBUyRMfdsG8MyvQODeP3BMbNZSrPdYRsSoa7DC8Csgoy4jykwJ3St7J3JRsRdEIQxqWv3UllghBMKs1y02uy5W6UHRsbc7UqFbO62FkhFF/eJeu51HV4cirgmUi0KzDx7uy+QYyHiLghCVLr7A/T4BsPeaXFWWri8rV00R/Hc7cyWaTLFvSw3ugiXTNBzr+/wUpabTpozfgm18uzFcxcEYUqpN0MPlWaNdSMsc3I8d48/SDCUfE79CXP8GeN47r2+QfoTLB5W1+FNKCQDxutKTVF0eOytVT8WIu6CIESlodMUd1PECrPSaPf4CdkguhYtvQO4nA5y0oeW3Fgpih5/8t67FZaJzMaJxLqotPQmFpqZiLgrpcjPSAsXMptsRNwFQYhKfYexmjLScw+GNF399nmeLT0+SnJcw+qp21nTvanHR0FmGu7U6LXWrXBQc0/8v0j6/UFaewfCcxGJUJCZRoeEZQRBmErqOrzkuJ3h5hJW5UM7QzPNPQOUZg8PmYRL8dqQMdPc7RszJAMT89zrR/yiSQTx3AVBmHLqO73DBKzQrFluZ8ZMS69vVMgky6ZSvGBMqM4Yp6jXRDz3uvbE0yAtxHMXBGHKqe/whkMyAMWm525nxszI0gMwFJaxYyFTc8/44p6bnkqa05GQ5z6RHHeLgswhz11rjS8weaUIRNwFQRhFKKRp6OwfFle2wjJtE0gdjIZ/MESvb5DCzNG9TSH5sMzAYJB2j3/csIxSipJsFy2JeO4dXjLTUijIHL/7UjTyM9Po6g8QDGm6+wMs/taz/PbN2oTHiQepCikIwiha+wYYGAwN805z01NJcSjbls939Vt11qM30Uh2QtUS7PE8d8AQ90Ri7h1GuGoiTbULMlLR2lhDYGUjlcRZmyZRxHMXBGEUVo57RYS4OxyKwsw02nrtCct0mvneIz3gLJs8d2sB03ieOxirVxOKuU8gDdLCupB1ePxJhXfiQcRdEIRRhDNC8ocLT1GWy7biYR3hxtXDOyRlptkTc7cWMMXqklSSHX8JAq11UuJuXcg6vUPiPpGsm3gQcRcEYRSNnUaOe0X+8Fzuwqw02mxK5bOW4Y/03FMcypaa7uG6MrHEPccd9yrV1l4zXFU4Qc89Y8hzr+/wUpiZFg5D2Y2IuyAIo2jr85Ptdo5a/FOc5bJtQtXy3AsyRk9M2lE87Eibh2y3k+wY4lmSbUwUR4u7+wJBjrZ5wo+PJeltF4wIy0yW1w4i7oIgRKGtbyCcHRNJUbYRlrGjl2pnOCwTRdxtaNix7VgHq2flx5z4tCpGRisg9q9/2ctV9/4tnLK4p7EbgMUzsidk00hxn6x4O4i4C8K05Z7nD/DXd5smZewOjz9qql9hZhoDgyE8CRbaikanN0C2yxm1sqLhuU9c3Lu9AQ4297F2dn7MfUvCC5mGe+4tvT4e29pAfyDIblPU32vsoSgrLeYk7Vi4U1PISEuhtXeA412+SRV3SYUUhGlIMKT5xatHcDhgcVk284qzbB2/w+OPGjKIzHVPNlbc6fWPSoO0SLbV3ra6DgDWVhXE3NcqfzAy1/1/Xq8lEAoBsLO+i7VVBexu7GZZee6E0iAt8jPS2HO8m2BIi+cuCMJwjnf14w+G8AVCfO3hnQSCIVvHb+vzU5Q1WniLsq1VqsnH3Ts8fvJHZMpYZLkSr+mutQ6XCX6nthOnQ1FdmRfzuLyMVNJSHDRHxNx7fQF+99YxrlpWRnleOjvqu+j3BznU0svy8tyE7BpJQWYa75m/BE6JmLtSKkUptUMp9Rfz8Ryl1NtKqRql1MNKqTRzu8t8XGM+XzU5pgvCmcsxs77Jp95XxbsN3fzilcO2jR0KaTq9Y4dlwJ4SBON57hPJlvn8b7fxhd9tA2BbbSdnleeSnha9GmQkVi/VE91D4v7HLfX0+gb5wvvnUV2Zx866LvY29RDSsCxJcc/PTMMXMC7GE826iYdEPPevAvsiHv8Q+LHWej7QCWw0t28EOs3tPzb3EwTBRo62GxkcX3j/PNZVFbBpf4ttY/f4jOXxVs/PSIpNz92O4mEdHn/UTBmAnPRUuryBhCZu953o4YW9zbywt5mdDV1xxdstyvPSw+mfAG8eaWdRaTbLK3KprsyjsaufVw4Y73HSnrv5ayU1RU04dh8PcYm7UqoC+CDwK/OxAi4CHjN3eRC41rx/jfkY8/mLVTIBKkEQRlHb5sGd6qAk28XswgyauvtjHxQnllc+suaLtU0pI987WTo9Y3vuM/PS6Q8E6fTGnw5p9Sb9+qO78A+GOLsqfnGvyM+gsWvoPazv8DLb9KqrZxmhnT++U09hZlrMRVGxsF5zRX4GKY7Jk8Z4PfefAN8ArMBeIdCltbZ+NzUA5eb9cqAewHy+29x/GEqpW5VSW5VSW1tbWydoviCcmRxr91BVmInDoSjLS6eld8C2uLuVf14YJebuTHFQmOkKt8ebKAODQTz+4JjFt6zFU5He9Hh4/YN4/UGqK/PoNpuJrJkdezI18nwnenz4B0NorYeVO142M5cUh6K1dyDpyVQYyuufzHg7xCHuSqmrgRat9TY7T6y1vk9rvVZrvba4uNjOoQXhtOdomyfsWc7MdaP16FS+idJhFgYbS3iNQlvJee5dpkeeP0ZYxhJ3q7hWLCyv/e/WzWLdnAIWlmaFQ0jxUJ6fjtbQ1N1Pa98AvkCIStOG9LSUcF57siEZGPLcZ02gk1MixJPLtAH4sFLqKsAN5AD3AnlKKafpnVcAjeb+jUAl0KCUcgK5QLvtlgvCGUowpKnv6OeSpaUAlOUZItHU7aMiP3lvsN3y3KPE3MHIC0+05+hIwqtTM6Nny1TkGa+jIU7P3creKcpO49efOjvhOumRvxRc5qrcyMnO6so89hzvSXoyFYbCXZOZBglxeO5a63/WWldorauAG4GXtNY3Ay8DHzV3uwV4wrz/pPkY8/mXtB3L2QRBAIbSIKsKM4GhwljHu+yJu1te8LieewJVFKMx3upUgJx0o2xAY5yvybK5KMtFlssZdXXteFgF0ho6+4cag0dcKM9fUIzL6WD1rNiplbEoNG2bbHFPZhXC7cAflVJ3ATuA+83t9wO/VUrVAB0YFwRBEGyi1syUGSnukal8ydDhMerKRFs5Ckbf0ba+AYIhPeEJwY4xioZZKKUoz0+POyxjee6FCYq6xYxcNw5lhIGs1x35K+jys0rZ9q1LbSnytWZ2Pnddu4yLFpcmPdZ4JGSp1voV4BXz/hFgXZR9fMANNtgmCEIUas0c9zlFhrhnu1PJdjnD9cuTpd3jj5opY1GS4yKkod0zukVevFie+1gxdzDENe6Yu2fsDJ94SE1xMCPHTUNnP6kpDoqyXMNy5JVStlVvTHEo/v7c2baMNR6yQlUQphmRaZAWZXluG8MyA+O2kAtXUUwiNGOlOI6s5R5JRX46DZ39ceW6t/UNkO0aXcUyESryM2jo6jcLek3uZOfJQMRdEKYZtW1DaZAWZbnptnnuHR7/uOGNYtNbTybXvcPjJ8ftJDVlbAmqyE+nb2CQnv7YK1Xb+/xRUzcToSLfWMgUmQY5nRFxF4RpRm37UBqkxcw8t20LmWKGZcapfx4vY5U3iMTKYKmPIzTT1jcw4Xi7RXl+Ok3d/TR1+0Z1oJqOiLgLwjTCSoO0JlMtZuSk09bnZ2AwuVK8oZCmc4xyvxbFNoRlOjz+MTNlLCry40+HbB+j0FkiVOSnE9LGe1wpYRlBEE4m7X0D+IOhUe3vyvLsyZjp8QUYDOlxvWB3agq56alJLWSKx3MvN/P340mHbPck77lHZseI5y4IwknFEtTiEVkqM3MNITzelZy4x5t1YqxSTSIs4wmMmykDxmRrZlpKzIyZYEjT4fFTNMFMGQvrYgKTXxrgZCDiLgiTwGSt27NKDJTmDPdSw557T3Jx96GVozHEPSe5EgSG5z52pgwY6YdGOuT4r6nT6yekh2rNT5SyPDdKGamKyRYHOxUQcRcEm3nmvSbO+d6mcAErO7EEtSRnkjz3vvHryliUZLvjjrmHQpoDJ3rDj32BIF5/cMyKkJFY6ZDj0d43frmEeHE5UyjNdjMzz41znCye6cL0fwWCcIqxs76Llt4BXtrfbPvYludePCK+nJ6WQl5GatIZM1ZYJtby/ZJsF6298TXKfnFfM5f/ZDNbjhqt7946YpSamh9Ha8Dy/HQaxwjLPLatgZYeX8Tq1OTCMmC0LFwyIyfpcU4FRNwFwWaOm5OaT793wvaxW3qNBUbRSgOU5abTlKTn3mF6wfkxQiYlOW78wVC4uuN4HGrpA+APbx8DDFHOy0jl/YtiV4OtyE+nxzdIj2/4eWrbPHz90V387OWaoaJhSU6oAvzXTau45+PVSY9zKiDiLgg202Rmd7x6sDWpJs/RaOnxDVuZGklZrjt8YYmXT//PFu58ck/4cau50tPlHH+l51Cue+zQjBVWeXr3CeravTy/t5lrVs6MeQ4wmnYAoy5a1q+AF/Y2h5uLJJsKCUYpB7vKDEw1Iu6CYDNN3T5mF2bgHwzxko3t78AQ05Hxdouy3MQWMmmteftoBw+8Uctze05wsLmXx7Y1hDsPjUciC5kaOr0UZKbhHwzxxT9swz8Y4vo1FXHZWBaeSxj+ut42xf14t4/NB1txOhQ57vF/bZxpiLgLgo0EQ5oTPT6uWl5GSbaLZ95rsnX85h4fpWN47jPz0unyBuj3x7eQqW/A6F6kFNzxp3e59TdbyXQ5+dENK2Mea11g4plUbejsZ/28Qqor89jd2MOCkqy4m17MNLOAjo+4aG2pbefsqnwcCjYfaqUwK21YOQZBxF0QbKWl10cwpCnPS+eKZTN4+UALXr89oZlgSNPW56ckZ+ywDBC3995sCvOXL5yP1x+kobOfn9+8mtI4mjbHG5YJhTSNnf1U5Kfzd+tmAXD9moq4W9WVZLtJcahhYZnjXf3Ud/RzxbIy1s4uQOvkM2VOR06P4JIgnCJYqYjleemU56fzmzePsau+m/XzRrURTph2j1FDfSzxtUIYTd0+5saRiWL1QX3fvCLOmVNISGvWVsXXdzTT5TTLDI9/IWnpNVbUVuZncM2qmXT3B7jpnFlxnQOMnPPSbNcwz/2dWiMkc86cAkIhzZbaDlsyZU43RNwFwUYssSvLc5NtxoAPt/bZIu5WCGSsCdVwCCPO0r/NvUMLouK5GIyksiCD+o7xV49aRb8q8tNxOVP43AVzEz5PWd7wLKC3j3aQ5XKypCyHLJeTf39636jUUEHCMoJgK5YIleWmMwIU0JEAACAASURBVDPXTUZaCjVmKmCyWJOXY02oWh59vKV/rbDMWOPFYlZBBnUjxN3rH+Q7T+zmaw/vBIYaXCfT23XkRPGWox2srconxaGoKsrkpnWV4X6ywhDiuQuCjRzv7iczLYUctxOlFPOKszjcapO4x/Dc3akpFGamJRBz95Hlck449W9WYQYvHWghFNI4HIqDzb38w++2cbjVaAN4x5WLqe8wbBlZ6CwRyvPSeX5vM1obNWRqWvq4bnV5+PnvX7diwmOfzojnLgg20tTloywvPTxhOL8kyzbP3fK0i8epoWJ0ZIrPc2/pGRhzcjYeKguMdE9rUvXOJ/fQ6Q3wzasWA8ZK1IZOL8XZrqQ6JJXluvEPhmj3+NlZ3wXAmln5Ex7vTEHEXRBspKm7f1jRqfklWTR1+2xZzNTS66MgM23cxT9GR6b4PffSCfZABSMsA1DX4UVrzZ7jPVyxbAYbz5tLjtvJm4fbqe/opzIJrx2MmDsYF85dDd04FCyviC+V8kxGxF0QbKSxyzesdOy8YqOpxhEbQjPNPQNjhmQsZua644+59/qS8twjxf1Ej4/u/gBLZmST4lCsm1PIm0faaejyJhVvh4iiaN39vNvQxYKSbDLSJKIcCxF3QbCJgcEgbX0D4ZREMDx3wJbQTGuvL+bkZ1leOr2+wZi/FLTWNPcMxJXTPhbleekoZYj7vqYeABaXGUW31s8r5Fi7l4bO/qS7GlnljJu6+nm3oZsV4rXHhYi7INhEc7cRe7bECGB2YSZOh7JF3OPx3MMLmWKkQ3b3B/APhmKONx5pTgczc9Op7/Cyr8ko6btoRjYA6+caqZ9aJ5cpA0bjkDSng3eOddLh8Yu4x4mIuyDYhLXQZmaE556a4mB2YUbS4h4KaVr7BkY16RhJuBZLjNCMNTmbjOcOUFmQTl2Hl/0neinPSw/Xd1k8I5u8DON+MpkyYDTtmJnr5mWzTs+Kiti1bwQRd0GwjcgFTJFMNB1yMBgK32/rM1anlsSYAI3Xcx/q6JScuM8yFzLtb+phSVl2eLvDoThnjrHa1Y5+pGW56Xj9QVJTFIsjziOMjYi7INiElYIY6bmDEXc/1u4lECHWsXh29wmWfPtZ/u+rh+ny+vnyQzsAWFY+fiOJGblGq7honrvWmreOtOMfDI3Zri9RZhVk0NI7wJE2D4tHNLm4prqcBSVZ4bK9yWBdMJeU5cRVKliQRUyCYBvHu/rJy0glPW24+MwvyWIwpDnW7mF+SXxe5/a6TgJBzfef2c+9mw4RCIa498Zq1swev/ZLaoqD4ixXVM/9md0n+OLvt3P7FYsJmR2UYv0SiIXVSDoY0qM86quWl3HV8rKkxrewLpgSb48f8dwFwSYau/qjhiAmkjFT2+ZhfkkWP7x+OZX5GTzw6XVcU10e+0DMWiwjPPdeX4DvPmU05fjjO3Wc6PaR43aOuhAlipUOCYzy3O3E8twl3h4/4rkLgk00dPZH7Qs6p8jIdT/aNn6RrUiOtXupKszg42fP4uNnx19FEYxc94PNvcO23f38QVp6B/jMhjn8+vWjPDNwIul4OwyJu8vpoKow+dj6WKysyCPH7Qxn4Qixiem5K6XcSqktSqldSqk9SqnvmtvnKKXeVkrVKKUeVkqlmdtd5uMa8/mqyX0JgjD1aK1p6PRGzQzJdqdSnO3iaFt8nrvWmmMdHmYXZk7IFmOVqi/cvPpIax+/ebOWvz9nNt+4YhH5Gam09SWX425RkJlGZloKC0uzcaZMXiBgWXku7955eTgMJMQmnk9jALhIa70SqAauUEqdC/wQ+LHWej7QCWw0998IdJrbf2zuJwinBO/UdtDp8ds+brvHjy8QGjPtb05RJkfbPHGN1dI7gC8QmrAnPLswA68/GK75su1YJyENn9pQhTs1hetXGy3uklmdaqGU4rKzZnDFshlJjyXYS0xx1waWy5Fq3jRwEfCYuf1B4Frz/jXmY8znL1bxtl0RhElkYDDIzb98my/9YXvYq7ULqwn0WAt25iYg7rXmfrMm6LmPjPHXtPaRluJgtun13mh2RIqsgZMMP/54NV+6cL4tYwn2EdfvKKVUilJqJ9ACvAAcBrq01tYa5wbAmu0pB+oBzOe7gVGBMqXUrUqprUqpra2trcm9CkGIg/oOL/5giDcOt/P/djbaOnajKe7l43jubX1+uvsDMcc6ZtZIn6jnPlLcD7f0UVWUEQ6bzC/J4v9+Yg2fOLdqQuML04O4xF1rHdRaVwMVwDpgcbIn1lrfp7Veq7VeW1xcnOxwghATa0KzKMvFXX/ZR7c3ttDGi9WUYjxxN2yI7b0fa/fgdKhhBcgSoSTbRbbLGV44VdPSFxZ8i8vPmsEMmzx34dQkoRkQrXUX8DKwHshTSlnZNhWA5Qo1ApUA5vO5QLst1gpCElgTmv910yq6+gP87JUa28Zu6OwnNz01vPx+JHOLLXGPPala2+6lPD99whOUSinmmXXkfYEgdR3eqFk8wulNPNkyxUqpPPN+OnApsA9D5D9q7nYL8IR5/0nzMebzL2m7A5yCMAGOtnkoyExj/bxC1szOZ9uxTtvGbuj0jutpVxZk4FBwtDW2517X7p1wpozFvGJD3I+1ewlpmFci4n6mEY9rUAa8rJR6F3gHeEFr/RfgduA2pVQNRkz9fnP/+4FCc/ttwB32my0IiXO0zRMOjywszeJQc69tE6uNXf3jFshyOVOoyM/gSIywjNaa2nZP0jnj80uyaOkdYEedcQGbJ577GUfMRUxa63eBVVG2H8GIv4/c7gNusMU6QbCRo20ezl9gzO8sKMmmxzdIa+/AhBtEWxg57v2cN3/8uaPx0iG313XS5fVTXZlPr29w2MrPiWDF2J/bcwKlRNzPRGSFqnBG4BkYpLlnIOy5LzDF71BLX9Li3ukN4PUHY5a2nVOUyTu1HWiticwO3nK0g0/c/zaBYIivXbIQgKokwzKWuL9e0055XnrSZQaE6YfUlhHOCGrbDY/ZEvf5paa4j1imPxFiZcpYzCvOHLa4CGB3YzcbH3iHivx0FpRkc/cLBwGoKkrOc6/MTyctxYE/GBqVKSOcGYi4C2cEVjjE8oiLs1zkpqdy0IYOSY3hBUyxPHdDZI9ETKp+64ndZLqc/HbjOfzfT6whx+1EqeS7FzlTHEMXMgnJnJGIuAtnBNaqT8sjVkqxoCSLmubkxT3W6lSLOcXDc9211hxq7uPys0qZmZdOVVEm93/qbO64YjHu1OTDKPNKMs2/Iu5nIiLuwinFs7ubuOInm/HEaPCcKEfaPMzIcZORNjTNtKA0i4MtyWXMWNkt2W4nuenRc9wtynLcuJyOcK57h8dP38DgsDIDZ1cV8Pn3z5uwPZFYHruEZc5MZEJVOGU43tXPNx57lx7fIPtP9MRsTJEIkWmQFvNLsuny1tPu8VOUFX8RLV8gyAt7m3ly13G2mU2bl5fHbiLhcKhhGTN1ZpmBZDNjxuIDi0t4+UArS8smr866cOoi4i6cEoRCmq8/uov+QBAwlszbKe61bR6uHNEVKJwx09wXt7hrrfnIf7/BvqYeZuS4uWRJCUvKcvjAopK4jp9TlMkBcxLXEvfZk1QHffWsfJ76ynmTMrZw6iPiLpwSPLK1njcOt/PvH1nGd5/am1DXolh0ef10egPMGZFeuKDUKrDVy/p58TWBaO0dYF9TD1++cD5fu3QhKY7ECp7OKcrkhb3NDAZDHGufXM9dOLORmLtwSvD07hPMK87k79bNYm5RJofjWKYfL/tPGJ6yJeYWM3LcZLmcHErgQmLtu35eYcLCDoa4D4aMRU/H2r2U5rhsmTwVhJGIuAtTzsBgkC1H2zl/QfGwold2sfd4DwBLZw6PPSulmF+SNaol3XhYefELJjhJOTciY6auw8PsguQWKwnCWIi4C1PO9mNd+AIhzptfBBhZHvWdXnxm/D1Z9jX1UJSVRkn26JWoi2dks/9E/Bkzh1r6yHE7Kc6eWBejcK57m4e6Di+zJrHvqHBmI+IuTDmv17SR4lCcM9eYQJ1XkoXWwxf7JMPeph6WjJExsnRmDl3eACd6fHGNdailjwWl2Uy0uVh+Riq56ansa+qhuWdA4u3CpCHiLkw5r9W0UV2ZR7ZZC93Kz65pTT40EwiGONTcN2Y6oCX6VugmFjUtfRMOyYARCppbnMnmg0b3scnKlBEEEXdhSunuD/BuQxcbzJAMGHFppYz2cMlyuLUPfzA0Kt5usXhGNhCfuLf3DdDh8Se9KGhOUWa4vox47sJkIeIuTClvHWknpAnH2wHcqSlU5mfY4rnvazJEe6ywTLY7ldmFGew7EVvcrUyZBaXZSdk0N2IxVbJNOQRhLETchbh5cW8zdWZutl1sPthKRloK1ZV5w7bPL8myxXPfe7yHNKdjmKCOZMmMnDE990AwxB/ersPrHxwS96Q9d+P4bJeT/IzxSxYIwkQRcRfiorbNw62/3cpPXz5k25i+QJC/vNvEhYtLSHMO/1ecX5LFkTYPwVBynZL2NfWyqDR73H6kS2fmcKzDS1+UejaPb2/km4+/x388e4Ca5l4y01IoS7KxtFUGYVZhxoQnZgUhFiLuQlzc97cjhDQcmGAVxRPdPtr7BoZte3b3Cbr7A9y8btao/ecVZ+IfDFHfMfFfClpr9jb1xKytsqQsB63hwIjQjNaa/3mjFqXgwTdreXFfC/OTyJSxsCpTymSqMJmIuAsxaen18di2BlIcikPNvYQS9Kb7/UEu/NErrLnrRdb9+4v8+rWjAPxhSx1VhRmcO3f00v9FMwxBtmLm8fB6TRv/8Ltt4fz45h5jAnRJ2fgxcmuydWRoZsvRDvY19fDNK5dQnOWisas/6ZAMQEaakw+uKOOSJaVJjyUIYyHiLsTkgddrCQRDbDxvDl5/kMau/oSOP9bhoT8Q5COryplfksW//mUv33t6H1uOdnDjulk4oizjX1KWTVqKg531XXGf54W9zTyz+wQ/eGY/APduMroara0avwDZzFw3uemp7G0avlL1gTdqyctI5e/Pnc23P7QUgEVJTqZa/OzvVnPd6gpbxhKEaEjhMGFcen0BfvvWMa5cNoPLlpZy3+YjHGrppTKBFL7aNiO0svG8OSyakc3nf7uN+zYfITVF8dE10QXO5UxhycwcdiQg7tZF54E3avH6B3lkawNfvnA+y2KU41VKsaQsm70RvxIau/p5bs8Jbr1gHulpKXxweRlpn3BwbpwFxgRhqhHPXRiXh7bU0esb5AvvnxdOATyYYNz9mNm/dFZhBqkpDv775tVcurSUT66vGrfU7qrKPN5r6GYwGIrrPI2d/bxvXiHzS7J4ZGsDH1hUzNcuXRjXsWfNzGV/Uw8B81wv7m0mpOHGsysB4wJw2VkzyHFLdoswPRBxF8ZkYDDI/a8d5X3zCllRkUdueiqlOS4OnkisqXRtu5eCzLSwMLpTU/jlJ9fyrauXjntcdWUe/YFg3BeT4939zCvO4r9vXs1N62Zx78dXxV25sboyj4HBEAfM17azvovibJdMegrTFhF3YUye2HGc5p4BvhDR9m1haTYHWxIT97oOz4RE0sp939UQOzTjGRikyxtgZl46C0uz+f51y8lNIIfcOpcVBtpZ30V1ZZ6kKgrTFhF3ISqhkOYXmw9z1swczl8wtHp0YWk2NS19CeWf17Z5qZrASszZhRnkZ6Sysy62uB834+0z8yaWg16Rn05hZho767ro8vo52uYZtbBKEKYTIu5CVN5t7OZIq4eN580Z5r0uLM3CF4g//3xgMMjx7v4J1VBRSrGyMi+ujBlrMrUiPz3h81jnqq7MY2d9Z/h8q0TchWmMiLsQFauBxapZ+cO2D02qxheaaejsR+uhhTuJUl2Zx8GW3qirRyNpDHvuExN361yHWz387VAbSsHyithNrwXhVEXEXYjK4ZY+0lIcVI7whMNNpeOs+2Jlyky0QFZ1ZR5aw7sx4u7Hu/pxOlTUhhxxn2uW4ak/urWeBSVZ4RLEgjAdEXEXolLT0secosxRNVmy3amU56XHvXLUynGfPcHStisqDMGNVZL3eJePGbnuCfU1HXmuHt+gxNuFaU9McVdKVSqlXlZK7VVK7VFKfdXcXqCUekEpdcj8m29uV0qp/1RK1Sil3lVKrZ7sFyHYz+HWvjHrlq+tyuf1mra48s+PtXvIdjkpyEybkB0FmWmU5riGLTCKRmNnf1IhGYDc9FTmmT1OqyvzY+wtCKc28Xjug8A/aa2XAucCX1JKLQXuADZprRcAm8zHAFcCC8zbrcDPbbdamFR8gSB1Hd6w0I3ksqUz6PQG2HasM+ZYxzq8zC5Krvrh4hk57I8oDaC15qX9zVz9X3/j9sfeBYyYe0WS4g5Dor6yUuLtwvQmprhrrZu01tvN+73APqAcuAZ40NztQeBa8/41wG+0wVtAnlKqzHbLBbTWtPUN0NY3QL/fnmbSALXtHkLa6GUajfcvKiYtxcELe5tjjnWs3cvsguQaUiwuM9IvrdWjX3t4J595YCsHT/Txp+0NtPUNcKLHl7TnDnBN9Uzev7DYthoygjBVJBRzV0pVAauAt4FSrXWT+dQJwCpxVw7URxzWYG4bOdatSqmtSqmtra2tCZotAPzgmf2svetF1t71Iut/sIleX8CWcWvMydKxwjJZLifvm1/IC/ua0XrsfPfBoJEymewqzyUzcvAHQxxt89Dh8fPEruPctK6SR76wnsGQ5sE3agmGtC3ifsHCYh78zLpx678LwnQg7v9gpVQW8CfgH7XWwwKg2viGJ1QHVmt9n9Z6rdZ6bXFxcSKHCibP721mRUUu/3jJArq8AZ5574Qt49a09KEUzC0au7ztpUtLOdbuHZY1o7Xm5f0t/K+HdrDqX5+n+l9fYDCkJ7SAKZLFZsnefU09vHm4Ha3hhrWVrKzIZW5xJg++UQtA+QRz3AXhdCQucVdKpWII+++11n82Nzdb4Rbzb4u5vRGojDi8wtwm2EhDp5ejbR6urS7nqxcvYG5RJo9ta0h4nKffa+JfHn9vmAd+uNVDeV466WkpYx5n1SJ/fs/QBeXRbQ18+oF32HyolYuXlPKxtZX8wwfmcdlZydUtn1uURWqKYv+JXl6raSPb5WRFeS5KKT68ciY9PiMHvnyCq1MF4XQknmwZBdwP7NNa3xPx1JPALeb9W4AnIrZ/0syaORfojgjfCDbxek0bAOctKEIpxfVrKthS25FQj9NgSPO9p/fx+7frePNIe3h7TcvYmTIWpTluqivzhsXdX9zbTEV+Olu+eQk/umEl3/7QUm6/YjF5GRPLlLFIczqYV5zF/qYeXq9p49x5heGwyYdXzgzvZ0dYRhBOF+Lx3DcAnwAuUkrtNG9XAT8ALlVKHQIuMR8DPA0cAWqAXwJftN9s4bWadoqzXeFFRR9ZVY5S8Kft8Xvvm/Y109DZT4pD8YtXjwCG4B9p7WN+ceyOQ5csKWFXQzetvQOEQpq3j3bwvnmFo/qh2sGSshzePtpBXYeX8+YP1bqZW5zF8vJc8jJSyUiT9gSCYBHz26C1fg0YK4/t4ij7a+BLSdoljEMopHmjpo0LFhaHUwxn5qWzYV4Rf97RwFcvXhC1u9FIHnijlpm5bj5+9ix+/OJB9hzvJtuVysBgKKbnDnDh4hJ+9PxBXjnQwpKyHLr7A6yfpGYWi2dk8/gOI7q3IULcAb7zoaU0dCbWHUoQTnckJWAasv9EL+0e/yiR+8iqcuo7+tl9vDvmGAdO9PLG4XY+sb6KT72visy0FL771F7++XEjbzwecV9alsOMHDcv7W/hLTOss35uUYyjJsZis8n1jBz3qPz7tVUFXLtqVEKWIJzRiLifwozViNqKt2+YP9xLtkrzvnm4fdQxI7n/tSO4nA5uPLuSXLNP6JajHRxu8fDVixewZnbsFZpKKS5cXMzfDrWx+VAbc4oymZE7OZOaS2YYGTMb5hdJjXVBiAMR91OU9r4BVn73eZ7dPTy9sdcX4LFtDcwrzqQsd/gEYonp1UZOjkZjZ30Xj25r4OZzZpNvlgW47bKFPPnlDbx+x0V87dKFcQvohYtK6BsYZPPBVs6dO3n9RYuzXXz9soV87oI5k3YOQTidEHE/RdlR10XvwCC/ebM2vM0XCPK532zlcGsf/98Ho7eoWz+vkHeOdoRXc45kMBjim39+j5JsF1+7dEF4u8uZwoqKvIQLb22YX0SambkyWfF2MH4lfPmiBSyekTNp5xCE0wkR90kiGNIEgqG4mzuP5L1GI27+5pF2Grv60Vpz2yM7eetIBz+6YSUXLi6Jetz6uUV4/MHw8SP5n9dr2dvUw50fOsuWkraZLifnzC0A4FzzryAIU4/kjk0CLT0+Lr7nVXrNxTX/ds1ZfGJ9VUJj7G7spigrjbY+P49vb2BucRZPv3eC/335onEnDy2BffNwO6tHNNp4aEsd339mH5csKeGKZTMSe1Hj8KUL57NqVn5StdQFQbAXEfdJ4JWDrfT6Bvn8BXP526E2fvpyDR87uxKXc+wVnyN5r7Gb8xcUc7yrn0e2NjAwGGRpWQ6fv2DuuMcVZrlYVJrNW0fa+dKF88Pbf/ZyDf/nuQN8YFEx9964ytZJyXPnFk5qvF0QhMSRsMwk8HpNG0VZLu64cjF3XLmY5p4BnthxPO7jW3p8tPQOsKw8l+vXVFDX4aWld4DvXbc8roJW6+cVsrW2E/+gERLq9Pi5+/kDXLlsBr/85FoyXXJNF4TTHRF3m9Fa83pNG+fNL0QpxfkLijhrZg6/2Hx4zNTGkVjx8uXluVy1vIzc9FRuWV8Vd3egc+cW0h8IsqPOqLf+Wk0bIQ2fu2AuqVLtUBDOCOSbbjMHmntp6xtaYKSU4vPvn8eRVg8v7Itd/xwMcVcKzpqZQ5bLyeZvXMi3r46eHRON8xYU4XI6ePo9o6TPqwdbyU1PZWWFtI4ThDMFEXebee2QtcBoaKXmVctmMCPHzf/bEV9xzN2N3cwtygyHT3LTU+MqJ2CR5XJy8ZIS/vpeE4PBEJsPtnLe/KKk+osKgjC9EHG3mTcOtzO3OHNYhUJnioNz5hawva5z3OYWFu81doebNU+UD6+cSVufnwfeqKWld4D3L5Sa+YJwJiHibiOBYIi3jrQPq1posXpWPs09Axzv9o07Rkuvj+YeYzI1GT6wqIRsl5O7nz8IwPkLJ6fmiyAIpyZnXNrES/ub+eu7xpL+RTOyuPWCebaNvbO+C68/yPvmRRd3gO3HOikfo+5438Ag//TILgDWVSW3IMidmsJlZ83gT9sbWFSaPapUgSAIpzdnlOceCIa440/v8fyeE7x6sIXvPb2f3WOs5JwIW452AHDOnNHCvLgsG3eqg+1mBstIWnp93Hjfm7xxuJ3/89EVLK9IznMH+HC10cjiAvHaBeGM44wS92d2n6Cld4D/vGkVL339A2S7nPzi1cO2jb/tWCfzS7LCxbgiSU1xsKIij+11XaOeO9rm4fqfv8HhFg+/+uRablhbOWqfiXDe/CL+18UL+GSCq2MFQZj+nFHi/sDrR6kqzOD9C4vJcafyd+fO4un3mjjW7klonP/adIhbfr2FvoHB8LZQSLO1toO145TKXT0rn73Hu/EFguFtB070cv3P38AzEOShW88ds2bMREhxKG67dCGVBRm2jSkIwvTgjBH3XfVdbK/r4pb3VYXTCjdumIPT4eCXfzsS9zi+QJD7Nh/h1YOtfO7BrWGhrmnto8c3yNpxYuWrZ+URCOphRb0efLOWgUCQP/3D++JepCQIghCLM0bcH3yjlsy0FD66piK8rSTHzXWry3l0awPd3kBc4zy35wS9A4PcfM4s3jzSzj/+cSdaa96pNeLt43rus4cmVS121nWxalY+c4oyxzpMEAQhYU4rcQ8EQ/z2zdpRYZZOj5+/vNfEdasrRpW5vXHdLAYGQ7wY5+rRP21vpDwvnX+7Zhm3X7GYZ/ec4IW9zWyr7aQoy8XswrFDIEVZLmYVZLDNFPd+f5ADzb3isQuCYDunlbg/ufM433piDxff/SrfeWI3PT7DG//zjkb8gyFuWjdr1DErK3KZmevmmd1NMcdv7vHx2qFWrltdjsOh+Nz5c5hXnMn3n9nP20eNeHusaovvm1fIm4fbCQRDvNfYTTCkRdwFQbCd00rcH9pSR1VhBh87u5LfvV3H/350F1prHtpSR3VlHktnju7io5TiyuVlbD7YRq9v/NDM4zsaCWm4brUR2nGmOPjmVUs42uahsauftVWx+45euLiE3oFB3qntYGe94cFXzxJxFwTBXk4bcT/Y3MvWY53cfM5svveR5Xzj8kU8t6eZbz6+m5qWPv4uitducdXyGfiDIV7a3zLmPoPBEA9tqWPN7OHx8YsWl7DerGU+3mSqxXlmW7qX97ews76Livx0irJcCbxSQRCE2Jw24v7QljrSUhxcb06Yfvb8uaybU8BDW+rIcjm5emXZmMeuqsynNMcVrqIYjT/vaORYu5cvvH/4ilalFN+/bjn/8IF5LI+jZIDVlu6l/S3srOuSkIwgCJPCaSHuvkCQP29v5PJlMygwFxClOBT3fGwleRmp3Hh2JRlpY1dacDgUVy4r45UDrXgictc7PX4GgyECwRD/9dIhlpfncsmS0XnoVUWZ3H7F4rirLl60uITDrR6Od/tE3AVBmBROC3G/54WDdPcHuGnd8JWdFfkZvHb7RXzzqiUxx/jgijIGBkM8t8eoO9PS62PDD1/i8p9s5jtP7qG+o5+vXbrAlvZ0F0UsVBJxFwRhMpj24v7zVw5z3+Yj/P25s8Kx70iyXM64aqGvnZ3PrIIM/rS9AYBHtzbg9QcJafjD23WsrMzjwkX2rB6dXZjJ3OJMnA6VdPVHQRCEaEzrqpB/3FLHD5/dz4dXzuRfP7wsKa9aKcV1q8u5d9MhGjq9/PGdOtbPLeS3G9fx3J5mlpXn2NpU+tbz57L/RC/uuNKQYAAACl1JREFU1PibZguCIMTLtBb3JWU5XLeqnB9+dEVCnYrG4vrVFfzkxUN847F3qe/o539fvhhnioMPrhh7Mnai3DhO9o4gCEKyxAzLKKV+rZRqUUrtjthWoJR6QSl1yPybb25XSqn/VErVKKXeVUqtnkzjV1bmcc/Hq21r+lxZkMG6OQW8cbid/IxULj+r1JZxBUEQTjbxqOIDwBUjtt0BbNJaLwA2mY8BrgQWmLdbgZ/bY+bJ46PmAqWPrqnA5ZSQiSAI05OY4q613gx0jNh8DfCgef9B4NqI7b/RBm8BeUop+2Mak8iHVs5k43lz+Oz5c6faFEEQhAkz0Zh7qdbaWvFzArDiF+VAfcR+Dea2UauDlFK3Ynj3zJp16sSf09NS+NbVS6faDEEQhKRIOlittdaAnsBx92mt12qt1xYXFydrhiAIghDBRMW92Qq3mH+toiyNQORKogpzmyAIgnASmai4PwncYt6/BXgiYvsnzayZc4HuiPCNIAiCcJKIGXNXSj0EfAAoUko1AN8BfgA8opTaCBwDPmbu/jRwFVADeIFPT4LNgiAIQgxiirvW+qYxnro4yr4a+FKyRgmCIAjJMe1rywiCIAijEXEXBEE4DRFxFwRBOA1RRph8io1QqhVjYnYiFAFtNpozmUwXW6eLnSC2TgbTxU6YPrZOlp2ztdZRFwqdEuKeDEqprVrrtVNtRzxMF1uni50gtk4G08VOmD62ToWdEpYRBEE4DRFxFwRBOA05HcT9vqk2IAGmi63TxU4QWyeD6WInTB9bT7qd0z7mLgiCIIzmdPDcBUEQhBGIuAuCIJyOaK1P+g34NUaZ4N0R21YCbwLvAU8BOSOOmQX0AV+P2PZVYDewB/jHcc53BXAAo6DZHRHbv2xu00BRMnYCVUA/sNO8/SLimDXm/jXAf2KGwxKw8yJgu/laHwSck2ErkAH8Fdhvvqc/GOc9jfqagDsxyjxbY181Ge9pxLFPRo5l12dv8+f/immD9VxJgu9prO+GHZ99dsS2nRg52T+x87O3+T39OPAuxv/pDyfwf3qDeWwIWGuHTgErzOf2mM+7T9b3P+p48exk9w24AFg94k17B3i/ef8zwL+NOOYx4FFMcQeWmS82A6MA2ovA/CjnSgEOA3OBNGAXsNR8bpX5D1RLdHGP205znKgiA2wBzgUU8AxwZbx2Yvy6qgcWmvv9K7BxMmw138sLzftpwN+i2Trea8L4gn892jF2vqfm89cBfxjnfZ/wZ2/z5/8KYwhInO9prO+Gbe9pxPHbgAvs/Oxt/D8tBOqAYvPxg8DFCdq6BFg03meToK1OjIvNyggbU8azIZ7/VeL8/ke7TUlYRkfvy7oQ2GzefwG43npCKXUtcBTjimixBHhba+3VWg8Cr2J82UeyDqjRWh/RWvuBP2L0ekVrvUNrXWuXndEwm5nkaK3f0san8xuGes7GY2ch4NdaHxzvnHbYar6XL5v3/RjeQkUSrynaOZK207QhC7gNuGuc3Sb82dtpazzEeE/HPafddiqlFgIlGBf3ROyMiU22zgUOaa1bzccvRjtmPFu11vu01gdstPUy4F2t9S7z2HatdfBkff+jcSrF3PdgfvEwfjJVQvhLfDvw3RH77wbOV0oVKqUyMOrIVzKasfq62mqnyRyl1A6l1KtKqfMjzt8Qx/nHsrMNcCqlrNVtHyX667TD1jBKqTzgQ8CmMWwd7zV9WSn1rlLq10qp/Emy89+AuzH6BoyF3Z/9RG0F+B+l1E6l1LeUUmoMW8d6T8c7p912AtwIPGyKUSJ2QuKf/URsrQEWKaWqlFJODLEc67sfz3cvEcaydSGglVLPKaW2K6W+kaANtn//TyVx/wzwRaXUNoz4n9/cfifwY611X+TOWut9wA+B54FnMWJywSm0swmYpbVeheFR/kEplZPsycwv2I3Aj5VSW4Be4n+dE7LV/MI8BPyn1vpIgib/HJgHVJvnudtuO5VS1cA8rfXjCdpmBxN5T2/WWi8Hzjdvn7DpnHbbaXEjxuefKBP57BO2VWvdCfwD8DDGr4taTs53fzxbncB5wM3m348opUb1vEiUZL7/MZt1nCy01vsxftpYPws/aD51DvBRpdR/AHlASCnl01r/VGt9P3C/ecz3gAalVCXGRAfALzBiV7b1dR3LTq31ADBg3t+mlDqMcTVvZHhoowJoTMROrfWbGKKAUuoyc9zJsHWreeh9GD97f2Iem4IRgwVjAvPn0V6TOV6ztVEp9UvgL5Ng59nAWqVULcb/cIlS6hUM0Zy0z36Ctm7VWlvvTa9S6g/AOqXU74n/PR3ru2Grnea+KzEm7LaZjyf1s5+orVrrpzA/a6XUrUAwEVsnyjifRQOwWWvdZj73NEa8/nfRbDgZ3/+YQfnJujFisgQzgwDj18RvgM9EOeZOhmfLWMfMwsjwyItyjBM4AsxhaKLirBH71DL2pFpcdgLFDE2gzDU/mAIdfUIlWhbBmHZGnNOFESa5aBJtvQv4E+CI8flFfU1AWcQ+XwP+OBl2jjWWnZ+9HbaaNhSZ21MxEgO+kOB7Gs93w5b3FKOF5ncn67O38f/UOiYf41f7wkRsjXj+FcaZ7E7A1nyMOarIBI8Pnszv/6gx49nJ7hvGT74mIIBxxduIkdZ40Lz9gCjpQowW978Be803IupsubnfVea4h4F/idj+v8zzDwLHgV9N1E6MSY495j/aduBDEeOsxZgjOAz8NNpri2Hn/wH2YaRJRU35tMNWDG9Bm+f6/9u7excnojCKw+eAgqLFooXYiTZbyKK1FmnE2m4XBBurLcRGEBvXTtTGf2AbFS0sRFjBRgQRXBthXcRGq238ahSLFPpa3BsclnyZTGK483sgkJlM5pN7uCRz3+ncfna+x/a6HpOkO0q3fW0o9Z4OTuKc9mp8dV37Gs/pHqXeZOe2vdvKgfUP57Rv26jznCoFzPyA9jvSta+5Td1XavvvJC2OsK9n8vbbkj5JejpuTkk6m/d3U9KNabb/bi/KDwBAgWbpD1UAQE0IdwAoEOEOAAUi3AGgQIQ7ABSIcEcj2A7bdyvTO2x/sT3UQJsu65uzvVyZbo26LmASCHc0xU9JR23vztOnNN5oxTlJywOXAv4Twh1N8kR/h4svqVI/xfY+249y0atXthfy/JVcBOu57Y+2L+SvXJd0JBcDu5nn7bX90PZ72/d6FAgDpoJwR5M8kLRoe5fSgxXWK59dk/QmIhYkXVEaWt4xL+m0UlnWq7Z3Sros6UNEHIuIS3m545IuKtXhPizpxCQPBuiHcEdjRMSGUrmCJaVefNVJpeHziohnkvZXqiWuRUQ7UlGoz5IO9NjE64jYiojfSkPmD9V7BMDwZqYqJDAljyXdktRSehDCMNqV97/Uu90MuxwwcfTc0TSrSlUP326b/0KpFrdstyR9jYjvfdbzQ6meNzCT6FmgUSJiS+khxdutSFq1vaH0dKdzA9bzzfZL25tKZVzX6t5XYBxUhQSAAvGzDAAUiHAHgAIR7gBQIMIdAApEuANAgQh3ACgQ4Q4ABfoD45YzUnKfzBIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ap1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "2wVAcd_T9mwU",
        "outputId": "d81da3f6-9549-4e43-dfa8-ff0714b9fd90"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Passengers\n",
              "Month              \n",
              "1949-01         112\n",
              "1949-02         118\n",
              "1949-03         132\n",
              "1949-04         129\n",
              "1949-05         121\n",
              "...             ...\n",
              "1960-08         606\n",
              "1960-09         508\n",
              "1960-10         461\n",
              "1960-11         390\n",
              "1960-12         432\n",
              "\n",
              "[144 rows x 1 columns]"
            ],
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              "      <th>Month</th>\n",
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              "      <th>1949-01</th>\n",
              "      <td>112</td>\n",
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              "    <tr>\n",
              "      <th>1949-02</th>\n",
              "      <td>118</td>\n",
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              "    <tr>\n",
              "      <th>1949-03</th>\n",
              "      <td>132</td>\n",
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              "    <tr>\n",
              "      <th>1949-04</th>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-05</th>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-08</th>\n",
              "      <td>606</td>\n",
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              "    <tr>\n",
              "      <th>1960-09</th>\n",
              "      <td>508</td>\n",
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              "    <tr>\n",
              "      <th>1960-10</th>\n",
              "      <td>461</td>\n",
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              "      <td>390</td>\n",
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              "      <th>1960-12</th>\n",
              "      <td>432</td>\n",
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              "<p>144 rows × 1 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing for Stationary\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "result = adfuller(ap1['Passengers'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrpBm_PAGhhS",
        "outputId": "0593428b-fe45-4ed1-d7f8-e1f840e2e8bb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: 0.815369\n",
            "p-value: 0.991880\n",
            "Critical Values:\n",
            "\t1%: -3.482\n",
            "\t5%: -2.884\n",
            "\t10%: -2.579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ho:Series is not stationary\n",
        "\n",
        "Ha:Series is stationary\n",
        "\n",
        "If significance level(alpha) is 5%, what is the decision?"
      ],
      "metadata": {
        "id": "u-cdBGalGv8r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Take differnce for making stationary\n",
        "dap = ap1 - ap1.shift(1)\n",
        "dap.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "2o5iuXEgGyU2",
        "outputId": "9fe15a88-23b9-481b-c4ca-0ab126f04114"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fede31aa5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "dap=dap.dropna()\n",
        "result = adfuller(dap['Passengers'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YrHmNAuINVC",
        "outputId": "84d0c844-0afa-4bd5-9aa0-68bead7f078b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -2.829267\n",
            "p-value: 0.054213\n",
            "Critical Values:\n",
            "\t1%: -3.482\n",
            "\t5%: -2.884\n",
            "\t10%: -2.579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dap = dap - dap.shift(12)\n",
        "dap.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "AksUm_4cGshr",
        "outputId": "905db93e-8d4e-47dd-e512-1131bf423dd5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fede313a250>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "dap=dap.dropna()\n",
        "result = adfuller(dap['Passengers'])\n",
        "print('ADF Statistic: %f' % result[0])\n",
        "print('p-value: %f' % result[1])\n",
        "print('Critical Values:')\n",
        "for key, value in result[4].items():\n",
        "\tprint('\\t%s: %.3f' % (key, value))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LC7LOFI8-rW",
        "outputId": "18f625bf-dc88-4d30-9aae-c0194b34db97"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -15.595618\n",
            "p-value: 0.000000\n",
            "Critical Values:\n",
            "\t1%: -3.482\n",
            "\t5%: -2.884\n",
            "\t10%: -2.579\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ACF and PACF"
      ],
      "metadata": {
        "id": "B8ar9_SF9JjF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics import tsaplots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = tsaplots.plot_acf(dap['Passengers'], lags=36)\n",
        "plt.show()\n",
        "fig = tsaplots.plot_pacf(dap['Passengers'], lags=36)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "nqucss19GD6m",
        "outputId": "be0fef03-c41e-45d7-fe24-2965bb1aa424"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/graphics/tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SARIMA"
      ],
      "metadata": {
        "id": "c-4hRML2IJaU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(ap1['Cost'],order=(0,1,1),seasonal_order=(0,1,1,12))\n",
        "model_fit=model.fit()\n",
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "91jmn1dDHa-r",
        "outputId": "2293b4f2-89a2-44a5-c8cf-7292b0c5c122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                     SARIMAX Results                                      \n",
              "==========================================================================================\n",
              "Dep. Variable:                               Cost   No. Observations:                  204\n",
              "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 12)   Log Likelihood                 279.164\n",
              "Date:                            Wed, 11 May 2022   AIC                           -552.329\n",
              "Time:                                    02:32:36   BIC                           -542.572\n",
              "Sample:                                07-01-1991   HQIC                          -548.377\n",
              "                                     - 06-01-2008                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ma.L1         -0.7581      0.046    -16.443      0.000      -0.848      -0.668\n",
              "ma.S.L12      -0.5650      0.056    -10.177      0.000      -0.674      -0.456\n",
              "sigma2         0.0031      0.000     10.054      0.000       0.002       0.004\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   2.14   Jarque-Bera (JB):                 1.60\n",
              "Prob(Q):                              0.14   Prob(JB):                         0.45\n",
              "Heteroskedasticity (H):               2.31   Skew:                             0.13\n",
              "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.37\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                <td>Cost</td>              <th>  No. Observations:  </th>    <td>204</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(0, 1, 1, 12)</td> <th>  Log Likelihood     </th>  <td>279.164</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Wed, 11 May 2022</td>        <th>  AIC                </th> <td>-552.329</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>02:32:36</td>            <th>  BIC                </th> <td>-542.572</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>07-01-1991</td>           <th>  HQIC               </th> <td>-548.377</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 06-01-2008</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>   -0.7581</td> <td>    0.046</td> <td>  -16.443</td> <td> 0.000</td> <td>   -0.848</td> <td>   -0.668</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L12</th> <td>   -0.5650</td> <td>    0.056</td> <td>  -10.177</td> <td> 0.000</td> <td>   -0.674</td> <td>   -0.456</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td>    0.0031</td> <td>    0.000</td> <td>   10.054</td> <td> 0.000</td> <td>    0.002</td> <td>    0.004</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>2.14</td> <th>  Jarque-Bera (JB):  </th> <td>1.60</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.14</td> <th>  Prob(JB):          </th> <td>0.45</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>2.31</td> <th>  Skew:              </th> <td>0.13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>3.37</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(ap1['Passengers'],order=(1,1,1),seasonal_order=(0,1,0,12))\n",
        "model_fit=model.fit()\n",
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "L1uxognz9irh",
        "outputId": "581d0fcd-8908-461d-b16f-1e3ff92d9958"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                      SARIMAX Results                                      \n",
              "===========================================================================================\n",
              "Dep. Variable:                          Passengers   No. Observations:                  144\n",
              "Model:             SARIMAX(1, 1, 1)x(0, 1, [], 12)   Log Likelihood                -508.197\n",
              "Date:                             Wed, 11 May 2022   AIC                           1022.393\n",
              "Time:                                     03:20:45   BIC                           1031.019\n",
              "Sample:                                 01-01-1949   HQIC                          1025.898\n",
              "                                      - 12-01-1960                                         \n",
              "Covariance Type:                               opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -0.3029      0.355     -0.853      0.393      -0.999       0.393\n",
              "ma.L1         -0.0052      0.336     -0.016      0.988      -0.663       0.652\n",
              "sigma2       137.0156     14.653      9.350      0.000     108.295     165.736\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 7.71\n",
              "Prob(Q):                              0.98   Prob(JB):                         0.02\n",
              "Heteroskedasticity (H):               2.33   Skew:                             0.00\n",
              "Prob(H) (two-sided):                  0.01   Kurtosis:                         4.19\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>             <td>Passengers</td>            <th>  No. Observations:  </th>    <td>144</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-508.197</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Wed, 11 May 2022</td>         <th>  AIC                </th> <td>1022.393</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>03:20:45</td>             <th>  BIC                </th> <td>1031.019</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-01-1949</td>            <th>  HQIC               </th> <td>1025.898</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 12-01-1960</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>   -0.3029</td> <td>    0.355</td> <td>   -0.853</td> <td> 0.393</td> <td>   -0.999</td> <td>    0.393</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>   -0.0052</td> <td>    0.336</td> <td>   -0.016</td> <td> 0.988</td> <td>   -0.663</td> <td>    0.652</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>  137.0156</td> <td>   14.653</td> <td>    9.350</td> <td> 0.000</td> <td>  108.295</td> <td>  165.736</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>7.71</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.02</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>2.33</td> <th>  Skew:              </th> <td>0.00</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th> <td>4.19</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(ap1['Passengers'],order=(0,1,1),seasonal_order=(0,1,0,12))\n",
        "model_fit=model.fit()\n",
        "model_fit.summary()"
      ],
      "metadata": {
        "id": "ytK19ic2Ipr2",
        "outputId": "6b034aa4-1852-44e1-c585-f3290052fd49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                      SARIMAX Results                                      \n",
              "===========================================================================================\n",
              "Dep. Variable:                          Passengers   No. Observations:                  144\n",
              "Model:             SARIMAX(0, 1, 1)x(0, 1, [], 12)   Log Likelihood                -508.319\n",
              "Date:                             Wed, 11 May 2022   AIC                           1020.639\n",
              "Time:                                     03:21:23   BIC                           1026.389\n",
              "Sample:                                 01-01-1949   HQIC                          1022.975\n",
              "                                      - 12-01-1960                                         \n",
              "Covariance Type:                               opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ma.L1         -0.3184      0.063     -5.038      0.000      -0.442      -0.195\n",
              "sigma2       137.2653     15.024      9.136      0.000     107.818     166.713\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 5.46\n",
              "Prob(Q):                              0.95   Prob(JB):                         0.07\n",
              "Heteroskedasticity (H):               2.37   Skew:                             0.02\n",
              "Prob(H) (two-sided):                  0.01   Kurtosis:                         4.00\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>             <td>Passengers</td>            <th>  No. Observations:  </th>    <td>144</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-508.319</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Wed, 11 May 2022</td>         <th>  AIC                </th> <td>1020.639</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>03:21:23</td>             <th>  BIC                </th> <td>1026.389</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-01-1949</td>            <th>  HQIC               </th> <td>1022.975</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 12-01-1960</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>   -0.3184</td> <td>    0.063</td> <td>   -5.038</td> <td> 0.000</td> <td>   -0.442</td> <td>   -0.195</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>  137.2653</td> <td>   15.024</td> <td>    9.136</td> <td> 0.000</td> <td>  107.818</td> <td>  166.713</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>5.46</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.95</td> <th>  Prob(JB):          </th> <td>0.07</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>2.37</td> <th>  Skew:              </th> <td>0.02</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th> <td>4.00</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=ap1[:-12]\n",
        "test=ap1[-12:]\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "qfXCZZsY7557",
        "outputId": "2a3f9db9-6168-46fd-ac9b-2ecdb12e3c3d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Passengers\n",
              "Month              \n",
              "1949-01         112\n",
              "1949-02         118\n",
              "1949-03         132\n",
              "1949-04         129\n",
              "1949-05         121\n",
              "...             ...\n",
              "1959-08         559\n",
              "1959-09         463\n",
              "1959-10         407\n",
              "1959-11         362\n",
              "1959-12         405\n",
              "\n",
              "[132 rows x 1 columns]"
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              "\n",
              "  <div id=\"df-48a19c75-7fcd-4ede-895f-b12002de3d7e\">\n",
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              "      <th>Passengers</th>\n",
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              "      <th>Month</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1949-01</th>\n",
              "      <td>112</td>\n",
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              "      <th>1949-02</th>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
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              "      <th>1949-03</th>\n",
              "      <td>132</td>\n",
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              "    <tr>\n",
              "      <th>1949-04</th>\n",
              "      <td>129</td>\n",
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              "      <th>1949-05</th>\n",
              "      <td>121</td>\n",
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              "      <th>1959-08</th>\n",
              "      <td>559</td>\n",
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              "      <th>1959-09</th>\n",
              "      <td>463</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(train['Passengers'],order=(0,1,1), seasonal_order=(0,1,0,12))\n",
        "model_fit=model.fit()\n",
        "test_prediction = model_fit.forecast(12)\n",
        "test_prediction=pd.DataFrame(test_prediction)\n",
        "new_index = test.index\n",
        "test_prediction=test_prediction.set_index(new_index)\n",
        "test_prediction.columns=['Passengers']\n",
        "#As we take ln , convert back to original series using exp\n",
        "\n",
        "\n",
        "plt.plot(train['Passengers'],label='TRAIN')\n",
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using SARIMA');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "-nvFNkCqMbhZ",
        "outputId": "93d2d50b-9612-4f9a-e834-d0c8c5ea6ce1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Test and Predicted Test using SARIMA');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "DMzg0DBZ-g5T",
        "outputId": "620082e3-43ec-4d28-861f-c7e767e8e891"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate forecasting Performance\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_prediction)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,test_prediction)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_prediction) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2FBRO3PqLAI",
        "outputId": "d5bafd54-51da-4418-9b0a-1919598a3d13"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 18.192135533294344\n",
            "Mean Squared Error = 554.0683011735397\n",
            "Mean Absolute Percentage Error = 4.1063384510531575\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "HOLT WINTERS"
      ],
      "metadata": {
        "id": "r5-XUctT9zGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "fitted_model = ExponentialSmoothing(train,trend='mul',seasonal='mul',seasonal_periods=12).fit()\n",
        "test_prediction = ExponentialSmoothing(train,trend='mul',seasonal='mul',seasonal_periods=12).fit().forecast(12)\n",
        "test_prediction=pd.DataFrame(test_prediction)\n",
        "new_index = test.index\n",
        "test_prediction=test_prediction.set_index(new_index)\n",
        "test_prediction.columns=['Passengers']\n",
        "\n",
        "\n",
        "plt.plot(train['Passengers'],label='TRAIN')\n",
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using Holt Winters');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "1xlXKsUIrwpm",
        "outputId": "124ef121-75bb-4d58-8d1f-22158d5dc7e5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning: overflow encountered in matmul\n",
            "  return err.T @ err\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Test and Predicted Test using Holt Winters');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "X4kQic9pvyVO",
        "outputId": "22a9e994-bb93-4427-fe10-2645885fdced"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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LJw64D0ApFSMivwBbgQxgbFkfoTN+/Pgcv2dm5v1w5s6dW+BxXGlIplGKlk/UF1Jv+Q4adAWvyo45b+i1MPsx2D7HJHwjT3YlfKXUBuDSsZ4jC6j/GvBaMeIyjLLp1AE9WqbrI46/89W3NtTvDNtnQa+nHXtuo0wwYwYNoyRFf6u/R9o9fqFkhV4LRzbDyTjnnN9waSbhG0ZJyUiF6MnQfCDUcNIF+7BB+vv2grsXjYrJJHzDKCkxf+jhlp3ucV4MNRvrC8PbZxde16hwTMI3jJKy9kuo1RQa9XJuHGGD9M1c5447Nw7D5ZiEbxgl4dB6fZdrx3tKb9ilvUKvBWXTF48NIxuT8Avg7u5OeHj4ha+4uDgAli9fTqdOnQgNDSU0NJQvvvjiwj7jx48nKCiI8PBwWrZsydSpUy+U3XnnnTRq1OjC8bp27Xqh7M8//yQyMpKWLVvSvn17Hn/88RyxhIeHM3z48BzbVq9eTefOnQkPDycsLIzx48fz7bffXji+l5cXbdq0ITw8nHHjxpXCM2RcsOYr8Kyix9s7W922UK2BHp5pGNkppZz+FRERoS61devWXNscrUqVKrm2HT58WNWvX19FR0crpZRKSEhQHTp0ULNnz1ZKKfXSSy+pt956Syml1M6dO1XVqlVVWlqaUkqpUaNGqWnTpuU65ubNm1Xjxo3Vtm3blFJKZWRkqE8++eRC+datW1Xr1q1VYGCgOnv27IXtzZs3Vxs2bLiwT0xMTI7jNmzYUCUkJOT7+FzhOS4XziUq9UptpWY+6uxILvpznFL/C1Aq5bSzIzFKERClipBrTQu/iD7++GPuvPNOOnToAIC/vz8TJkzgjTfeyFW3WbNmVK5cmZMnTxZ4zAkTJvDcc88RGhoK6E8WDzzwwIXyqVOnMnLkSPr378+MGTMubD927Bj16tW7sE/Lli2L/fiMy7B+CmSkOPdi7aVCr4XMVNi90NmRGC6kbKx49ec4Pba4JNVtA1fnTtLZZZ9CoVGjRvz+++/ExMQwatSoHPUiIyOJiYnJtf+6deto1qwZtWvXvrDtySef5NVXXwWgVatW/PDDD2zZsiVXF052P//8MwsWLGD79u18+OGHjBgxAoDHHnuMFi1a0KtXLwYOHMioUaPw8fGx7/EbJcOWCWu/hobdoI4LTfLX4AqoXEt367S6wdnRGC6ibCR8J8lvGuTCvPvuu3z77bfs3LmTWbNm5Sh76623GDp0qN3HioqKwt/fnwYNGhAUFMSYMWM4ceIENWvW5MUXX+S2225j/vz5/Pjjj0ydOpUlS5YUOV6jGHYtgFP74KqXnR1JTm7u0OJq2DoLMtLAw8vZERkuoGwk/EJa4o7UsmVLoqOjGTJkyIVt0dHRtGp1sXX32GOP8cQTTzBz5kzuuusu9uzZU2DLu1WrVkRHR+dYJzfL1KlT2b59OyEhIQCcPn2aX3/9lXvu0d0HTZo04YEHHuCee+4hICCAxMTEC6tuGQ6w9kuoWk9PXOZqQq+D9d9D3FKzwLkBmFE6RTZ27FgmTZp0oeWfmJjI008/zVNPPZWr7uDBg4mMjGTy5MkFHvPJJ5/k9ddfZ+fOnQDYbDY+++wzbDYbv/zyC5s3byYuLo64uDhmzJhxYeTPnDlzUNaaxLt27cLd3Z3q1auX5MM1CpK4R/eRR4wGd09nR5Nb41565NA2cxOWoZmEX0T16tXj+++/55577iE0NJSuXbsyZswYrrvuujzrv/jii0ycOBGbzQbo5J59qGdaWhpt27blvffe49ZbbyUsLIzWrVsTGxvLsmXLCAoKIjAw8MLxevTowdatWzl8+DBTpkyhRYsWhIeHM3LkSH744Qfc3c3ydg6z9mtw84CIUYXXdQZPH2jWz1r60ObsaAwXIFktRGeKjIxUUVFRObZt27aNsLAwJ0VUMZjnuBjSzsHEMGjSF4Z9m6v4dEo6fj4u0OrfNA1+uxvuWgD1Ozk7GqOEiUi0UurSmYzzZVr4hnE5Nk+DlCS9POEl5sccoe34+dzy+SrmbTlCps2JjapmV+lPIdtmFV7XKPdMwjeMolJK31lbp3We68d+uSwWf19v4k+e5/7vo+n51mK+WLqHpOR0x8daqTo06qEnU3OBT/OGc7l0wneF7qbyyjy3xbB/NRzdrG+0umTt2JhDSayNO8n9PRvzz5O9+Oz2DgRVr8Trc7fT5f8W8dzvm9l97Ixj4w0dBCdiIWG7Y89ruByXTfg+Pj4kJiaaxFQKlFIkJiaam7Qu19ovwbsatBmWq2jyyjgqebozLLI+Hu5uDGxdj5/vu4I5j3RnUNt6TIuOp9/EpYz8+l/+3n4UmyO6e0Kv1d/NaJ0Kz2XH4QcHBxMfH09CQoKzQymXfHx8CA4OdnYYZc+ZI7B1hu6796qSo+jkuTRmbDjETRHBVKuU84Jtq8BqvDWsHeOuDmXqmv1MWb2PMZOiCKlVmVFdQxgaEUzV0rrIW7UuBHfUSx/2fLJ0zmGUCS6b8D09PWnUqJGzwzCMnKIngy0DOt6dq+intQdIzbAx6oqQfHev5evNQ32acV/PJvy55QiTVuzl5VlbeWf+ToZGBHNn1xBC/Kvku/9lCx0EC1/Sa+5Wr1/yxzfKBJft0jEMl5OZrtesbdIXajXJUZSRaeP71fu4onEtWtStWuihPN3dGNwukN8e7MaMsd3oF1abH/7dR+93lnDXpLUs25VQst2ZYdZ9ImbK5ArNJHzDsNf22XDmcJ5DMRduO8bBU+cZ1TWkyIdtV7867w1vz4qn+/Bwn2ZsjD/FyK/X0P/dpXy/eh/JaRnFj71WEwgINUsfVnAm4RuGvdZ8BdUb6LHtl5i8Mo6g6pXoF1Y7jx3tU9vPh/9e1ZwV4/rwzrB2eHu68fwfW+jy+iL+b+424k8mFyd63a2zbwUknyjecYwyyyR8w7DH0RjYt1z33bvlnL5ix5EzrIpN5PYuDfFwL/5bytvDnZsigpn1UHem338FVzYL4Kvle+kxYTH3T4lmdexljl4LG2QtffhnsWM0yiaXvWhrGC5l7Vfg4QPtR+YqmrwqDm8PN4Z3LNmLoSJCZEhNIkNqcujUeaas3sfUNfuZF3OEsHp+jO4WwuB2gfh42jl/Ur1w8AvW3TrtbyvRWI2ywbTwDaMwKUmw8WdofRNUrpmjKCk5nd/XHWRIeCA1qpTenPOB1Svx9MBQVj/TlzdubIPNpnhq+ia6vvE3b/+1g2NnUgo/iIgek7/nbz0XkFHhmIRvGIXZMBXSz+W5hOG06AOcT8+8rIu1l8PH053hnRow7z9X8uM9nYloWIOPl+zmls9X23cTV9ggvRzj7kWlH6zhckzCN4yC2Gz6ztqgSAhsn6Mo06b4btU+OobUoFVgNYeGJSJ0beLPl3dEMvHmduw9fo7VsYmF79igK1SqaUbrVFB2JXwRqS4i00Vku4hsE5ErRKSmiCwQkV3W9xpWXRGRD0Rkt4hsEpEOpfsQDKMU7V0CibvzHIq5ZMcx9p9IdljrPj9Xt65HVR8PpkXHF17Z3UMvfbhznr6vwKhQ7G3hvw/MU0qFAu2AbcA4YJFSqhmwyPod4GqgmfV1L/BpiUZsGI605iuo7A+trs9VNGllHHX9fBjQqq4TArvIx9Odwe0C+XPLYU6n2JHEQ6/V1yXilpd+cIZLKTThi0g1oAfwNYBSKk0pdQoYAmSt3TcZyHpHDAG+U9pqoLqI1CvxyA2jtJ3aDzv/1CtaeXjnKNqTcJZlu45zW+cGeJbAUMziGhZZn5R0G3M2HS68cpM+4FnZdOtUQPa8UhsBCcC3IrJeRL4SkSpAHaVU1qvrCFDH+jkIOJBt/3hrWw4icq+IRIlIlJkgzXBJUd/o7xGjcxV9tzIOL3c3bu3cwMFB5a1dcDWa1vZluj3dOp6VoGlfPc2CWfqwQrEn4XsAHYBPlVLtgXNc7L4BQOm7QIp0J4hS6gulVKRSKjIgIKAouxpG6UtPgXXfQYtrck02diYlnenR8QxqWw9/X+98DuBYIsKwiGCi951kT8LZwncIHaSniTi0rvSDM1yGPQk/HohXSv1r/T4d/Q/gaFZXjfX9mFV+EMj+Dgm2thlG2RHzOyQn5jkU89foeM6lOW4opr1u6BCEu5vY18pvPkAvfWi6dSqUQhO+UuoIcEBEWlib+gJbgZnAKGvbKGCG9fNM4A5rtE4XIClb149hlA1rvwT/5tCoZ47NNmsoZnj96rSrX91JweWtdlUfejUP4Ld18YWvo1upBoR0N4uiVDD2Xm16GPhBRDYB4cDrwBvAVSKyC+hn/Q4wF4gFdgNfAg+WaMSGUdoORuuvjrmXMFy2+zixx89xp4u17rMMjQjm6OlUlu2y47pY6CBI3AUJO0o/MMMl2DWXjlJqAxCZR1HfPOoqYGwx4zIM51nzFXj5QrvhuYomr4zD39eba9q45sCzvmF1qFHZk2nR8fRqUcjMnaHXwtwndLdOQIuC6xrlgvPHkxmGKzmXCFt+hba3gI9fjqJ9iedYvOMYIzo3wMvDNd86Xh5uDAkPYkHMUU4lpxVc2S8QgiJMt04F4pqvWsNwlvXfQWZqnhdrv1u1D3cRbnORoZj5GRYZTFqmjZkbDxVeOXSQHqmTZMZVVAQm4RtGFlsmrP0GQq6E2mE5is6lZvBL1AGublOPOn4+TgrQPq0CqxFWz8++0Tqhg/R3s/RhhWASvmFk2fkXJO3Ps3X/+/qDnEnJ4M6uDZ0QWNENiwhmU3wSO46cKbhiQHM9GskMz6wQTMI3jCxrv4SqgdDi2hyblVJ8tyqO1kF+dGhQwzmxFdH17YPwdBemRR0ovHLoID2vjln6sNwzCd8wAI7v1guDRI7RM0pms2pPIjuPnmXUFSHIJcM0XVXNKl70Da3DHxsOkp5ZyPQJYYNAZepPOEa5ZhK+YYBewtDNU0+UdolJK+OoWcWL69oFOiGwyzc0IpjjZ9NYsqOQMfn12utPNqZbp9wzCd8wUs/Chh+h5RDwzTl2Pf5kMgu3HWV4x/r2rx3rInq1CMDf17vwbh03Nz0mf/ciSEt2THCGU5iEbxibf4HUpDwXOZmyeh8iwu1dysbF2uw83N24sUMQf28/xvGzqQVXDhsEGed1t5ZRbpmEb1RsSuk7a+u2gfqdchSlpGfy89oD9G9Zh8DqlZwUYPEMjQgmw6b4Y30h4+wbdgOf6qZbp5wzCd+o2PavgmMxunV/yQXZGRsOcio53eVmxSyK5nWq0i64GtOj49GznuTD3ROaD4Qdf0JmhuMCNBzKJHyjYlvzhW7Zth6aY7NSikkr9xFatyqdG9V0UnAlY2hkfbYfOUPModMFVwwbBCmnYN8KxwRmOJxJ+EbFdfowbJsF7W8Hr8o5itbGnWTb4dOM6lp2hmLmZ3DbQLw83Aq/eNukL3hUMt065ZhJ+EbFFT1JT6fQ8a5cRZNXxlGtkifXh+danbPMqVbZkwGt6jJj4yFSMzLzr+hVWa93u32OvrZhlDsm4RsVU2a6TvhN+0HNxjmKDiedZ17MEW7pWJ9KXmVrKGZ+hkYEcyo5nYVbjxVcMWwQnD4Ih9Y7JjDDoUzCNyqmbbPg7JE8h2L+sHo/NqUYWQaHYuane1N/6lXzYXp0Id06zQeCuJtunXLKJHyjYlrzJdQI0S38bFLSM5m6Zj99Q+tQv2blvPctg9zdhBs7BPHPzgSOnk7Jv2LlmhDSzcyRX06ZhG9UPEe2wP6V0PFufZdpNnM2HSbxXJrLLmFYHEMj6mNT8Nu6Qsbkhw6C4zvg+C7HBGY4jEn4RsWz9kvw8IHw23JsVkoxeVUcTWv70q1pLefEVooa+VchsmENpkUfKHhMfqg1W6jp1il3TMI3Kpbzp2DTL9BmmO6+yGb9gVNsik9i1BUNy/xQzPwMiwwmNuEc6w+cyr9StWAIbG+6dcohk/CNimXDj5CenOciJ5NXxuHr7cENHYKdEJhjXNs2kEqe7kyLKmQ1rNBr4WCUvlfBKDdMwjcqDptNT4Mc3AnqtctRdOxMCnM3H2ZoRDC+3h75HKDs8/X24Oo2dZm98RDn0woYkx96nf6+wyx9WJ6YhG9UHLGL4cSePIdi/vjvftIzFXdcUX6GYuZnaEQwZ1Iz+CvmSP6VAlpAraamW9UetosAACAASURBVKecMQnfqDjWfAlVAqDl4Byb0zJs/PDvfno2D6BxgK+TgnOcLo1qEVyjUsGLnItYSx8ug/MnHRecUapMwjcqhpP7YOc8iLgTPLxzFP255TAJZ1LL5VDMvLi5CUMjglmx5zgHT53Pv2LoILBlwK4FjgvOKFUm4RsVQ9TXIG4QMTpX0eSVcYTUqkzP5gFOCMw5buoQjFLwa0Gt/KAI8K2r70o2ygWT8I3yL/UsrJsCoddAtZyToW2OT2Ld/lOMvCIEN7fyORQzL/VrVuaKxrWYHh2PzZbPmPwLSx8uhPQCPgkYZYZJ+Eb5t+ZzOH8Cuv0nV9GklXFU9nJnWGT5HYqZn2GRwew/kczauBP5Vwq9Vg9jjV3isLiM0mNXwheROBHZLCIbRCTK2jZeRA5a2zaIyDXZ6j8jIrtFZIeIDCit4A2jUClJsOIDaDYAgiNzFCWeTWXWpkPc2CEIPx9PJwXoPFe3roevtwfTCurWCbkSvKuZ0TrlRFFa+L2VUuFKqezvmnetbeFKqbkAItISGA60AgYCn4hI+Zhj1ih7Vn+qV3Hq/Wyuop/WHiAtw8aoK0IcH5cLqOTlzqC29Zi7+TDnUvNZ1tDDC5oPgB1zzdKH5UBpdOkMAX5SSqUqpfYCu4FOhexjGCUv+QSs+liPNgkMz1GUkWnj+9X76Na0Fs3qVHVSgM43NCKY5LRM5mwu4I7asEG6S2z/KscFZpQKexO+AuaLSLSIZL9r5SER2SQi34hIDWtbEJB90u14a1sOInKviESJSFRCQsJlBW8YBVr1EaSeybN1P3/rUQ4npVTY1n2WiIY1aOxfpeAx+U36gru3XgnLKNPsTfjdlVIdgKuBsSLSA/gUaAKEA4eBd4pyYqXUF0qpSKVUZEBAxRkOZzjIueOw+jNodQPUaZWreNLKOIJrVKJvWB0nBOc6RISbIoJZs/cE+xLP5V3J29da+nC2WfqwjLMr4SulDlrfjwG/A52UUkeVUplKKRvwJRe7bQ4C9bPtHmxtMwzHWfEeZJyHXs/kKtp2+DRr9p5gZJeGuFegoZj5ualDMG5Cwa38sEGQdAAOb3RcYEaJKzThi0gVEama9TPQH9giIvWyVbsB2GL9PBMYLiLeItIIaAasKdmwDaMAZ47Cmq+gzc0Q0DxX8eSVcfh4unFLx/p57Fzx1K3mQ/dmAfwaHU9mfmPymw/UN66Zbp0yzZ4Wfh1guYhsRCfuOUqpecAEa6jmJqA38BiAUioG+AXYCswDxiqlCpiWzzBK2PKJkJkGPZ/KVXQqOY0/Nhzk+vAgqlf2ckJwrmlYRDCHklJYued43hWq+EODrmZRlDKu0HlglVKxQLs8to8sYJ/XgNeKF5phXIakgxD1DYSPgFpNchX/vPYAKek2RlWQeXPsdVXLOvj5eDA9Op4rm+VzTS1sEMwbB4l78nxuDddn7rQ1ypdlb+sLi3m07jNtiimr99GpUU3C6vk5ITjX5ePpzpDwIOZtOULS+fS8K5mlD8s8k/CN8uPkPj1nToc7oHqDXMWLth0l/uT5CjMrZlENjQgmNcPG7E2H8q5QvQHUbWv68cswk/CN8mPpBH1hsccTeRZPXhVHvWo+9G9ZsYdi5qdtcDWa1/EtePnDsOvgwBp9Ydwoc0zCN8qHxD2wYSpEjgG/wFzFu46eYcXuRG7v0hAPd/Oyz4uIMCyiPhsOnGL3sTN5VwodBCjYNtOhsRklw7zyjfLhnwng7gXdH8uzePKqOLw83BhuhmIW6Pr2Qbi7Sf4TqtUOg3rhsOJ9M2VyGWQSvlH2JeyEzb9Ap3ugau7umqOnU/g1+iDXtQ2klq93HgcwsgRU9aZ3iwB+W3eQjExb7goi0P8VfRPW6k8dH6BRLCbhG2Xfkv8Dz8p5zncP8Maf28lUikf6NnVwYGXT0Ij6JJxJZemufOa4atQDml8NyybCWTMPVlliEr5Rth2NgZjfoPP9UKVWruKouBP8vv4g917ZmIa1qjghwLKnT2htalbxKniqhav+pxdGWfJ/jgvMKDaT8I2ybfHreoGOrg/lKsq0KV6aGUO9aj482NvcKGQvLw83rg8PYuHWY5w8l5Z3pYDm+gJ59CRI2OHQ+IzLZxK+UXYd2qBvArpiLFSqkav4p7X7iTl0mmevCaOyV6E3lRvZDI0IJi3TxowNBcx72GsceFWB+S84LjCjWEzCN8quxa+DT3Xocn+uolPJabz91w46N6rJoLb18tjZKEjLQD9aBfoVvPxhFX+48nHY9ZdZ87aMMAnfKJsOrNWJptsj4FMtV/HEBTtJOp/O+MGtEDFTIF+OYRHBxBw6zdZDp/Ov1Pl+qNYA/noebGaORFdnEr5RNi1+DSr7Q6f7chVtO3ya71fv4/YuDc2cOcUwJDwIT3cp+OKtpw/0ewmOboaNUx0XnHFZTMI3yp59KyF2MXT/j16NKRul9IXaapU8+e9VuefCN+xXo4oX/cLq8MeGg6Rl5DEmP0vrmyAoEha9Amn5rJpluAST8I2yRSn4+zXwrQORd+Uqnr3pMGv2nuCJAS3MfPclYFhkMCfOpfH39mP5VxKBAa/B2SOw8iPHBWcUmUn4Rtmy9x/Yt1xfLPSqnKMoOS2D1+duo1WgH8M75p4t0yi6Hs0CqF3Vu+BuHYAGXSBssJ5y4cwRxwRnFJlJ+EbZkdW69wuCDqNyFX+yeA+Hk1J4eXArs1ZtCfFwd+OGDkEs3nGMhDOpBVe+6mW90tjfrzomOKPITMI3yo7dCyF+jZ7+2NMnR9G+xHN8sTSWG9oHERlS00kBlk/DIoLJtCn+WF/AmHyAmo2h072w/ns4sqXguoZTmIRvlA1K6ZE51RtA+O25il+ZvQ1Pd2Hc1aFOCK58a1q7KuH1qzMt+gBK5bPIeZYeT+hhsvOf138zw6WYhG+UDTvmwqH10PNp8Mh5MXbJjmMs3HaUh/s2o46fTz4HMIpjWGQwO4+eZVN8UsEVK9fUf6PYxfoTmeFSTMI3XJ/Npu+qrdkE2g7PUZSWYeN/s7bSyL8Ko7uFOCe+CmBQ20C8PdwKv3gL0PFu3b0z/3nIzCj94Ay7mYRvuL5tM+DoFj13i3vOOXG+XbGX2OPnePG6lnh7uDspwPKvWiVPBrSqy4wNB0lJL+SOWg8v6PcyJGyH9d85JkDDLibhG67NlgmL/w8CQvUNPtkcO53CB4t20Te0Nr1b1HZSgBXHsMhgTqdksGCrHevZhl0HDbrqT2YpBUzNYDiUSfiGa9vyKxzfoVv3bjlb8G/8uZ30TMULg1o6KbiKpWsTfwKr+RQ8oVoWERjwKpxLgBXvlX5whl1MwjdcV2YGLHkD6rSGsCE5iqL3neC39Qe5+8pGhPibhU0cwd1NuCkimOW7EjiSlFL4DkER0GYYrPoYkuz4J2GUOpPwDde16Wc4sQd6PwtuF1+qWQub1PXzYWxvs2yhI93UIRibgq+Wxdq3Q98X9fDMRa+UbmCGXUzCN1xTZjr88yYEtocW1+Qo+iXqAFsOnuaZa0Kp4m0WNnGkEP8qDO9Yn6+W72V+jB1TKFRvAF0egE0/6WG1hlOZhG+4pvXfw6l90Ps53R9sSUpO562/dtAppCaD2wU6McCKa/zgVrQNrsZ/f9nInoSzhe9w5X+hci09Z765GcupTMI3XE9GKix9G4I7QdN+OYomLtjBqeQ0s7CJE/l4uvPp7RF4ebhx/5RozqYWMtbepxr0ekZPerdjrmOCNPJkV8IXkTgR2SwiG0QkytpWU0QWiMgu63sNa7uIyAcisltENolIh9J8AEY5FD0ZTsdDn5yt++1HTjNl9T5u69yQloFmYRNnCqpeiQ9vbc+ehLM8NX1j4VMuRIwG/+aw4EXdXWc4RVFa+L2VUuFKqUjr93HAIqVUM2CR9TvA1UAz6+te4NOSCtaoANLPw7J3oGF3aNTzwmalFC/NiMHPLGziMro19efpgaHM3XyEL5YWchHX3QOuegUSd0PUN44J0MilOF06Q4DJ1s+Tgeuzbf9OaauB6iJiVpE27LP2a72QxiWt+zmbD/Pv3hM80b8FNaqYhU1cxb09GnNNm7q8OW87K3cfL7hy8wHQqIceanv+lGMCNHKwN+ErYL6IRIvIvda2Okqpw9bPR4A61s9BwIFs+8Zb23IQkXtFJEpEohISEi4jdKPcST0Ly9+Fxr2hYdcLm5PTMnhtzjZa1vPj1k5mYRNXIiJMGNqOxgG+PDR1PQdPnS+oMvR/Dc6fhGVvOy5I4wJ7E353pVQHdHfNWBHpkb1Q6Q68Il1+V0p9oZSKVEpFBgQEFGVXo7xa8wUkH9cjc7L5dIm1sMkQs7CJK/L19uDzkRGkZdh44PvogufaqdcWwkfAv5/DyTiHxWhodiV8pdRB6/sx4HegE3A0q6vG+p616OVBoH623YOtbYaRv5TTsPIDaNYf6ne8sHl/YjKfL41lSHggHc3CJi6rSYAv79zcjk3xSbw0I6bgyn2eBzcPWDjeIbEZFxWa8EWkiohUzfoZ6A9sAWYCWevMjQJmWD/PBO6wRut0AZKydf0YRt5Wf6o/6vd+NsfmV+ZsxcNNeObqMCcFZthrQKu6PNS7KT9HHWDqmv35V/QLhK4PQ8zvcGCN4wI07Grh1wGWi8hGYA0wRyk1D3gDuEpEdgH9rN8B5gKxwG7gS+DBEo/aKF/On9TzrYQO0nfWWv7ZmcCCrUd5qE9T6lYzC5uUBY9d1ZwezQN4aUYM6/efzL9i10fAtw789Zy5GcuBCk34SqlYpVQ766uVUuo1a3uiUqqvUqqZUqqfUuqEtV0ppcYqpZoopdoopaJK+0EYZdzKjyA1Sd+cY0nLsPHyrBga+Vfhru6NnBicURTubsL7t4RT28+bB75fl//C596++lpN/BrY+odjg6zAzJ22hnOdS4R/P4NWN0Dd1hc2T1q5l9iEc7w4yCxsUtbUqOLFZ7dHcDI5jYenriMj05Z3xfa3Q+1WsOAlfXe1UepMwjeca8V7kJ6co3V/7HQK7y/cRZ/Q2vQONQublEWtg6rxfze2YXXsCd6ctz3vSm7u0P8VPWfSmi8cG2AFZRK+4TxnjsKaL/Wc6QEtLmx+Y55e2ORFs7BJmXZjh2DuuKIhXy7by6yNh/Ku1LSvni9p6VuQfMKxAVZAJuEbzrP8XchMg55PX9gUve8kv607yF1mYZNy4flrWxLRsAZPTd/EjiNn8q7U/1VIPaOnwzZKlUn4hnMkHdRzqoTfCrWaAHphk/EzY6jj581DZmGTcsHLw41PbuuAr48H902JIul8HhOn1Q6DDnfA2q/g+G7HB1mBmIRvON654zBjLCgb9HjqwuZpUQfYfDCJZ68JMwublCN1/Hz45LYOxJ88z+O/bMBmy2MYZu/nwMMHFr7k+AArEJPwDcfa8Sd80gX2rYCr34QaDQG9sMmEv3bQMaSGWdikHOoYUpPnrw1j4bZjfLQ4j1a8b23o/h/YPhviljs+wArCJHzDMVLPwIyHYOpw8K0L9y6BjnddKH534U6zsEk5N6prCDe0D+LdhTtZvP1Y7gpdxoJfEMx/Hmz5DOU0isUkfKP07VsJn3aFDT9A98fgnkVQp9WF4h1HzjBl9T5GdG5Aq8BqTgzUKE0iwus3tCG0rh+P/rSefYnnclbwqgx9XtBr326Z7pwgyzmT8I3Sk5EK81+Ab68BcYPRf0K/8eDhfaGKUvpCbVUfDx6/qkW+hzLKh0pe7nx+ewQiwn1TojmfdsnMmm1vgXrtYOHLejEco0SZhG+UjiOb4YveegbMiFFw/wpo0CVXtbmbj7AqNpHHzcImFUaDWpV5f3g4O46eYdxvm3Iuj+jmpufMPx0Pqz9xXpDllEn4RsmyZcKyiTrZn0uAEb/Ade/ruVMucT4tk9fmbCWsnh8jzMImFUqvFrX5b7/mzNhwiEkr43IWNroSWlwDy96Fs2ZxpJJkEr5Rck7E6u6bRS9Di6vhwdV6Wbt8fLpkN4eSUnh5sFnYpCIa27sp/cLq8NqcbazZe8ldtlf9DzLOw5LXnRNcOWUSvlF8SkH0JPi0OxzbBjd8ATd/B1Vq5bvLgRPJfLY0lsHtAunUyCxsUhG5uQkTb2lH/ZqVefCHdRw9nXKx0L8ZRI7Rr6tj+czFYxSZSfhG8Zw5Cj/eArMeheAIeHAltLslxwLkWU4lpzFjw0Eembqeaz9Yhoeb8Ow1ZmGTiszPx5PPR0aQnJbBA99Hk5aRbThmz3HgVRUWvOC8AMsZczujq8pMh2l3gpcvtBwMTfqAZyVnR5XT1hkw6z96tsuBb0Kne/VFN4tSit3HzrJo+zH+3naMqH0nsCnw9/ViQKu6DO9U3yxsYtC8TlUmDG3LQz+u55XZW3nlemua7Cq1oMfjsOBF2DEPWgx0bqDlgEn4rmrDD/quQy9f2PST/t6sv07+zfqDlxMnFjt/Cv58WsdVLxxu/OLCbJepGZn8G3uCv7cfY9H2oxw4oYfWtQr046HeTekTVoe2QdVwM332RjaD2gayKT6JL5bG0q5+dYZGBOuCzg/Axp9g9n+gwWqoVN25gZZxolxgebHIyEgVFWUWxrogPQU+7KDX/rxzLsQt063p7XMg+Th4VNLTyra8Xl8U9fFzXGyx/8AfD8KZw9DjCejxJMeSM1myPYFF24+ybNdxktMy8fF0o3tTf/qE1qF3aAD1qrnYpxPD5WRk2hj59RrW7T/Jrw90pXWQdRPewWj4qp9eMGXwh84N0sWISLRSKtLu+ibhu6BVn8Bfz8AdM6Fxz4vbMzNg/yqd/LfNgrNHwN1Ld/eEDdYjYyqX0gXQ9PP6Zph/P0XVakps94nMTgzk7+1H2RifBEBgNR/6hNWmb2gdrmhSCx9Ps1KVUTTHz6Zy3YfLcRNh9sPdL96bMf8FfU/HyD+gSW/nBulCTMIv61LPwvvtoE5LGDUr/3o2G8SvtZL/TEg6AG4e0KiHTv6hg8A3oGRiOrQe26/34pa4k5W1bmTc6aHsP6Ovy7avX52+YXXoE1qb0LpVzTw4RrFtOHCKmz9bRefGNZk0upMespt+Hj7tBrZ0eGBVnvd1VEQm4Zd1S9+Gv1+BuxZC/Y727aMUHFoHW2fq5H8iVk9l0LCbTv5h14FfvSKHEp94muNzX6f1ni84rqrxRPp9bPBsT4/muqumV4sA/H29Cz+QYRTRT2v2M+63zYzt3YQnB4TqjftWwrdXQ+f79Uyrhkn4Zdr5k/BeO2jYFUb8dHnHUAqOxlxs+SdYY5jrd9bJv+VgqJ73Xa2ZNsWGA6f4e/tRtm9Zz8NJbxHutocF7j1Y3/o5urVpSseQmnh5mNG8Rukb9+smflp7gM9uj2Bg67p649wn9bKYY+blOVVHRWMSflm26H+w7B0970zd1iVzzIQdVst/hp7fBiCwPbQcov8BWKtNrdh9nEd/Ws/xs6mM8ljIs54/oty9OdXnTepccavpqjEcLiU9k1s+X8WehHP89VgPgqpX0l2en1wBHl5w/3LXG6rsYCbhl1Vnj8H74Xqs8dBvSuccJ2IvdvscjNbb6rTmWP0B3L02iMpVa/Bh5S8JOLZSLyw9+KPL6goyjJJy4EQyfd/5h+vbBzJhaDu9cc/fMOUG6PYfuOpl5wboZEVN+GYcvqtYNhEyUqDXs6V3jpqN9apC3f8Dpw7Atlmkbv4D/6iJzBSFSvZAUr3g2on6tnbTqjecrH7NytzWpQGTV8ZxX88mNAnw1aPS2o+ElR/qT6pBHZwdZplhOmNdwakDEPU1hI8Afwct3l29Pknh93Dduefoqz7jSPdXkYg79cfkjneZZG+4jAd7NcXH0513F+y8uLH/q3pZxBkPQUaa84IrY0zCdwVLJ+jvPZ922ClTMzK5b0oUe4+f47WR/ajb72G49p0LffqG4SoCqnozulsIszcdJuaQvueDStX1J9FjMbB8onMDLENMwne2xD2w/geIGA3V6zvklDab4olpm1gde4K3hraja1N/h5zXMC7XvVc2wc/Hg4nzs7XyQ6+B1kP1UOajMc4LrgyxO+GLiLuIrBeR2dbvk0Rkr4hssL7Cre0iIh+IyG4R2SQipoOtIEv+Ty/5d+XjDjvlm39tZ9bGQzw1sAXXtw9y2HkN43JVq+zJfT2bsGj7MaL3nbxYcPUE8KkGM8bqO9GNAhWlhf8osO2SbU8qpcKtrw3WtquBZtbXvcCnxQ+znDoaA5unQ+f7oGodh5zyu1VxfP5PLLd3acADPU33jVF23Nk1BH9fL976a/vFZRGr1IJrJuiFz1d/7NwAywC7Er6IBAPXAl/ZUX0I8J3SVgPVRcSM7cvL36+Bd1Xo+ohDTvdXzBFemhlDv7A6vDy4tRlbb5QpVbw9GNu7KatjT7Bid+LFglY36qlEFr8Ox3c7L8AywN4W/nvAU4Dtku2vWd0274pI1j32QcCBbHXirW05iMi9IhIlIlEJCRVw3cr4KNgxRyf70prwLJvofSd5ZOp62gVX58Nb25slBY0yaUTnBgRW88nZyhfRAw48vGHmQ3qeqbIgIw1+v1/fV+AghSZ8ERkEHFNKRV9S9AwQCnQEagJFGmKilPpCKRWplIoMCCihSb7Kkr9fgcq1oMv9pX6qvcfPcffktdSt5sPXoyKp5GVmsTTKJm8Pdx7t14yN8Uks2Hr0YkHVujDg//Rssmvt6YhwsrRk+GkEbJyqlwV1EHta+N2AwSISB/wE9BGR75VSh61um1TgW6CTVf8gkH24SbC1zciydynELoHu/9VdOqXo+NlU7vx2DSLC5NGdqGUmOzPKuJs6BNPIvwrvzN9Jpi3bTAHhI6BJX1g4Hk7uc1p8hTp/Cr6/EXYvhOvehyvGOuzUhSZ8pdQzSqlgpVQIMBz4Wyl1e1a/vOiO4OuBLdYuM4E7rNE6XYAkpdTh0gm/DFIKFr0CVQP1DU6lKDktg7smreXo6RS+HhVJiL8TV8kyjBLi4e7GY1c1Z8fRM8zedOhigYhOoCIw6xH9XnM1Z4/B5EG6S3fYtxBxp0NPX5xx+D+IyGZgM+APvGptnwvEAruBL4EHixVhebNrPsSvgZ5PlurETxmZNh7+cT2bDybx4a0daN+gRqmdyzAcbVCbeoTWrcrEBTtJz8zWZ1+9vp5fJ3YJrJ/itPjydGo/fDNA33sz4idodYPDQyhSwldKLVFKDbJ+7qOUaqOUaq2Uul0pddbarpRSY5VSTazyCj4rWjY2m+67rxGi5wIpJUopXpoZw6Ltx3h5cCuuaumYIZ+G4ShubsIT/VuwLzGZ6dHxOQsjxui1IP56Hk4fyvsAjpawA74eAMmJetWupv2cEoa509aRtv6hpyju9Sy4e5baaT79Zw8//Luf+3s2YeQVIaV2HsNwpr5htWnfoDofLNpFSnrmxQI3N732bWYazP6v87t2Dq6DbwaCLUOvUd2gs9NCMQnfUTIz9DjhgFBoM7TUTvP7+ngmzNvBkPBAnhrQotTOYxjOJiI82b8Fh5NS+OHf/TkLazWBPs/Bzj9hy6/OCRBg7zKYfJ1eknHMvJJb5+IymYTvKJt+gsRd0Od5cCudYZErdh/nqembuKJxLSYMbYubGWtvlHNdm/rTrWktPlm8m3Opl0yt0OVBCIrQq2SddcK9Ptvnwvc3QbVgGPOXS0xMaBK+I2SkwpI39UpToYNK5RTbDp/m/inRNPKvwmcjI/D2MGPtjYrhif4tSDyXxrcr9uYscHOHIR9D2ln48ynHBrVhKvx8u27Rj/4T/AIde/58mITvCOu+g6T9unVfCtMZHE46z+hv11LZ251JoztRrVLpXR8wDFfTvkEN+oXV4fOlsZxKvmRu/Nph0OMpiPkNts12TECrP4M/7oeQbnDHDIfcSW8vk/BLW1oyLH1Ljxpo0rfED386JZ07v1nL2dQMJo3uRGD1ir3Gp1ExPd6/OWdTM/h8aWzuwu7/gbptYM5/4fzJ3OUlRSlY8gbMe1p/kh8xrdRvrCwqk/BL25ov4OxR6PNCibfu0zJs3PddNHsSzvL5yAjC6vmV6PENo6wIq+fHdW0DmbQijmNnUnIWunvqrp1zx+Gv50onAJsN5o3T052H3wbDJoOnT6G7RcWdIDnNcdM6m4RfmlKSYMV7esxtwytK9NA2m+Kp6RtZFZvIhKFt6WYWMTEquMeuak5apo1PFu/JXVivnW7pb/gBdi0s2RNnpsMfD8C/n0GXsTD4I3AvfLnw6H0nue2rf3ltjmvNpWNcrlUf64+QfZ4v8UO/NX8Hf2w4xJMDWnBjh+ASP75hlDWN/KswLCKYH//dT/zJ5NwVejwF/s1h1qOQcrpkTpqeAr/coUfh9XkeBrym7wMoRGzCWe6evJZ61Xz471XNSyYWO5iEX1rOJeqEHzZYj84pQVNW7+PTJXsY0bkBD/Zy/lAvw3AVj/RtBsAHi3blLvT00V07pw/qCdaKK+U0/DAUdsyFa96GHk/a1W2rJzRci4gwycETGpqEX1qWT4S0c9C7ZPsMF2w9ykszttA3tDb/G9zKLGJiGNkEVq/EbV0a8Ou6g+xJOJu7Qv1O0OUBiPoa4pZf/onOJcJ3g2HfSrjxS+h0j127ZU1oeOyMcyY0NAm/NJw+rOfkbjccaoeW2GHX7z/Jw1PX0SaoGh+OaI+Hu/nzGcalHuzVFG8PN95dsDPvCn2e1/NZzXhIj6IrqqR4+Hagnsd++I/Q9ma7dnOFCQ1NxigNS9/S82b0LNKaMAWKO36OuyZHUbuqD1+N6khlr8IvChlGRRRQ1Zsx3Roxe9Nhth7Ko6/eq4qea+fkXlj8WtEOfny3nhfnzBG4/TdoMdCu3ZRSvJg1oeGQ1k6b0NAk/JJ2Yi+smwwdRkHNRiVyyERrEROlFJNGdySgqlnExDAKck+Pxvj5ePDO/B15V2jUAyJGw+pP4MBa+w56eJNu2aefh1Gz9I1VdvpkyR5+/Hc/D/RqwsguDe3er6SV/YSfmkc/nTP9ZWNiBwAAE0FJREFU8ya4eegLOCXgfFomd02O4nBSCl+N6kjjAN8SOa5hlGfVKnlyX88mLNp+jOh9+dxsddX/oGo9mDFWT39SkH2rYNK14O6tJ0ELDLc7lt/WxfPWXzu4PjyQJ/s7d0LDsp3w9yyG91rr/nJbZuH1S9ux7bDxJ+h4N/jVK/bhMm2Kh6euZ2P8Kd4f3p6IhmYRE8Ow1+huIfj7euVc8Dw7Hz+9QtbxHbobNj8758OUG8C3tk72/s3sjmH5Lj2hYdcmtZgwtJ3TJzQs2wm/al2o0xrmPA6f94S4Fc6NZ/Frun+w+3+LfahMm+KFGVtYuO0o469rxcDWdUsgQMOoOCp7eTC2d1NWx55gxe7EvCs1uwra3QrLJuoum0ttng4/3aqT/Oh5ekUtO209dJr7v4+mSYAvn42MwMvD+enW+REUR+0wjt0wDYZN0jc4TboGpo/RV9Ed7dB62DZTL0hcpVaxDpVwJpVR36zhx3/3c1/PxozqGlIyMRpGBTOicwMCq/nw1vwdebfyAQa8DpVr6a6dzPSL29d+Bb/eDfU7w52zwTfA7vMeOnWe0ZPW4OvtwbejO+Ln4xoTGpbphP/n5sP0eHsJU850QD20Ro+K2TYbPuqoP6KlpxR+kJLy96vgU73YK9Cvjk3k2g+WsTbuBG/e1IZxA0tuWKdhVDTeHu482q8ZGw+cYsHWo3lXqlwTrn0HjmyCFe/rSdCWvq17DpoPgNt/BZ9qdp8z6Xw6d367huTUTCaN6ehSExqW6YQf0bAGnRrV4oU/tnDXj1tJiHwcHloDTfvqBPxxJ9g+p/SXONu3CnYvhO6PFemFkZ3Npvh48W5GfLmaKt4e/DG2G7d0bGBurDKMYrqpQzCN/Kvwzvyd2Gz55IKWg6HlED3oYsZYvfZ0m5vhlu/B0/6EnZqRyX1Toth7/Byfj4wgtK5rTWhYphN+bT8fJo/uyMuDW7Fi93EGvreUBYcr6T/SyD/0H+qnEfD9jXoR4dKgFCz6H/jWgU73XtYhTpxLY8zktbz11w6ubRvIrIe7m5kvDaOEeLi78dhVzdlx9AyzNhWwqPk1b+trcP/f3plHV1GkffipJKyyE0AgKFsiKBojYZEQYECDiKiMOMjnBgEcRInooMDM6DcOxzkin4JBERwZQEBRGGVARiIiArITdjCBAJGdQIBA2LJQ3x9VVy4xIYF7+3KTvM85ObldXd2/fqur365+q7tq00xoNRB6TrqmuacvXdK8OnsLq/ec4J1ed9HODwc0LNYOH8y8ls+2a8g3Q9pTp0p5Bn66npFfbeFsSDQM+gkeGA0HEuGjdrDwz2YES2+yezHsWwnRw6BsxWvePPGXE3SPX87KlHRGPdqC+CfuplI5+ahKELzJQ3fWpdnNlRm7aCfZuZfyz1Sptvlytsf78OCYIg2C5s47CcnM22wGNOwZ4Z8DGhZ7h+8itE5l5r4QxfOdmjBr3X66xy9n48FMaDsI4jaYMapXT4DxLWHDdDN+tadoDYtHQdVboOWz17ip5p/L9tB70mrKBAbw7+fb8XTbWyWEIwgOEBCgGBZzG6np55iTeJWXOm5tBy37XvPcFdNXpTJx6W6e9PMBDUuMwwcoGxTA8AeaMWtgW7JzNb0mrmLc9zvJKV8DHo6H55ZA9UYw70X4pEvRv7AriJ/nw+FN0Gk4BBX969eMc9kM/DSRt/77M12a12b+kPbcGXJ9sX9BEIpGl+a1ibilGvGLd3Eh23vf7Xy3/Qj/O2879zWvzZt+PqBhiXL4Lto0rsm3Q6N5JLwe477fRa+Jq0g9ftYMU9z/O+j5MZw+BJPvg6+fhzMF9N5fjUu55r37mqFw1xNF3mzz/lN0H7+cH5PTeOOh25n4VEuZg1YQfIBSildjbuNwxgVmrtnnlX1u2HeSuFkbubN+VeL7+P+Ahv59dB5QpXwZ3ut9N+P7RLDnWCYPxi9n1tp9aIDw3jBkPUQNha2zTZhnRTzkZBW228tsnQPHkuB3fy7S7DZaa6au2EuviSvRGmYPupfY9o38ujUgCCWNdk2DiWpakwlLUjh70bOpBVOPn2WAHdBwct/iMaBhiXX4LnqE1yPh5Q5E3FKNEV9t5bnpiaRnXjSTC9//JrywxsTtFr0OH90LuxYVvtPcbPjxH1DnTrj90UKzn76QzQufbeBv83fQIbQWC+La35ChUQVBgGExt5F+NospK/Ze9z7SMy/yrB3QcFpsa4J9OImJJ5R4hw9Qt2oFpse24a/dm7M0+Rhdxy1nSVKaWVmzCTz5pZlhXmszg81nvSE9n3kxXWycDidTocvrhfbkbzuYQY/xP5Gw/SgjuzXjn89EUq1iWe8ZJwjCNRFxS3Xua16HScv2kHEuu/AN8nA+K5fYaes5knGByX1b0cjHk5h4QpEdvlIqUCm1USn1jV1upJRao5RKUUp9oZQqa9PL2eUUu76hM4d+bQQEKAZEN2bekCiCK5Wl39R1vD53G+ezbOdNWAwMXm1G0Ev9CSa0NdOg5R2NM/s8LB0DIa0hNKZAPa01M9f8wu8/WsnF7EvMeq4tf+zY5IYPniQIAvwpJozMizlMWnaVhl0+5OReYsjnG9hy4BTxfSK4p5g9qV9LC/8lwH169dHAWK11U+Ak0N+m9wdO2vSxNp/f0OzmKsx9IYoB7RsxffUvdB+/nK0H7Lv5QWUh6iUYkggtHoOfxsIHkbDly8tf666bDGcOmdZ9AfH3sxdzGPrFJv7y9TbaNq7Jgrj2tGpYw0cWCoJQGM3rVqHHXfWYsiKVtDNFG4JFa83f5m/n+5/TePPhO+h6R/Eb0LBIDl8pFQJ0Bz6xywroDMyxWaYBrmD2I3YZu76L8rOeyfJlAvnrQ7czc0Abzl3MpeeEFXy4JIVc12fXlW+GnhOh/yLzBe1XA80sN6krzFy1jTuZCRTyIenIaXp88BPzNx9iWEwYU/u28ukkxYIgFI2X7w8jK/cSE5YUrZU/cekeZqw2Axo+c29DZw/OIYrawh8HvAa4vlaqCZzSWru6uQ8A9e3v+sB+ALs+w+a/AqXUc0qp9Uqp9ceOHbvOw/eMqKbBJAztQNcWNzMmIZnek1ax/4TbHJcNWsPAJWY6tPQUMxrnuXTo/Ea++/ty/X4e/XAFZy7kMGNAG17sHCohHEHwUxoF38TjLUP4bM0+Dp46f9W8czceZPTCJB4Or8fwrsV3QMNCHb5S6iEgTWud6E1hrfXHWutIrXVkrVpFH3bU21StWIYP+kQwtnc4yUfO0O395cxJPHB5KNWAALjnGRPmaRdnhlAIaXnFPs5n5TJs9mZem7OFiAbVWRDXnnZN/G8cDUEQriSui5nMJP77XQXmWZlynFfnbKZt4xqMefyuYt2IK8qLo1HAw0qpB4HyQBXgfaCaUirItuJDgIM2/0GgAXBAKRUEVAUKmH3AP1BK0TMihFYNa/DKl5sZNnszPyQd5a1H76T6TfaNmgrVIGbUb7ZNSTvD4Jkb2JWWSVyXUF7qEkpgMa4QglCaqFetAk+1vZVpq1J5rmNjmuSZQjTpyGn+OD2RRsE3MenpSMoFBd6YA/UShbbwtdYjtdYhWuuGwBPAD1rrJ4ElQC+b7VngP/b3PLuMXf+DLnDmAf8ipHpFPh/YlhHdmrFox1G6jlvGsp0Fh5vmbjzIwx+sID0zi2n9WvPK/WHi7AWhmDH4d00oFxTA2EU7r0g/nHGeflPWUbFcIFP6tS4RX8R78h7+cOAVpVQKJkY/2aZPBmra9FeAEZ4dom8JDFAM6tiErwdHUaVCGZ7511renL/9irE3LmTnMvKrrQz9YhMt6lVlQVw0HcJuXFhKEITrJ7hSOWKjGvHNlsPsOHQaMB9L9puyjjMXcpjStzX1/WgSE09Q/tD4joyM1OvXr7/Rh/EbLmTn8va3SUxdmUpYnUqM6x1BhbKBDJ65gZ8Pn+b5Tk340/1hfj9+hiAIVyfjfDbRo3+gVcMafPRUS/pOWcvavSeY2q817UP9tz9OKZWotY4scn5x+IWzdOcxhs3ezKlzWZQNDKBMUADv/SGczs3q3OhDEwTBS3y4JIUxCcm0aVSDNXtP8O7j4TzW0j/HtXdxrQ5fmqZFoGNYLRKGduCBFnUJb1CNBXHR4uwFoYTRL6ohwZXKsmbvCYbFhPm9s78epIUvCIJgWb7rGMlHztC/mIxke60tfP8fz1MQBMFHRIfWIjq05L6AISEdQRCEUoI4fEEQhFKCOHxBEIRSgjh8QRCEUoI4fEEQhFKCOHxBEIRSgjh8QRCEUoI4fEEQhFKCX3xpq5Q6BvxynZsHA8e9eDilWa8k2+ZrvZJsm6/1SrJtnurdqrUu8pdifuHwPUEptf5aPi0WPf/QKul6Jdk2X+uVZNt8rSchHUEQhFKCOHxBEIRSQklw+B+LXrHUKul6Jdk2X+uVZNt8qlfsY/iCIAhC0SgJLXxBEAShCIjDFwRBKC1orX3+B/wLSAO2uaWFA6uArcB8oIrburvsuu12fXmb3tIupwDx2BBVPlqngIs23wg3rYPAWUADwZ5q2XwJQI7VG+Fm2zGbdhqYDpRxWO8ocN7qzQUqOVyWrvM2Ech02LY0IAvIALYAdzust8pqZgLJQJzDZXnWzbZD9vw5Zdt2W08y7P6bOlyWLr3TwGdAkJfK8rzV25bnvO3AXHu7gUVAdS/YthO4BFzI47+SgQsYf9LRG/6rIH9p08cASbaefA1UK9T3+sLB52NAB+AernT461yFBMQCo+zvIGtQuF2uCQTa32uBtoACvgW65aPVCdhvT0ZZYLMt5I5ABPAq5sIL9oJWIOYm0sOe3M3A7da21+y2scA24HmH9bq5leVqjENxrCzt+r/b/Wc6bNu3QC8v15Or6b0NfAr0B0YBtX1QlrFW699AXwdt2w80t3r/BaY6XJZpQJjVW2bL1FO9DnY/KVx2+Osw1/g7wBxbliPs8nVr2XwvAv/DlQ5/HfA0cBvGCX/ijXpZkL+06TFcvmGOBkYX6nt96ejzHGxDrnT4GVzuRG4A7LC/HwRm5LN9XSDJbbkPMCmffPcCS90qwkjMXdhdKwvj8L2hleCyzWqNzMe2I8BbPtQ7AQx3siwxF/lKTOsn02HbpmIcvrfrSUF6a4GmDugVVi+TgJPAYw7algy0sXpHgX84WJangd1u9qVibjIe6bn5k2S38szA1MtkTEt6h93fAU+17Pr2XOnw3a+5VcAeb9STPPZtu8r6nsDMgta7/vwphr8deMT+fhxTIcC0BrRSKkEptUEp9ZpNr485eS4O2LS81AcO58l3Mo+Wa25fb2jtzyefu229gVrAQqf1lFJTMBW9CjDeS3oFleWLmNZbXbvOSdtuwdww1wBNlVLlHNZrhjlvK4EwpVSol/QKq5e3Aosx14JTtk3FON2tmEbP216yLT+9bUBlpVSkta+etc1TvfxwXXN1MC19V0OrpgNa7npgru869rcTtuVHLOYp4ar40yTmsUC8Uup1YB6m1Q3mGNsDrYBzwGKlVCLmjnq9LAYGu2lpB7XgStsAsrTWy5VSbZzUw1TudzGPsr1xrixfxrSi3gWigXIOaYGxbSLmKeILTKhsuNVwSm8bMAj4BBiGian+xwG9vPUyEPgcaOSAFhjblmJu1LMwZfke5snCKb1pwBK7rxwgF2fqiusaqARUxlxzWimlHdD6Vc+eu0Ag26Y7dR38ilLqL5iynFlYXr9p4Wutk7TWMVrrlphKvtuuOgAs01of11qfw7RG7sHECEPcdhECHFRKNVBKbbJ/g2y+unnybc+jleNFrQZ587lsw1zEJzCtKcf1rH2fAXsxYQFHyhITF83ExFErARUxjtEp2zpZ22ZY21o7WZaY2HAHTF35GdMJ53S9XIgJSSxwyjYgHdPB3tzatgto52RZaq3baK0rA52BfZgQoKd6v8HtmtsNfA/sVkrVxfTXeVXLXc+euzQut969bps7Sqm+wEPAk9rGdq5KYTEfp/74bQy/tv0fgOkgi7XL1YENGCcShDl53e26vJ0eD+ajE4SpWO6dY9F5tI5jHme9obUHc0d3dVbdgenkG4AJCcz0sm0F6TV1s28h8H8+KstYjPN30ra6bnrfYcIQTuq9jbmZfYrpHFvng7JcDaxwuJ7UxdT926xtUzCdxE6WZW1r3wxMuLGzp3pu/sQ9hu/yJ2PsvmMxT7rve6pl8+aN4bv7ryPA371x7grylzbtAVuGtYrsd2+Qs/8cE7/MxtwB+wMvYe72OzEXmHLL/5StONuAd9zSI23abuAD923yaJ3AhG1ygK/ctNIwHUk5mNffPvFEy+b70e5PYx7bXLZpTJjqKLAJeMNT266iNxTzmtpFzCtpM7GvuTpYlr+eNy6/lulUWZ5zs20Gl185dUovBXMTS8N0yIX7oCz3AQ944xooxLZDtiyP2jyNHdY7YfXSgaFevMbPWy1tNT635ZiC6RzehXG2Nbxg2y+YUJTr3E1xK8tsm3YUSPBSWf7GX9r0FExfySb7N7Ew3ytDKwiCIJQS/CaGLwiCIDiLOHxBEIRSgjh8QRCEUoI4fEEQhFKCOHxBEIRSgjh8QRCEUoI4fEEQhFLC/wMDP+IvXUdWEAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate forecasting Performance\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_prediction)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,test_prediction)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_prediction) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAv0DEtdv224",
        "outputId": "9ca95587-0f77-4c4c-87b1-46bc4a3a2794"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 20.848100067783903\n",
            "Mean Squared Error = 666.1677826108831\n",
            "Mean Absolute Percentage Error = 4.326709104479357\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ETS"
      ],
      "metadata": {
        "id": "yWZUcYHg_OX7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from statsmodels.tsa.exponential_smoothing.ets import ETSModel\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "test_prediction=ETSModel(train['Passengers'],error='mul', trend='mul',seasonal='mul',damped_trend=True, seasonal_periods=12).fit().forecast(12)\n",
        "test_prediction=pd.DataFrame(test_prediction)\n",
        "new_index = test.index\n",
        "test_prediction=test_prediction.set_index(new_index)\n",
        "test_prediction.columns=['Passengers']\n",
        "\n",
        "\n",
        "plt.plot(train['Passengers'],label='TRAIN')\n",
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using ETS');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "oB01fQd0zO9s",
        "outputId": "8845180e-4b9f-45a2-8a38-088379a0127d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  self._init_dates(dates, freq)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Test and Predicted Test using ETS');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "a-L-SXks_AM7",
        "outputId": "e7044ae1-e4ec-43f1-831e-955bba697241"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate forecasting Performance\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_prediction)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,test_prediction)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_prediction) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4QaW1DC-xwl",
        "outputId": "8e9246fa-42a0-4b3e-e62b-28caf4dd317f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 29.596242729299195\n",
            "Mean Squared Error = 1130.0278343125249\n",
            "Mean Absolute Percentage Error = 6.037987863663688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TBATS"
      ],
      "metadata": {
        "id": "B6pCzUSs_SiJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tbats import TBATS, BATS# Fit the model\n",
        "estimator = TBATS(\n",
        "    seasonal_periods=[12],\n",
        "    use_arma_errors=False,  # shall not try only models with ARMA\n",
        "    use_box_cox=False # will not use Box-Cox\n",
        ")\n",
        "model = estimator.fit(train)\n",
        "test_prediction = model.forecast(steps=12)\n",
        "\n",
        "test_prediction=pd.DataFrame(test_prediction)\n",
        "new_index = test.index\n",
        "test_prediction=test_prediction.set_index(new_index)\n",
        "test_prediction.columns=['Passengers']\n",
        "\n",
        "\n",
        "plt.plot(train['Passengers'],label='TRAIN')\n",
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Train, Test and Predicted Test using TBATS');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "3gPwnLdWAOyG",
        "outputId": "964c865a-d45a-4678-a7f9-47f35b8ce6d7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test['Passengers'],label='TEST')\n",
        "plt.plot(test_prediction,label='FORECAST')\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title('Test and Predicted Test using TBATS');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "m9FO9KOJCWSs",
        "outputId": "1d54e793-283e-4818-9da2-0c7778efe5df"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Evaluate forecasting Performance -TBATS\n",
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print(f'Mean Absolute Error = {mean_absolute_error(test,test_prediction)}')\n",
        "print(f'Mean Squared Error = {mean_squared_error(test,test_prediction)}')\n",
        "import numpy as np\n",
        "def mape(actual,pred):\n",
        "    return np.mean(np.abs((actual - pred) / actual)) * 100  \n",
        "print(f'Mean Absolute Percentage Error = {mape(np.array(test),np.array(test_prediction) )}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_gKvmEgCceD",
        "outputId": "32d6831a-8322-47e7-ce87-bc7a27440fbf"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error = 44.76525334905747\n",
            "Mean Squared Error = 3322.074712746387\n",
            "Mean Absolute Percentage Error = 8.533847014781305\n"
          ]
        }
      ]
    }
  ]
}