SARIMA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPunLf2L/tJIku4hx+MBUvx",
      "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/SARIMA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": 449
        },
        "id": "FyDGxIvGA9d6",
        "outputId": "137c6c06-9bcf-4ab2-c41f-46614b4d09e5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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yihJPU28u8VxwwQX49NNPsWDBAixcuBADBw7ESy+9hLlz58rHPPLII2hqasLdd9+Nuro6XHrppVi1apXHvijvvfce7rvvPkyZMgVKpRKzZ8/Gyy+/HLyrIiIi6oMaTK4hbYJYzdOrMygAcM011+Caa67x+rxCocDChQuxcOFCr8ckJydj2bJl/r41ERERdaC9HpRYbc8s8XCzQCIiol7CfYqs4CrxMEAhIiKiCBA9KPqo9ko8PasHhQEKERFRL9FuiceZQenVy4yJiIio+3I1ybpW8fTUZcYMUIiIiHqJhvYmyWp75jJjBihERES9hCjjeEyS7aHLjBmgEBER9RKiB6U3TJJlgEJERNRLNHTQJMsAhYiIiCJCNMl6jLp360GRJCki5xUIBihERES9gMlqg9lqB9D+qHubXYLJ+XxPwACFiIioF3AfxOYeoMRo1W7H9JwyDwMUIiKiXkAsMY7VqqBSKuTHVUoFojU9b5osAxQiIqJeoL0GWaEnTpNlgEJERNQLyDNQdG0DlDgxC6UHbRjIAIWIiKgXEBkU9xU8Qk9caswAhYiIqBdoNLUdcy/IS43Zg0JEREThJO9k3E6JJ7YHjrtngEJERBQE9c0WnGswRez9DR0GKGJYGwMUIiKiPsNsteP6177DVc+vh8G53DfcGtuZIiu4SjwMUIiIiPqMFfvKcOJcExpMVpytbYnIOTT6tMyYPShERER9giRJeHtTsfx5pLIUcgalg2XGzSzxEBER9Q3bT9Zi39l6+fNIDUMTk2TbW8UTw0FtREREfctb357w+DxSS3l9mSTLHhQiIqI+4FR1E9YeqgAADEiJARD5Ek+Hk2TZg0JERNT7LfnuJCQJuHxoGkb1SwAQyRKPD6t42INCRETUuxktNny04zQAYN6lA+XMRaQzKO1OkmWJh4iIqG+oMBjRZLYhSqPEZUNSXUt5I5Sl6HiSLEfdExER9Qn1LY5VM4nRWigUiohmKYwWG8w2O4D2m2S5mzEREVEfYWhx3Oz10Y6AIJKNqO59L3HadpYZc5IsERFR3yBG2idEO5pSYyM4a8S9vKNUKto8L87NYpNgsvaMMg8DFCIiogCIEo/euWomkvvdNHTQfwIAsVqV/OfmHtKHwgCFiIgoAIaW9jMoTebwBwANJu9TZAFArVJCp3bc8nvKNFkGKERERAEQJR69HKCIHpQIlni8BCiAK7vSUxplGaAQEREFwFXiEU2ykSvxdDRFVuhps1AYoBAREQXAtYon8k2yhlb9MO3pabNQGKAQEREFQM6gOAMU9wyKJElhPZeaZse5JMV2EKBoI1eCCgQDFCIiogB4W2ZslwCjxR7Wc6lrNgMAkmK0Xo+JZIYnEAxQiIiIAtC6rBKjcS3lDXcQUNPUeYAiMjzNEVhlFAgGKERERAGobzVJVqlURKyMUuvMoCTHeg9QYpznxgwKERFRL9a6xANEroxS0yR6UDov8bAHhYiIqJcyWmwwWx19Jnq3ACVSS41dPSjem2RZ4iEiIurlRP+JQuG5OV9sBIahSZLkUw9KjI4lHiIiol5NniIbpfHYnC9WDgLCl6VosdhgcmZzOupBieQguUAwQCEiIvKTawaK5+TWSAQBInuiVSvlRtj2iM0MmUEhIiLqpcQUWfcGWSAyjai1okE2RgOFQuH1uFj2oBAREfVu7iUed5FYxVPrw5A2ILKbGQaCAQoREZGf6r3sfROJEo8vM1AATpIlIiLq9cQqnjYlHrnPI3xlFHkFTycBCpcZExER9XLemmQjUUapbep8BgrASbJERES9nrcm2ciUeBzBUnInPSji3MxWOyy28G5mGAgGKERERH6Sm2S9rOIJZ5aiptm3Ek+M20C55jCWoALFAIWIiMhPnTbJhnGSrCjxdNYkq1UroVU5bvsNJkvIz6urGKAQEVGPYrLaUNlgjOg5tLdRIOA+ByX8TbKJnZR4AFcQU91oDuk5BYNfAcqTTz4JhULh8TF8+HD5eaPRiPnz5yMlJQVxcXGYPXs2KioqPF6jpKQEM2fORExMDNLT0/Hwww/Dau0ZDTtERBR5D364Fxc9sw5FlY0ROwfRg+KtSTacJZ46H3tQACBDrwMAVBgiG+D5wu8MysiRI1FWViZ/bNq0SX7ugQcewJdffomPPvoIGzZsQGlpKW644Qb5eZvNhpkzZ8JsNmPz5s3417/+haVLl+KJJ54IztUQEVGvVtlgxMp9ZbBLwOFyQ8TOo7vMQZEkya0HpeNVPACQoY8C0DMCFHXnh7T6ArUamZmZbR6vr6/H22+/jWXLluGqq64CACxZsgQjRozA999/j4suughr1qzBwYMH8dVXXyEjIwPjxo3D008/jf/7v//Dk08+Ca228+iPiIj6rv/94AhOgMg1etrtEho6KfE0m22w2yWPjQRDodlsg9m5UWBnk2QB9wDFFNLzCga/MyjHjh1DdnY2Bg0ahLlz56KkpAQAsHPnTlgsFkydOlU+dvjw4cjNzcWWLVsAAFu2bMHo0aORkZEhH1NYWAiDwYADBw54fU+TyQSDweDxQUREfc8Xe0vlP4ezEdVdo9kqB0mtV/GIDAoQnvPzdaNAITOh52RQ/ApQJk2ahKVLl2LVqlV47bXXUFxcjMsuuwwNDQ0oLy+HVqtFYmKix9dkZGSgvLwcAFBeXu4RnIjnxXPeLFq0CAkJCfJHTk6OP6dNRES9wOmaZuwuqZM/j9SeMmKKrFatRJTGMyjQqZVQObMm4WiUde8/6WijQCE93tGDUt4DAhS/SjwzZsyQ/zxmzBhMmjQJeXl5+PDDDxEdHR30kxMWLFiABx98UP7cYDAwSCEi6mPcsycA0BShke1yg2xU254PhUKBWK0KBqM1PBkUH2egCCKDUtkbSzzuEhMTMXToUBQVFSEzMxNmsxl1dXUex1RUVMg9K5mZmW1W9YjP2+trEXQ6HfR6vccHERH1LV86A5Qs5022OUIZFG9j7oVwNsr6OuZeED0oPSGD0qUApbGxEcePH0dWVhYmTJgAjUaDdevWyc8fOXIEJSUlKCgoAAAUFBRg3759qKyslI9Zu3Yt9Ho98vPzu3IqRETUix2raMDh8gZoVArMOr8fgPBuyOfO2wwUIZzTZH3dKFAQAUp9iwVGS/eeJutXgPLb3/4WGzZswMmTJ7F582Zcf/31UKlUmDNnDhISEjBv3jw8+OCD+Oabb7Bz50787Gc/Q0FBAS666CIAwLRp05Cfn4/bbrsNe/fuxerVq/HYY49h/vz50Ol0IblAIiLq+UR55/KhacgWGZQINckavCwxFmLCOKyt1lni8WUGCgDoo9SI0jhu/d29UdavHpQzZ85gzpw5qK6uRlpaGi699FJ8//33SEtLAwD89a9/hVKpxOzZs2EymVBYWIhXX31V/nqVSoXly5fj3nvvRUFBAWJjY3HHHXdg4cKFwb0qIiLqVf63rwwAcO3YbFhtjiU0kepBESUebxmUuDDuaFzrZw+KQqFAhj4Kp6qbUWEwIS8lNpSn1yV+BSjvv/9+h89HRUVh8eLFWLx4sddj8vLysGLFCn/eloiI+jC7XcLJqiYAwKSBKdhzug5ABFfxGNufIivEasNX4qltcgRLvvagAJADlO7eh8K9eIiIqFura7HIc0eSY7XyOPlILzP2VuIJZ5NsjY8bBboTfSiVDFCIiIgCV93oWBKbEK1xDiRzTWuNBEMnJZ7YcK7iESUeH3tQACDTuR9PeT0DFCIiooCdcwYoqXGOm3C497tpTaziaT1FVnCt4gljk2wAGZSKhu49C4UBChERdWvVjY6bcEqc4zd/MdI9UqPuvW0UKISrSVaSJLkHJdHPHhQAqGAGhYiIKHDVrTIoIkNhtNhhE80pYSQmyXY6ByXEAVST2QazzbFRYGAZFAYoREREAat2NoKmxDoyKKJJFohMFsVV4vGyiidMJSgxRVanViJa0/lGgUKmmCZbb4QkhT/A8xUDFCIi6taq5AyKI0DRqpRQOzfka47ANNnO56CEKUBx6z/xZaNAId3ZJGuy2uVsUHfEAIWIiLq1KrkHxVHGUCgUEetDsdjs8uohbz0o4WqSFUuME/1YwQMAURqV3LPSnWehMEAhIqJurXUPChC5lTxiiTEAxEd52ywwPE2yrgyK7w2yQka8sw+FAQoREVFg5B6UONeebeHc78admCIbp1NDrWr/FhquHpQaeYqsfxkUAMhI6P67GjNAISKibk0sM051C1BinSWecG8Y6Joi632nmHCNuq8NYIqskBHv+F5252myDFCIiKjbMlps8o0+xa3E4+rzCG+AIsoqCR1kLUT5yWS1w+pcBhxskiTh22PnAAA5STF+f71YaswMChERUQDECh6tSol4nStrEalx9+ec01fT4nVej4l1O89QlaB2nqrF3jP10KqVuH58P7+/XpR4Kgzdd5osAxQiImrDZpfwzeFK/HXtUdQ5swaRUO22gsd9KW2kNgwUY/fT4rwHKFq1Elpnf0qohrW9vakYADBrXLZH6ctXosTTnZtkvRfRiIiozzEYLXjv+xIs23YKp2taADhWq/ziskEROZ/qJs8ZKILIUnTHDArgCKDMzfaQBFCna5qx+kA5AGDepYH9vWQmdP9VPAxQiIhI9tsP92LNwQqPxyJ5E2s9A0UQTbJhz6D4HKCoUdtsCUmPzNLNJ2GXgMuGpGJYZnxAryF6UM41mGC12b2uSIqk7ndGREQUMYfKDQCAhwuH4ddThgAAapstHX1JSIkeFDHmXhA9KOEe1OZrgBKqOS0NRgs+2H4aAPDzSwcG/DqpcTooFYBdci3j7m4YoBAREQDAbpdQ7tzh9rpx2eiX6PgtuzaCNzDXEuNWGRRnD0q4R9370oMCAHrnGPz6luAGdx/uOINGkxXnpcXi8iFpAb+OSqmQg6zuWuZhgEJERACAqiYTLDYJCoWjBCBGqNdEtEm24x6UcC8z9jWDIgIqEWAFy9eHHeW32y7Kg1Lp+/477Ul3TpOt7KYreRigEBERAKCszvGbdHq8DhqVUh4AFtEMSpO3HpTwN8kaLTY0OCfJdhagiJKUKFEFy9laR+PyiCx9l19L7McT7CxPsDBAISIiAEBZvePml5UQDcA1Qj2yPShtx9wDiMhmge4zWTqaJAu4AqqqIGZQ7HYJpc4SXHZidJdfT+zGXMcAhYiIurMy+ebnSP2LDEp9iyVkE1E742qS9cygRGKzQPfyjvtMlvaIklR1EDMo1U1mmK12KBSuZcJdwQwKERH1CCJAERmUhGgNxH04Er9l2+0SapwlntYllUhsFigClNROyjuAWw9KEMtjIsMlSnBdJTIo9RHsMeoIAxQiIgIAlNaJEo/jt3OVUiHfxCLRh1LfYoHNLgFou2NvJDYL9HUFD+AqSQUzgyL+foJR3gGAxGhXhqw7YoBCREQA3Es8rhtgcgT7UMQU2YRoDbRqz9tVbAQzKJ01yAKuklQwe1BKnU3M2QnBCVDYg0JERD1CWasMCgAkOW+0NRHIoHibIgu4VvGYbXaYreHpj/ErQHFmUBpNVhgtwQmiXBmUrvefAEACe1CIiKi7s9klVDhvwFluv6EnOW9itRHoU6jyMgMFAGKcg9oAoCVMS439CVD0UWp5w8Bg9aGU1ge3xOPqQWGAQkRE3VRlgxE2uwS124RRwNX7EYkMircpsgCgUSnlsk+odgxuzZ8eFIVCIWd+gtWHIko8WUEq8XAVDxERdXvi5pehj4LKbUKpWGpcF4EMSrWXfXgEuVE2TEuN/cmgAO6zUIIVoDgyKP2C3CRb12KBJElBec1gYoBCRERuQ9o8+xtcPSjh/y27yssUWcG1YWDoSzySJMkBSrqvAYo8TbbrwZ3ZapczOEHrQXGWeGx2KSzfQ38xQCEiInnMfVar384j2oPS4L0HBQjvsLYGkxUmZzOut/NpzTWsrevfuwqDEZIE6NSuLQi6KkrjKpNFIkPWGQYoRETkasBsnUGJZA9Kk/ceFMDVKBuOAEVkT+J0akRrVZ0c7ZAaxB6Us24zUDqbYusrhcI156Y79qEwQCEiIlcGpVWA0i16ULxkLMK5YWCVn/0nQHB7UIK9xFhI7MYreRigEBGRqweldYkngnNQRGmk9T48Qqwzg9IYjgyKHyt4BNGDEoxlxq23IQgWZlCIiKhbk3fJTWjdg+IIDgxGKyxh3DDQaLGhwRl4eNv7xpVBCV+JJ7AMStcDlLNBHnMviKXG3XGaLAMUIqI+zmy1y2WIrFYlBI8NA8NYBhBNuRqVAvHOZtjWXD0ooS/xBBKgBHNHY7nEE4RdjN3pxbh7lniIiKi7EStEtCqlvPeOoFIq5D6FcPahiJKDI0BqvylUZFDC2SQbUIDSZIbd3rU5I6JHKOgZlHY2DPz396dw0TPr8Oyqw0F9L38xQCEi6uPEb+eZCVFQKtsGA5HoQxG/0Yvf8NsjbxgYhibZQHpQRIOxzS51uccj2DsZC64eFNff7ZmaZpQbjEHbQyhQDFCIiPo4VwNm++WDJHlH48hkULyJEZNku2kPilathD7KEUSJnZkDYTBa5H6coK/iaWfcvehHCtbE2kAxQCEi6uM624TONQslfH0K4oaZ6EsGpZuWeABXg29XGmVFeScxRiNPzw2WhHZ6UEKVrfEXAxQioj7O2wwUITk2/NNkDT5kUFwBSmhLETa7JC8V9jtAie36NFlXg2zwA4aE9jIode1vexBuDFCIiPo4ucTjLYPi7KWojUAPSocBSphKPLXNZtjsEhQK+D1mPhjD2s6GaEgb0DaDYrXZUWFgiYeIiLqBMi9j7gW5xBOJHpQY7wGBKHeEelCbKO8kx2ihUfl320wJwrj7sk5KcF0hSmgiY1XRYIJdcizv9nXPoVBhgEJE1MeVOzMomd5KPDHhz6D40iQrNgsM9aj7c51sWtgReUfjLnzvSkO0xBhwfX8bTFZYbfZOV3SFEwMUIqI+zG6X5N4SbzdgucQTxmFedb6s4gnTZoGBNsgCribZrmRQzoawJ8T9+2swWkPa7+IvBihERH1Yg9EKMUPMWzAQiSZZXzIo8qA2sw2S1LVBaB05WtkAAMhLifH7a1NjfR9332K2wdbOQLeztY6gIRQ9IWqVUp7UW9dsDmm2xl8MUIiI+rA654CuaI0KURpVu8ckxoR/UJvoiRBzOtojNgu02SWYrKHbJ2jfmXoAwJj+CX5/bYqP4+7L64249NmvceeSbR6PN5qscgblvLQ4v9/fF3q3DQNDtWtyIBigEBH1YaJsk9RBICB6UBrCuGGgb4PaXDNBQtWHYrdL2HfWEaCM7pfo99e7mmQ7Du6WbStBdZMZm4qqPJp+j1Y4sjfp8Tq51BZs7hsGhrIh118MUIiIIqC83oi3vj2BBmNkN2kTZZuOVsvoozVQhnHDQEmSfApQVEoFojSO21io+lBO1TSjwWiFTq3EkAz/Mxiir6fBZPU6Ot5is+P9bSUAAEkCjpQb5OeOljsClGGZ8X6/t68S3FbynK1rf1frSGCAQkQUAS9/fQx//N8h/P7T/RE9j3ofMigqpUIu84SjD6XRZJV7MToKUADXSp6mEM1CEdmT/Gy930uMAUAfpYZG5Yjuqr2UyNYdqkRlg6sEdLCsQf7zYRGgZIQuQJEzKM2WbjNFFmCAQkQUESfONQIAvtxbik3HqiJ2HiLgSOoggwK4bmLh6EMR2ROdWum1L0aI0YZ2muy+M3UAgNH9/O8/AQCFQiEvNfbWh7LMmT0Rg+cOlrplUJwlnqFhyKCU1rfI33v2oBAR9VFnnCszAOCJz/fDZI3MzrHyxNYOMihAeGeh+DJFVgj1hoE/nBH9J4EFKEDHfSgl1c3YePQcFArgvquGAAAOlrkClCPODMrwkAYojvM77MzcxOvUiI/q/HsfagxQiIjCzGqzy+Pl46PUOFHVhDc3nojIudTJGZSOb0jhnIXiyz48QlwINwy02yXsPytW8CQG/DqiD6W9cfcie3LZkDQUjswAABwuM8Bqs6Oq0YTqJjMUCmBIeugzKIecgVF3KO8ADFCIiMKu3GCEzS5Bq1LiqR+PBAC88nURTtc0h/1c6uRdgzsu8SSHsQel3oclxkJMEDcMfGfLSVy8aJ0clJyoakKT2YZojQrnpcUG/Loig3KuVYBittrx0Y7TAIC5k3KRlxKLGK0KJqsdJ6ub5OxJXnIMorUdl7q6QnyfRR9MdyjvAF0MUP785z9DoVDg/vvvlx8zGo2YP38+UlJSEBcXh9mzZ6OiosLj60pKSjBz5kzExMQgPT0dDz/8MKzW0G+XTUTUHYjyTnZiFK4/vx8uGpQMk9WOt74NfxZFZEQ6CwYSY8Pfg+JLBiWYGwau2FeG0nojnlt9BACw72wdAGBkth7qABpkhUy944Zf4cyaCd8VVaG6yYwMvQ5ThqdDpVTIpZyDZQ1ygDI0hA2yQNvvs7dNI8Mt4O/49u3b8cYbb2DMmDEejz/wwAP48ssv8dFHH2HDhg0oLS3FDTfcID9vs9kwc+ZMmM1mbN68Gf/617+wdOlSPPHEE4FfBRFRDyIClP5JMVAoFLj+/H4AHEtaw63OxybZ5DAOaxNZHb0PAUp8lJiC2vXSU22T4zU2Hj2HPafrXP0nAQxocydG1Je1ClBOVjcBACbkJckBUH62HoCjUVY0yIay/wRwbRgoRHoXYyGgAKWxsRFz587Fm2++iaSkJPnx+vp6vP3223jxxRdx1VVXYcKECViyZAk2b96M77//HgCwZs0aHDx4EO+++y7GjRuHGTNm4Omnn8bixYthNodvSiERUaSclQMUx40gPd5xA6s0BL5fS6DqfMygyBNRw5hB6azsBAAZIjvRYOzkyM65l6/+/nWRW/9J1wKUTOdMkXKD5zmKgCXLbeZIfpbjvQ6WGeQlxqFcwQO0DQRDsedPIAIKUObPn4+ZM2di6tSpHo/v3LkTFovF4/Hhw4cjNzcXW7ZsAQBs2bIFo0ePRkZGhnxMYWEhDAYDDhw40O77mUwmGAwGjw8iop7qTK0jUyIHKHrHzd99Fka4iJtyYicZFNdKlNCfoz8lHrEDc3l91wIUSZI8ApSvDlVgz+k6AF1bwQO4Sjytz7G0nU0AXRmUehyrCP0MFKBtcNpjm2Tff/997Nq1C4sWLWrzXHl5ObRaLRITEz0ez8jIQHl5uXyMe3AinhfPtWfRokVISEiQP3Jycvw9bSKibsO9xAO4MijVTSZYwzRKHnCsJmowOno3OsugpMqzPMKQQZGXGas7OdJ7+cRfTWYbLDbHcLipIxz3JItNQqxWhYGpXdsDRwRR5xpNHlsFiHN2DwiGZcRDqXBsLthktkGrUmJAauANur5oHQj2yBLP6dOn8Zvf/AbvvfceoqLClwJasGAB6uvr5Y/Tp0+H7b2JiILtTJ1nBiUlVguVUgFJ8m3X22ARmQqgbR9Ca3IGpckU0p2D3c+rs9ksgFuJx9C1AEXMd9Gplfht4VD58ZH9EqASc/4DlBKrhUbl+Ps955YlK2sngxKtVWGgW0AyKC02oAm2/ojTqeVrVChc39NI8+uqd+7cicrKSowfPx5qtRpqtRobNmzAyy+/DLVajYyMDJjNZtTV1Xl8XUVFBTIzMwEAmZmZbVb1iM/FMa3pdDro9XqPDyKinshqs6PMud+JyKAolQqkOgOAyiD0UvhKNKPG69SdrlJJds5BsdgkNIRo3xvBnx4U0b9R1Wju0rA7Ud5JjtVieKZenklyfm5iwK8pKJUKOUsmsiY2u4QKeVmvZ8YiP9tVUgp1gyzgmHYrsihpcTpo1d1jAolfZzFlyhTs27cPe/bskT8mTpyIuXPnyn/WaDRYt26d/DVHjhxBSUkJCgoKAAAFBQXYt28fKisr5WPWrl0LvV6P/Pz8IF0WEVH3VNFggtUuQaNSID1eJz8eiUZZsYJHLCHuSJRGJQ9FC3WZp96PVTxJMRr5htqV751YnSR6cf58wxj8bsZw3Hv5eQG/prusVr0ylQ2OWThqpUIe5CbkZ7l+CQ91g6wgMmjdpf8EADov8LmJj4/HqFGjPB6LjY1FSkqK/Pi8efPw4IMPIjk5GXq9Hr/61a9QUFCAiy66CAAwbdo05Ofn47bbbsNzzz2H8vJyPPbYY5g/fz50Ol2b9yQi6k3OOJcSZydGQ+lWOhDBSjgbZcWy2s6WGAspcVo0mqyobjR5lCGCTQROvjTJKhQKZOqjUFLTjHKDETnJMQG+p+N7kewM1pJitbgnSMEJAGSIAMVZiip1ZtEy9FFtSkiiURYIfYOsIMpp3WVIGxCCSbJ//etfcc0112D27NmYPHkyMjMz8cknn8jPq1QqLF++HCqVCgUFBbj11ltx++23Y+HChcE+FSKibudMqyXGgmslT/hLPL4EAoCjlwIIbZ+M3e4qIfkySRZwNaF2pVG2dQYl2LLklTyOv/+yetewvtbcMyjDwpRBET8D2Qk9NIPSnvXr13t8HhUVhcWLF2Px4sVevyYvLw8rVqzo6lsTEfU4coCS6Pmbfpoo8YQxg+LrkDbBNQsldOfYYLRC9OD6Gji5yictnRzpnfheJIcoQJGXQzvLUKIPKaudgCAtXocHpg6FxWaX+5RCTazcGZzetRVLwdTlAIWIiHzXegaKkCEyKF1cjeIPX4e0CSKDEsoelLoWx2vHaFU+r15xzRkJPHASI/872zQxUJmtgqhS53+zvJRUfjN1SEjOw5sHfzQUFw5MxvRR7S9WiQQGKEREYSRnUJJblXgikEHxdUibIJYah3LcvT9D2gRXdiLwDEqNyCbFhqjE06oMJTIo3aWkkhKnw3Xj+kX6NDx0j7VERER9hGsGimfqXm6SDecqHnk5r68ZFMc5VoVwmmwgAUowhrX5W+7ylxh3X2kwwW6X5B6U7jJWvjtigEJEFCY2u+Q2A6X9JtmqRscNLBzkm7IPy4wB93H33SuDkuFlt2B/1IgVTSHKoKTH66BQAGabHTXNZpS2sw8PeWKAQkQUJhUGI6zO2ReipCOkxjluYFa7JJcbQk0sM/a1xJMahibZuuZAMiiOm3xFgwm2AIM7VwYlND0oGpVS/v6V1DTLWShvPSjEAIWIKGxE/0l2YnSb2RcalVJeQRKuMk+9vyWeMGZQfG3cBRyrXlRKBWx2KeDyk+irCVWJB3CVc/aeroMkAVq1Um48prYYoBARhYm3FTxCWnx4Z6HU+tl3Icbd1zSbA85UdMYQQIlHpVQgzZmdCGRX4xazDSarYxO/UJV4AFcpandJHQBHwKJQdG2fn96MAQoRUZh4G9ImiBtYODIoJqsNzWbH3jW+ZitEhkeSXCWRYAukBwXo2rA2UVLTqBSI1ar8/npfiQzKrpJaj8+pfQxQiIjCxJVBaX/4VnoYMyj1zl4PhQLQR/kWDKhVSrlHozpES40D6UEBujasrdatvBPKjIYIouRSHxtkO8QAhYgoTMSNqZ+XDdlc4+5Dn0FxH3OvVPp+UxbTZEO11FjOoPjZCyKyT+UBZJ/8LXUFSgyUE9gg2zEGKEREYXLOGXhkeknth3NH49oAm0JDPU020BJPlzIoYoqsj8utA9X6751LjDvGAIWIKExEWUSshmktnCWeugBWywDuK3lCnEEJYw9KoMGav1pnULrTzsHdEQMUIqIwsNrscilBTGRtLawlHjHm3s9AQJx7qHpQ/F36LIibf0UAexnVhnjMvcAMin8YoBARhUFNsxmS5GhK9TYMzH0/HkkK7TRZ1+Z4fpZ4RAYlBAGK1WZHo8kKIJASj+NmX1Zv9Pt758qghLbEE6NVe1wXm2Q7xgCFiCgMRM9GcowWai+79Io5KGarXc4khIq8WsbvEo8zgxKCEo/BaJX/rPczQBHZJ5PVLl+brwIN1gIhMj0xWhX00dyvtyMMUIioV6tsMOK+Zbvww5m6iJ6HCFC89Z8AQJRGJf+GHeoyT6Cb46WGsElWnFO8Tt1m0m5nojQqeZBcuZ9lnnCt4gFcZR4OaescAxQi6tU+2nEGy38ow59XHo7oeYj9a7z1nwjh2tW4Ts4aBJhB6UKJp9FkxSe7zsBosXk8LjIZ/mZPBJGd8HeabK2fmyZ2hVhtlO1lqTm5MEAhol5N3Ky2n6yR+xsiocqHDArg3igb2pU84qbs77wRkaXoyhyUt78txoMf7sVzq454PL65qAoAMDQjLqDXzfJxJY/RYkOL2RUciU0Tw5FBEVOEc5LbH9ZHLgxQiKhXEzd6i03Cd84bYCSIG7rY0dYb90bZUBI9Lv5mUFKdAVaD0QqT1dbJ0e07VtkAAPhi71lYbXb58ZX7ywEA00dlBvS6GWIWSgclHptdwrWvbMK0lzbIQUo4SzxzLszFfVcOxj2Tzwv5e/V0DFCIqFdzv9GvP3IuYudRLQconWRQwlTiqZWXGft3U9ZHaaB29ofUBFjmEVmtqkYzvj9RAwAoqW7GwTIDVEoFfpQfWICSpe98WFtRZSOOVTbidE0Ltp2sgdHi2pMo1MuMAUeJ7LeFw5CbwgxKZxigEFGv5n6j33CkMuTLd71xNcl2nEEJ5o7GRosNr64vwolzjR6PS5Ik93v4O6hNqVTIZZ5AG2XdSzBf7D0LAFi5vwwAMGlgsvz6/vJljsye07XynzcdOyf34qiUCuijuKqmO2GAQkS9liRJHjf60nojjlU2dvAVoVMlpsh2cvMN5o7GK/aV4blVR3DHkm0eDakNJivMVkdpxd8ABehao6zNLnkMU1u5vxwmq00u78wIsLwDAOnysLaOApR6+c/fHqtyK+9ouKqmm2GAQkS9Vm2zBRabI2Ny8XkpAID1Ryojci6ixNNZBiWY4+5L6xyljtM1LXh1/XH58edXO5pT+ydFI07nf9bAtR+P/0FUdaMJVrsEpQLI0OvQYLTig+2nsed0HRQKoHBkFwIU5/fuXAffu72n6+Q/Hy5vwNEKRz9MYhj6T8g/DFCIqNcSN/mkGA2m5WcAAL45HP4+FEmS5CbZtM4CFH3wmmSr3Eowr68/juKqJqw/Uol3tpwCADxz/eiAsgau/Xj8z6CUOss7GfooXDMmGwDkJeATcpPk6w+EyD5VNZphcWu+FVrMNhxxBiRiSfLyHxylpWQGKN0OAxQi6rVEmSRDH4UrhqUDAHacCv9y42azDUaL44bZ6TJjZxag2Wzr8nmecwZFWpUSZpsdv/vvD3jk4x8AAHcU5GHy0LSAXlfMcqlq8j+IEg2smQlR+PFYR4AimlQDXb0jJMdo5Qbe9pZBHyith80uIS1eh+vOd7z3BmfjdCClLgotBihE1GuJXoe0eB0GpMZiQEpMRJYbi0xDlEaJGK2qw2NjdWrEOo+pDGDjO8/3ddykf3XVYGjVSmwtrkFlgwmD0mLxuxkjAn7drmRQRINsdkI0xvRPQJ7bapauBihKpUJext1eD88eZ3lnbP9ETB7iCM7MzkxLoI25FDoMUIio1xJlEjFbRGRRwr3cuMptiqwvJZVglXlEiWfCgCTce7lj7oZaqcBLN41DdCeBUkfEUulAlhmLJcaZzlHvIosypn8C+id1felthnMlT3u7Gu8942iQHZeTgAl5SdCpXbdA9qB0P1xTRUS91rkGUeJx3LQuG5KKpZtPYtep2o6+LOhEpiE1vuP+EyEtXofiqqYgBCiu4XD3XnEemkxWjMlJxJj+iV16XTlLEUAjr+hBEVNf75o8CPUtFtwwvn+XzklIi48CUN/u9040yI7LSUKURoULBybj22OObFpyGMbck38YoBBRryV+ixZ9HeelOUaon6ppgiRJYVtWKgcKPpYRXEuNAy/xWGyuXX1T43SI0qjw2DX5Ab+eu6wEx7j2sjr/z8+9BwVwDH5beN2ooJwX4ApGWwcoNU1mlNQ0AwBG908A4AhYRYDCDEr3wxIPEfVaconHecPvlxQNlVIBo8Ue8lHy7lxLjH27CbqWGgd+jqL8olIqkBjg5nveZCc6vp/VTeY2G/51pkzOoIRmszx5q4BWwd1e527Wg9Ji5R2jLx3sahIOx5h78g8DFCLqtUQGRfxWrVEp0c+5i+zJqqawnUeVj1NkBde4+8AzKCJrkxyrhVIZ3ExRQrRGbvYVs1baY7La5DIbANjdhrSJEk+weZsmu6ekDgAwzq28NTwzXv5eh+p8KHAMUIioV3JMkfVskgUgrxo55Uz3h4OYuNrZRoGCLyPbOyOCIl/f0x8KhQLZzkDP287BR8obMPXFDbj4z+tQUu34Xlc1mWCxOYa0pfnYj+Mvb02yIoMyNidRfkypVOC1Wydg0Q2jMapfQkjOhwLHAIWIeiVDi2ucu/vNUA5QqsOYQWnwbaNAIRg7Gvv7nv4SGYez7WRQ1hwoxw2vfofTNS2w2CR8W+RYNSVW8KTF66BRheb20973TpIkuUHWPUABgAl5SZhzYW5IzoW6hgEKEfVKFc4VJgnRGkRpXEtqB6TEAgBOVYczg+JaZuyLYJR4xHuGIoMCQC6VtS7x/HNTMf7fuzvRZLbJ81x2O8sroe4/AVzZp+pGE6zOGSena1pQ22yBRqXAiKz4kL03BRcDFCLqlcSgrvRWpYTcZJFBCWOAIveg+JdBMRitfjehCnLfS4gGkMklHreVPC1mGxatPARJAm4vyMNfbxoHANhVUus81hHMhLLfIyVWB6UCsEuu0trBMgMAYGhGPHTqwOe/UHgxQCGiXsnVIOt5MxyQ6signKx2LDUONZtdQk2zfwGKPlotDxE7F2CZRy7xhKjXQwQZpfWuDMqJqkZYbBKSYhxLhy8YkOx4/FwT6prNKDO4hrSFiqqdabKHyx0ByvBMfcjel4KPAQoR9UquBtn2MygNRqs8JySUapvNkCRAofB9QzqFQiGXKtqbiOqLKj8bc/0lSjzuPSjHzzn6esS8maRYLQY6A8Ldp+vkHpTsEJZ4AFdQKr53h8scGwSyvNOzMEAhol5JTDltvTtulEYl72R7MgyNsmK5b1KMFmo/GkO72igrMii+Zm385V7iEZmo45WNAFwBCgCcn5sIANh9qlYuB4UygwK0nSPDDErPxACFiHolbz0ogGslT0kYlhpXB9gL0tVGWREYpYUogyKCjBaLTc5EHT/nDFDSY+Xjzs9NAuDIoJQZQt+DArjvZWREk8kqLylnBqVnYYBCRL2SK4PiPUA5WRX6AKXKzymyQlemydrtkjxJNlQZlCiNSl7CLPpQRIlncLorgzLemUHZU1KHinrHtYQrg1JhMOFoRQMkybG02ddBedQ9MEAhol6pwiA2Cmx7M8yTlxqHvsRTHeDAtK7saFzfYoHV7ii7+Lq0ORBiuXBpnRE2u4QT59qWeIZlxCNGq0KDyQqzzQ6Fov2/k2ASQem5BiMOlzv6T4ZnMnvS0zBAIaJexzFF1nOjQHfhnCYb6DyStC5kUMR7JkRroFWH7p95sSdPaV0LSutaYLLaoVUp0T8pRj5GrVJiTH/XlNa0uNANaRMy3Pp3DjuXGI/IYv9JT8MAhYh6nQaTFUaLY0iX+5h7YUAYMyhVDeHvQTnXENryjiAaZUvrW1DkzJ4MTI2FqtXeP6IPBQjPnjfuK6AOMYPSY6kjfQJERMEmburxUWpEa9sO5sp1ZlCqGs1oNFkRpwvdP4XyFFk/MygZXSjxiL6XUC0xFrLdSjzyCh63BllhvFuAEur+E8D1vatqNKPZ7Bh0xxU8PQ8zKETU63S0ggcA9FEaJDszGqHOorg27Qssg1LTZJb3FPJVtRyghCmDUtfSZgaKO7HUGAjtmHshJVYLhcIxJK/BaIVaqWg3cKLujQEKEfU6Yh+ejpoxxcC2kiCNvD9+rhH1LZ6D30xWm7xXjb8ZlKQYLdTOUonIiPgqlDsZuxM9KGV1LfISY/cVPEJqnE7+foejxKNWKT2ag89Li+OI+x6IAQoR9TqdZVAAYIBYahyEAKWkuhlTX9yAq55fjx0nawAAFpsd9y3bjcoGE2K1KgxuJ7PQEaVSEXCjbNhKPM4MSrnBiGMVjl6P9jIoAFA4MgNKhWP34HDIcFtezvknPRMDFCLqdeQx9x1lUILYKHugtB6Sc3O6OW9+j/9sK8H97+/B2oMV0KmV+MftE5EQo/H7dX1tlP3npmIULFonT0yt8nNzwkA5VuQoYJeAWuewNjHavrXfXz0Cux+fhonO/XlCzT04Hc4VPD0SAxQi6nXKDd6XGAsigxKMXY3FfjQ6tRIWm4QFn+zD//aVQatS4o3bJuCSwakBvW6aD+PuS+ta8OdVh1FWb8SSTScBhC+DolQqPJpesxOiEOul4VihUAQUpAXKffUWV/D0TAxQiKjXEZvSddSQGcxhbWdqHQHKnRcPwANThwIA1EoFFs8djyuGpQf8umK5bEcByl/XHpWbaFfuL4PZancLUEKbQQE8v8fntdN/EimeJR5mUHoiLjMmol5HBCgdLWkVTZtlBiNMVluXmihFBqV/UjRuKxiAK4alQatWdvnGKA8c81LiOVLegP/uOgMAiNOpYTBasfHouYCn1wZC7GoMeO8/iYQ0Z3kvKUbTYSaNui9mUIioV7HbJVQYRAbFe4CSGqdFtEYFSXLM8eiKs84MSr8kx816bE5iUH5r7yyD8pfVh2GXgOkjM3HjxBwAwPvbT6PF4pj9EY4ARazkAbpXBmVYhqOsMyEvGQqFopOjqTtigEJEvUp1kxlWuwSFwjUuvj0KhQL9nQHF6S6OvD9T6/j6fokxnRzpH9eGgW0DqG3FNfjqUCVUSgUenj4MPx6XDQBYd7gCABClUSKmnSF1weZR4knrPrNGLhiQhP/eW4Dnfzom0qdCAWKAQkS9iijv+LLnizwLpQsBSoPRAoPRCsCVQQkW0egpNj509/K6YwCAmy7IwXlpcRjbPwG5yTGQHHsEIjVOF5bMgXuJx9+l1KGkUCgwIS8ZiTGh78Oh0PArQHnttdcwZswY6PV66PV6FBQUYOXKlfLzRqMR8+fPR0pKCuLi4jB79mxUVFR4vEZJSQlmzpyJmJgYpKen4+GHH4bVag3O1RBRn1dW7yi3+DJSPccZoJyuDTxAEf0nCdGaoI/MF+WTcw0mGJ1lG8CxGeLe03UAgNsL8gA4bsjXjs2Sj/F3MFygxLYBiTGaDjNWRP7yK0Dp378//vznP2Pnzp3YsWMHrrrqKlx33XU4cOAAAOCBBx7Al19+iY8++ggbNmxAaWkpbrjhBvnrbTYbZs6cCbPZjM2bN+Nf//oXli5diieeeCK4V0VEYSdJEv695SQOOXePjRTRf5LZwQwUQZR4ztS0BPx+ov+kf5CzJwCQHKtFvDPocS9D1TSZ0WCyQqFwbXwIAD8e20/+c1oYVvAAjsbYp2eNwt9uPp+9HhRUfgUo1157La6++moMGTIEQ4cOxZ/+9CfExcXh+++/R319Pd5++228+OKLuOqqqzBhwgQsWbIEmzdvxvfffw8AWLNmDQ4ePIh3330X48aNw4wZM/D0009j8eLFMJvNIblAIgqP74qq8fjnB3D7P7eh2Ry5rGhZfecNskIwMyjupY5gUSgUcobCfeLtKWewkqmPQpTG1WcyLDNebg4NR4OscNtFebh8aFrY3o/6hoB7UGw2G95//300NTWhoKAAO3fuhMViwdSpU+Vjhg8fjtzcXGzZsgUAsGXLFowePRoZGRnyMYWFhTAYDHIWpj0mkwkGg8Hjg4i6l6JKx6jzcw0mvP1tccTOQ/SgZPgSoCQ5A5Qu9KC0XsETbAPamdci/pyX0rYp9+eXDgAAXBCmia1EoeJ3gLJv3z7ExcVBp9Phnnvuwaeffor8/HyUl5dDq9UiMTHR4/iMjAyUl5cDAMrLyz2CE/G8eM6bRYsWISEhQf7Iycnx97SJKMRO17rKJG9sPCHvqBtu5T4sMRZykh1BRW2zBQ1GSydHt+9MCDMogCsIcZ94e7LK8ee85LarZm66IBd7nvgRbhjfr81zRD2J3wHKsGHDsGfPHmzduhX33nsv7rjjDhw8eDAU5yZbsGAB6uvr5Y/Tp0+H9P2IyH8iC6FQAI0mK/7+TVFEzkMe0qbvPGCIj9IgyTl+/XSAfSih7EEB3AIUtyyPWHWUl9r+subEGC37QajH8ztA0Wq1GDx4MCZMmIBFixZh7Nix+Nvf/obMzEyYzWbU1dV5HF9RUYHMzEwAQGZmZptVPeJzcUx7dDqdvHJIfBBR9yIyKD+/ZCAA4N3vT3V5voi/JEmSe1B8WcUDdL0PxdWDEtwZKEJ7I/lPihJPOxkUot6iy3NQ7HY7TCYTJkyYAI1Gg3Xr1snPHTlyBCUlJSgoKAAAFBQUYN++faisrJSPWbt2LfR6PfLz87t6KkQUQWJY2ZwLc3DZkFRYbBJeWHMkrOdgMFrlKaq+rOIButaHYrTYcM455TVUPSgig3K2tgUWm2PPnRJnuae9HhSi3sKvRfsLFizAjBkzkJubi4aGBixbtgzr16/H6tWrkZCQgHnz5uHBBx9EcnIy9Ho9fvWrX6GgoAAXXXQRAGDatGnIz8/Hbbfdhueeew7l5eV47LHHMH/+fOh0XD9P1FPVN1vQIIaVJcbgoWnD8O2xKqzYV44XbpSgUoan3CDKO4kxGkT7OEW1v7MP5Uyt/yWeUmf2JFqjkktFwZYRHwWdWgmT1Y7SuhYkx2pR3eRY9cgAhXozvwKUyspK3H777SgrK0NCQgLGjBmD1atX40c/+hEA4K9//SuUSiVmz54Nk8mEwsJCvPrqq/LXq1QqLF++HPfeey8KCgoQGxuLO+64AwsXLgzuVRFRWInySGqcDtFaFUb3S4BWpYTZ5ripijJKqMlD2nzMngBdy6C4bxIYqp4PpVKB3OQYHKtsxKnqZjkQTInVIj4qNEERUXfgV4Dy9ttvd/h8VFQUFi9ejMWLF3s9Ji8vDytWrPDnbYmomxM3d7EqRqV0zO8oqmzEyeqmsAUo8pA2H/tPANe4+0B6UEK9xFjIS4l1BihNcoCSy+wJ9XLci4eIukyUR0Q2AgAGtDNgLNT8GdImyE2yNS2QxEY2PgrlkDZ3A9yWGp+qaXI+xgZZ6t0YoBBRl4nsg/tSW3EDPVnV1O7XhII8pM2PEk92YhQUCqDFYkNVo38TrcOXQXEFe6ecM1Byw5SVIooUBihE1GWuEo/rppmXGoEAxY8hbYJOrZJ7Vjor82w6VoUb39iC74qqAIR+SJsglhqX1DS5MiheZqAQ9RYMUIioy063U+IZKDIo1d07gwL41ihbVNmAe97diW3FNbhv2S5UNhhDPqRNcJ8mW+wM+HI5A4V6OQYoRNQlkiTJM1A8Sjyprt4Om92/3o5AuTIo/gUMnS01rm+x4K53dqLRuYNwbbMFj3z8g/x+oRrSJvRLjIZaqYDJakeFwTF3ZQCbZKmXY4BCRF1S1WiG0WKHQgFku5U6shKiPZYah1qL2Ya6Zsd+Ov6s4gFc/Rwl7TT02uwSfv2f3SiuakK/xGgs+8VF0KqVWH/kHGx2CRqVAunxoZ3jpFYpPfpc4nRqJMdqQ/qeRJHGAIWIukT0bWTpo6BVu/5JEUuNgfCUeUQ2I1qjgj7KrwkKrhJPOz0ob357AhuOnkOURok3bpuAgvNS8EjhMPn57MRoKMMwiC7PbdVOXkoM99qhXo8BChF1iejb6N/OqhJ5qXEYGmXL3ZYY+3vz7mg/nvVHHFtzPFw4HKP6JQBw7DdUMCgFQOj7T4S8ZPcl3Ow/od6PAQoRdcmZDhpF5aXGYZiFUm5wTpH1s7wDuAbMldYZYXXudyOU1jkCn9HO4ARwTHd96eZxmD2+P+ZfOTjQU/aL+1h7DmmjvsC/PCgRdTtWmx1KhSIsZYb2iAZZ9xU8woAwLjWWdzH2cwUP4NjvRvTLlNUb5YyK3S7J4/NbzzrJ0EfhhRvHdvGsfeeeNWGDLPUFzKAQ9WAmqw2zX9+Cq15YD6NzF99wO13jXGLcboknfEuNK+r9H3MvKJUKOQPkXuY512iCxSZBqQAyQtwI2xmPDAqXGFMfwACFqAf7x4YT2Hu6Dierm1FU2RiRc2hviqwQ6qXGRosN35+oxj82Hsf6o+cA+DekzV3/5LazUMQo+0x9FNSqyP5zmZMcA7UzSzYojQEK9X4s8RD1UKdrmvH3b4rkz09WN8lNnOFis0vyEuL2MiitlxoHa9PAospGvLf1FP678wwMzs3zhGGZ+oBeM0dkUGpcS6LDNcreF1EaFZ77yRg0mqx+D6Ij6okYoBD1QJIk4Q9fHIDJ6mroPBXGTfmECoMRFptjFkh7vR/B3tVYkiTc95/d+N8PZfJjGXodzs9JwpicBFw0KAXjc5MCeu32VvKI4Cs7xKPsfXXD+P6RPgWisGGAQtQDrT1Yga8PV0KjUmDGqCx8sbcUp8I4Ul4Q5ZDsxGiovDTpDkiJdQQoVU24bEhal96vtN4oBydTR2TgtoI8XDY4NSgNwu2Nu+9uAQpRX8IAhaiHMVvteOrLgwCAuy4bhGGZ8fhib2lYlvK2dtqHvWgGuO3E21ViNdCgtFi8dcfELr+eO7HU+LTbuPuzYdoMkIjaYpMsUQ+z53Qdzta1IDlWi/uuGtzhmPZQO+nDxnXBXGosNsobGIJBZeL7eK7BhBazY0XU2Tqx1w4DFKJwY4BC1MNsP1kDAJg0MBkxWrW8lLfcYAz7UmOxcmhwepzXYwY6A5TiIJSgRBkrLwQBSkK0BvE6R1JZzHZhiYcochigEPUwO0/VAgAmDkgGACTGaOS9Z0pqwptFOVbZAAAY0kGAIuZ3nK5p7vJS4+Iqx/UNTA3+oDKFQuFaalzbjEaTFfUtjs0HsxO5aoYo3BigEPUgdrvkClDyHKtVFAqFnFEIx8RWwWy1yyuHOsqgZCdEQ6tWwmKT5MxEoEKZQQE8lxqL7Ik+So34KE1I3o+IvGOAQtSDHD/XiPoWC6I1KuRnu+Z9iCxFODMop6qbYLVLiNWqOhyOplQqMMhZ5unKMDm7XcKpGpFBCVGA4jas7SzLO0QRxQCFqAfZftKRPRmbkwCN22TTPHmlTPgyKO79J53tHjwkI97jawJRWt8Cs9UOjUoR8LTYzuS4jbs/68MKJSIKHQYoRD3IjlOOBtkLnP0ngih5hHNY2zE5QInv9NjBaXEeXxMIcW05yTEhGzufm+Iazc8GWaLIYoBC1IOI/pMJeZ7TUvOcpYlwBii+rOARhmTEeXxNIEK5xFhwH9bGGShEkcUAhaiHqGww4lR1MxQKYHyrAEXMGjlb1wKLzd7elwedyIZ0tIJHEEFMUWUjJCmwlTyiAThUDbIA0N8ZoDSYrDhUZgDADApRpDBAIeohdjr7T4ZlxEPfalVJerwOURolbHZJ7p0IJZtdwolzvmdQBqTEQqVUoNFkRYXBFNB7ikm0oVhiLERrVUiN0wEAjlY4ro8BClFkMEAh6iF2yPNP2m6Gp1AokOec5hqORtkztc0wWe3QqpU+bQCoVSvlRl4xO8VfJ0O8xFgQI+8FNskSRQYDFKIeQg5Q8pLbfT6cS41FL8mg1FivmwS2JhplA+lDsdkleZR/qJYYC7luAZdGpUCaM6NCROHFAIWoB2gx23DgbD2Atg2ygrzUuCr0AYrcf5LR+QoeQTTKBrKSp6y+BWZbaJcYC6JRFgAyE6KCslMyEfmPAQqRj7YV1+DvXx+DvYvj2gOxq6QWVruEDL3Oa8lBlD5KakJf4pFX8KR13n8iuDfKdqbFbMNzqw5j3xlHUCaCrlAuMRbcSzxcwUMUOQxQiHxgttrxy/d24vk1R7Hx2Lmwv//yH8oAAFcMTfc6FM01rC2cGRTfA5Qh6b4Pa/vnd8V4df1x3PPuThgtNrn/JJRLjAX3DAobZIkihwEKkQ/WHCxHVaMZQNdmeQTCbLVj5X5HgPLjcdlejxsgZ1CaQ5rlkSQJx/2YgSIMSnOcX02TGdWN3lfySJKE/+48A8CxbPrd70+FZYmx4N70ywwKUeQwQCHywXvfl8h/Lg7jhnwAsKnoHOqaLUiL1+GiQSlej8tKiIJaqYDZake5wRiy8yk3GNFoskKlVMhBkS9itGq5PNVRkLerpA4n3L7Hf/+mCD84+29CucRYyEqIkht/GaAQRQ4DFKJOHD/XiC0nquXPgx2gbDpWhV++txN/WX0Yqw+Uo6JVcPHFnlIAwMzRWR2umFGrXEt+g7WrcXtD30RwkZcSA63av39C5D6Uc94DlP/ucmRPrhuXjSHpcahrtmBbcY3zPUOfQVGrlHIg1T8p9AEREbVPHekTIOru/rPVkT3JTohCab0x6AHKS18dlZcQA4BSATz145G4rWAAWsw2rDlYAaDj8o4wNCMOxVVNOFTegIsHp3bpvO59dyd2ldTi43su9ih7HKvwfYJsa0PS47D+yDn5NVozWmz4cq8jILvpghw0m2z4xTs75OdDvcRYeGxmPrYcr8ZFg9pf0k1EoccMClEHjBYbPnb+Rv/bwmEAgLJ6I1rMtqC9x+laR1Pr1BEZGJYRD7sEPPnlQXxXVIV1hyvQbLYhJzka5+ckdvpa+VkJAICDpYYunZMkSfj6cCUqDCY89eVB+XG7XcKq/eUAgKF+LDEWOlvJs+ZgBRqMVvRLjMZFA1MwZUQ6LnAOpgvHEmPhR/kZeOLa/JCvGCIi7/h/H1EHVuwrQ12zBf0So3HduH5IiHaMmA/WtFaz1Y7KBkfD6LOzR2PV/ZfhhvH9YLNLmL9sF5Z8dxIAcO2YbK+rd9zlZ+sBAAfLuhag1LdYYLI6yjtfHarAWmcW5+1Nxdh2sgYxWhV+OiHH79cd3MlKno+dzbGzx/eDUqmAQqHA72aMgFqpwPk5SQwYiPoQlniIOvCes7wz58IcqJQKDEyNxZ7TdSiuasKILH2XX7+svgWSBERplEiO1UKhUOCZ60fjeGUj9p6pl3cvvnZs5+UdABiRJQKABpido+gD0Xq/nCe/OIDUOC3+svoIAODxa/KRm+J/f4bIoJQbjDAYLR57CpXXG7HJuYR79oT+8uMT8pKw7qHLkRit9fv9iKjn4q8jRF4cLjdg56laqJUK3DjRkS0Y5OyBCFYfyhnnxn79EqPlDEmURoU3bpuItHjHiPUh6XEYnulbOaVfYjT0UWpYbFLAe94AkFcB5aXEoF9iNM7WteCmf3wPs82OKcPTcfMF/mdPACAhWoN053W1zqIs/6EUdgm4cEBym2bYvJRYJMR4bpBIRL0bAxQiL5Y5syfTRmYgXe/ofRgY5ABF7Dzcr9VqkcyEKLx5+0SMzUnEQ9OG+lTeARybBsplni70oVTIAUosnrg2H4CjHJUcq8Wi2aN9Pp/2DHMGW4fLPAOo3SV1AIApI9IDfm0i6j0YoBC1o8lkxSe7zgIAbrkwT358QLAzKHWuDEpr43IS8fn8SzB9VJZfrykaZQ+VBZ5BqXQGKJl6HablZ2D6yEyolAr8+YbRSI/vWqNqfpbok6n3eFz0zYgAi4j6NvagELXjy72laDRZMSAlBhef5xqOFqoMirf9dQLhapSt7+RI70SJJ0MfBYVCgVduOV8eFhes83MPoBpNVrnxOBi9PUTU8zGDQtSOZdsc5Z1bJuV67GYrApSaJjPqmy1dfp+zdY4lxsGcWCpnKEoNkKTARt6LJtkMZ2lLo1IGJThxP79DZQZ5JP+RcgMkCcjQ65AaF5z3IaKejQEKUSs/nKnDD2fqoVUp8ZNWS2ljdWq5ybM4CEuNz4oSTxAzKIPT46BRKWAwWuXX91eFWwYl2AamxkKnVqLZbMOpGkeAJvpl8pk9ISInBihErYjm2BmjM5Ec23Zpq6vM07VNA212CWV1jkAgmBkUrVopzxsJtFG2Qu5BCX6AolYp5VVJ4vzYf0JErTFAIXLTbLbic+feN3Mn5bV7jNiVt/hc1zIolQ1GWO0S1EpF0DMVrkZU/wMUq82Ocw2ixBOackvrPpmDzn4U0eBLRMQAhchNUWUjWiw2pMZp5RHrrYkdfIurm7v0XmIGSlZiVIebAAaiK0uNq5vMsEuASqlASoj6QVx9KA2w2uw4zAwKEbXCAIXIzUln0DEwNdbrrI9glXjO1npfYtxVXcmglNc7yjtpcbqgB07CCLdG3pPVTTBZ7YjRqpCXzN2DiciBAQqRm1PO5cO5yd53zXUv8QS6SgZwa5BNDP5NWQQoZ2pbUN/i32ojuUE2hBvzDXeeX7nBiG+PVTkey4z3WDFFRH0bAxQiN2JVyYAO9pnJSY6BUgE0mW0412jyelxn5DH3QVzBIyTEaOTMzGE/syhygBKkZcXtidOp5e/xf527RbO8Q0TuGKAQuTnlXDrc0UZ4OrVKDiq60igrMij9Q1DiAVxllEN+ByiOoCszhBkUwBWQ7D8rlhizQZaIXBigELk5VS0yKN5LPAAwMNWxK2/RucD7UM7WOoe0hSCDAgBDMhzneNzPIKo8hDNQ3LWeecIMChG5Y4BC5NRstqLSubw2r4MMCgCM7e/4bX/riZqA3kuSJLcelNAEKOeliQDFvyAqlEPa3LkHJEoFMCzDtx2biahvYIBC5FTi7D9JiNYgMabtgDZ3lw5OBQB8V1Qlj2v3R02TGUaLHYBjmXEonOds5g08QAntyHn3ks6gtDhEa1UhfT8i6ln8ClAWLVqECy64APHx8UhPT8esWbNw5MgRj2OMRiPmz5+PlJQUxMXFYfbs2aioqPA4pqSkBDNnzkRMTAzS09Px8MMPw2q1dv1qiLrgZJUjQOksewIA5+cmIUarQnWTGYfK/V/KKxpk0+N10KlDc2M+L92RQakwmGAwtl3JI0kSvthbiml/3YCnvjwgPy73oIQ4g5Kh1yEpRgOAGwQSUVt+BSgbNmzA/Pnz8f3332Pt2rWwWCyYNm0amppcNe4HHngAX375JT766CNs2LABpaWluOGGG+TnbTYbZs6cCbPZjM2bN+Nf//oXli5diieeeCJ4V0UUgJIax89xXif9J4BjnPxFgxy7HG9yLpP1h9wgG6L+EwDQR2nkfYNOtOpDKa5qwu3/3IZf/2c3jlY04l+bT6K2yQyjxSYvS04PcYCiUCgwMtuRReEePETUml8ByqpVq3DnnXdi5MiRGDt2LJYuXYqSkhLs3LkTAFBfX4+3334bL774Iq666ipMmDABS5YswebNm/H9998DANasWYODBw/i3Xffxbhx4zBjxgw8/fTTWLx4Mcxmc/CvkMhHYkibr8PCRJlnU1EAAYq8xDi0g8nkPpRKV5mnqLIBM/62Ed8eq4JWrURyrBZ2CVh/tFIu70RrVNBHqUN6bgDw4LSh+MmE/rjlwtyQvxcR9Sxd6kGpr3fso5GcnAwA2LlzJywWC6ZOnSofM3z4cOTm5mLLli0AgC1btmD06NHIyMiQjyksLITBYMCBAwfQHpPJBIPB4PFBvcuKfWWY+9b3mPMPx8fd7+xAWX1gO/EGSiwx9qXEAwCThzoClG3FNTBabH69V6gbZIXz0tv2oXyxtwxGix2j+umx9oHJcnDw1cFKubyTodd5naQbTONzk/D8T8ciwVnqISISAg5Q7HY77r//flxyySUYNWoUAKC8vBxarRaJiYkex2ZkZKC8vFw+xj04Ec+L59qzaNEiJCQkyB85OTmBnjZ1U3/63yF8V1SNLSccH2sOVuDtb4vDeg7yEuPUzks8gCM7kamPgslqx46TtX69VyiHtLlrbyXPzlOOlUdzLsxFXkospuY7/v/bcPQcTjsbhUO9goeIqDMBByjz58/H/v378f777wfzfNq1YMEC1NfXyx+nT58O+XtS+JTWteBsXQtUSgVeumkc7rtyMABg5f7yLo2S94fZakepM6vha4lHoVDg0iGOLMq3x855Pc5ul7DpWBXmL9uFa1/ZhGtf2YTNxx1loVANaRNcAYojO2S12bG7pA4AMDHPkfkc0y8BafE6NJqs+HyvYydnBihEFGkBFZnvu+8+LF++HBs3bkT//v3lxzMzM2E2m1FXV+eRRamoqEBmZqZ8zLZt2zxeT6zyEce0ptPpoNOFdskjRc6OU47sQ36WHrPO74cWsw1vbyrG2boW7D9rwOj+oZ8weqa2GXbJ0XuR5seI98uGpOLjnWfw7bEqLGj1nCRJeHdrCf65qRjFVW2HpamVCgzPCu3sD7GS52RVEyw2Ow6XNaDZbIM+So0hzueUSgWmjkjHf7adxsajjkAr1FNkiYg641cGRZIk3Hffffj000/x9ddfY+DAgR7PT5gwARqNBuvWrZMfO3LkCEpKSlBQUAAAKCgowL59+1BZWSkfs3btWuj1euTn53flWqiH2nnSUXKYkJcEAIjWqnDl8DQAwMr9ZUF7n+0na3Dj61twoLS+zXOivJOXEuNX78UlzkbZg2UGVLXal2f/WQMe/2w/iquaEKdT4/aCPLx1+0QsufMCLLnzAqy6fzKyEkKbQcnSRyFGq4LVLqGkphk7Trm+1+4b800d4Vl2TQ/hPjxERL7wK0CZP38+3n33XSxbtgzx8fEoLy9HeXk5WlocqfGEhATMmzcPDz74IL755hvs3LkTP/vZz1BQUICLLroIADBt2jTk5+fjtttuw969e7F69Wo89thjmD9/PrMkfdR2Z//GxAFJ8mPTR2UBAFYFsczz7MrD2HayBn9ZfaTNc/42yAqpcTp5hsd3rVbzbHMGXhcOTMbW30/BwutGYWp+Bq4cno4rh6djsDODEUpKpULeffl4ZaOcrZo4INnjuEsGpyJK4/rngBkUIoo0vwKU1157DfX19bjiiiuQlZUlf3zwwQfyMX/9619xzTXXYPbs2Zg8eTIyMzPxySefyM+rVCosX74cKpUKBQUFuPXWW3H77bdj4cKFwbsq6jEaTVYcdg46Ez0RAHDV8HRo1UqcqGrC0YrA97sRiqua5JuzezOoIC8x9mEGSmuXDXFNlXW3u8TxfpcPTUOsLvRLdr0RfShF5xqxo1W2SojSqHDp4DT5c/agEFGk+fWvpi+/yUZFRWHx4sVYvHix12Py8vKwYsUKf96aeqndJbWwS46BZe6/tcfp1Jg8JBVfHarEyv1lGJbZtV6N/+48I/9ZkoD/bCvBI9OHy4+JMff+ZlAA4OLzUvCPjSew5US1x+OiGfX83ET/TziIRICy8eg5VBhMUCsVGNu/7Tn9KD8dXx1y9oMxQCGiCONePBRRYnnuxFa/0QNA4UhH0/Sq/e0vP/eV3S7hk12OAGXmGEfp6MMdp2G22uVjTooST7L/GZQLBiRDrVTgdE2LnJmpMBhxtq4FSgXaDQbCSQQo3zs3NhzVL6HdfW+uGp4BrUqJOJ3ar0ZhIqJQYIBCEbXTWXaZ0KonAgB+lJ8BtVKBw+UN7a6C8dWWE9UorTciPkqNZ2ePQVq8DlWNZqw96MgW2OwSztQ4lxgHkEGJ1akxNidRfi/AVd4ZlqmPaHkHcA1rE9oLBgEgLV6HZXdNwjvzLkSUhhv3EVFkMUChiLHa7NhV4j2DkhijRcF5jv1uupJF+dhZ3vnx2GzE6dS4+QLHoL/3tp4C4JjDYrbZoVEpkB3gXJIC5748W447ApRd3aS8AwADUmLhtmDHoxm5tYkDkjE+1/vzREThwgCFIuZwuWMmR3yUGkMz2u8xmTI8HQDwfav+Dl81GC3yUuXZExwze26+MBdKBbD5eDWe/OIArn/1OwBATlIMVMrAxrtf7AykNh+vgiRJcgalO9zsozQq5LgNn5uQ1zZbRUTU3TBAoYgRK0rG5yZ5DQzEzXR3SS3sdv+XG6/cVw6jxY5BabE431mG6ZcYjSuGOQKfpZtPoqrRjPR4HR740dAArsJhfF4StColKgwmHK1oxA9nHLNWukMGBXD1oQxIiWF/CRH1CAxQKCSsNnunq77kmRxeeiIAYHhWPKI0ShiMVpzwsw/FbpewdPNJAMDs8f09BrDdd9VgpMRqUTAoBa/OHY/vfncVrh2b7dfru4vSqDA+LxEA8M9NxTBZ7UiM0WCQj/v6hNqQDEeA0nr+CRFRdxXZ7j3qlXaeqsVPX9+Mh6YNw3znvjqt1Tdb5H6NCR30RGhUSozpl4htJ2uwq6TWr+FmX/5QioNlBsTp1Jjj3LFXGJ+bhJ2P/8jn1/JFwaBUfH+iBp/sdvS8nJ+TGJYdgX3xi0sHwWKV8IvLBnZ+MBFRN8AMCgXd2oMVsEuO8kl7ZRmbXcKv39+N6iYz+iVGd9qncb4zMyHmivjCZLXJE2PvuXwQkmO1Pn9toERDr8XmuObzu0H/iZAWr8MT1+YH3ARMRBRuDFAo6I44J8OeazDJq3TcPbf6MDYcPYcojRL/uH1Cp0taz89x3Oh3t/Na3izbWoIztS1Ij9fh55eGJ2swLifRY1x8d2iQJSLqqRigUNC5j6Zf2Wp58Od7zuKNDScAAH/5yViMzO58p+LxzkbTIxUNaDBaOj2+wWjBK18XAQDunzoUMdrwVDK1aiUucPZ4KBTA2JzQ78JMRNRbMUChoDIYLThb1yJ/7r7Z39m6Fvzff38AANx7xXk+N6Wm66PQLzEakgR5dUxH3thwAjVNZgxKjcWNE/sHcBWBE2WeoenxiI/ShPW9iYh6EzbJ9jFf7i3FwuUH5THvWrUST183Ut49uKuOVTQAAFLjtGg223C2rgX7ztZjTP9EvLjmKIwWOy4YkITfThvm1+uOz0vC2boW7DpVi0sGp3o97vM9Z/Hqekf25JHpw6BWhTcGv3FiDr4/URP2wIiIqLdhBqWPeWfLSZxrMKG+xYL6FgvONZjw/JqjPm0E6YvD5Y4AZWR2Aq50zhpZub8ch8sN8uqWR2fm+z0QTZR5dp+u83rMyn1lePDDvbBLwJwLc+W9fMIpNU6Hd35+Ia4ZE/iSZSIiYoDSp5isNux1lkjenTcJK39zGaI1KhRVNmL7Sd8bUDty1BmgDMuMx/RRrs3+nl15GJIEzBydhXHOgWn+ECtidpfUthtMfXWwAr/6z27Y7BJ+MqE//jRrVLdZ4ktERP5jgNKH7D9rgNlqR0qsFpcMTsGILD2uG+f4TV/sS9NVIoMyLCMeVw5Ph1atRHFVE745cg4qpQK/LfSvtCPkZ+mhVStR22zByepmj+eazVbc/8EeWO0SrhuXjWdnj4EywJH1RETUPTBA6UPk0fJ5SXJ2Ye6kPACOkfA1TeYuvb4kSTha4cqgxOnUmDwkTX5+zoU5GBjgZFWtWonR/RyrYnad8sz27C6pQ6PJiqyEKLzw07EB76dDRETdBwOUPkSMlr/AbXLr6P4JGNM/AWabHR/vPN2l1z/XYEJtswVKBeSJrzOcZZ4YrQq/njKkS68v+lBaz1bZ6txI8KJBKWFviiUiotDgv+Z9hCRJcuah9W62tzjHwC/bWhLQhnzCEWf2ZEBKrDx87dqx2bjrsoH4283nIz0+KuDXBoAJzj17xIh8YWuxIzN04UDuM0NE1FswQOkjiquaUN1khlatxKh+eo/nrh2bjXidGierm7G51c3fH0fcGmQFrVqJR2fm40f5GQG/rnDJ4FRoVAqcqGrC8XOOYXAmq01e2cMAhYio92CA0kfscK7SGdc/ETq152j5WJ0a14/vBwD4YEfgZR4RoAzNiO/kyMDER2lw0SDHILR1hyoAOAa3ma12pMZpu83OwURE1HUMUPqIHaccZRBvOwfPHO0Y1CYaaQMhSjzDM0MToACQMzFfHawE4Oo/uXBgMpcVExH1IgxQ+gjRIDsxr/0AZaRzhUxZvRHVjSa/X99ud63gGRrCAOWq4Y7hbztO1aC2ySz3n0wamBKy9yQiovBjgNIHVDeacOJcEwBXo2lrcTq1vAT4QKnB7/coqWmG0WKHVq3EgJTQlVr6J8VgRJYedglYe6gCO52BF/tPiIh6FwYofYC4iQ9Jj0NijNbrcSOzHc2z+0s735CvNVHeGZIeF/I5JD8a4ciivPpNEZrNNuij1BgWor4XIiKKDAYofYAIUCZ66T8RRjnLPAfO+p9B2eYstYQjUJjq7EMRE2UvHJjMybFERL0MdzMOkfJ6I15cewRNZhsAQKVQ4OYLc3Dxed534g2VnV7mn7Q2KtsZoPiZQVm2tQRvbyoGAFw2NPTXNyo7AenxOlQ2OHplWN4hIup9GKCEyN/WHcOHO854PLbzVC02PnJlWEexW212uWRzvnMSqzeixHOyuhkGowX6KE2nr//xzjN49LN9AIC7LhuIWeP6de2EfaBUKjBlRAb+s60EABtkiYh6I5Z4QsBosWH53lIAwL1XnIenfjwSiTEanK1rwcaj58J6LkXnGmG02B1NsJ00rybFatEvMRoAcNCHRtnP95zFIx/vhSQBd148AL+/ekTYlvr+KN/RhxKrVcmBFRER9R4MUEJgzcEKNJis6JcYjYenDcMdFw/A7PH9AQRv12Bf/XDGkT0Z1U/vU5+GuNl3tpJn5b4yPPjhXtglYM6FufjDtflhnUNy+dB0/PKK87Bo9hjuv0NE1AvxX/YQ+Hino7Qze3w/OSi4ZZJjv5uvD1eitK4laO9ltNg63D9nnzNAETsBd8bVKOu9D+WrgxX41X92w2aX8JMJ/fGnWaPCPiRNpVTgkenD8eOx2WF9XyIiCg8GKEFWXm/EpmOOMs7sCf3lx89Li0PBoBTYJeD97V3bNVg4Vd2E8U+vxW3/3Aqz1d7uMfucgcbo/ok+vabYp8fbUuONR8/hl+/tgtUu4cdjs/Hs7DFcQUNEREHHACXIPt19FnYJuGBAEvJa9XzMvciRRXl/WwkstvYDCn98sussms02fFdUjT98caDN8xabHQfLHKWaMT5mUEY6V/IUVTaixbkCyd3jn++H2WbHjFGZePHGsWFt+CUior6DAUoQSZKEj3c6siM/ccueCNPyM5Eap0VlgwnrDlV2+f1WHyiX//yfbSV493vP/pajFQ0wW+2Ij1IjLyXGp9dMj9chNU4HuwQcLvfsQ6lrNuOUc/bIsz9h7wcREYUO7zBBtOd0HY6fa0KURomrnZvvudOqlfjpxBwAwDLnEtlAFVc14XB5A9RKBe65/DwAwJNfHJAHpgGu/pMx/RN87hFRKBRuZR7PAEVkY3KTY3xagkxERBQoBihB9MmuswCA6SMzEe/lBn7zBY4A5buiKtQ3WwJ+r5X7ywAABeel4P+mD8M1Y7JgtUu4b9kuuTTzg+g/6Zfo12vLA9taNcqKpcf5WVzWS0REocUAxUfl9Uaca/C+y6/FZsf/9jmChuvHty3vCHkpsRicHgebXcK3RYHPRFm131HemTEqCwqFAs/9ZAz6J0WjssEkDzBzz6D4w1ujrMigjGCAQkREIcYAxQfVjSYUvrQRl/z5a7y49iiMlrbNo98VVaGmyYyUWC0uOa/jyaZXDE0DAKw/EliAcqa2GT+cqYdSAUwb6diXJkarxr1XOEo9b2w8jgajRe4h8XWJsTDGueLnUFkDGk1W+XE5g8LBaEREFGIMUHywYl8Z6lssMNvseHndMRS+tBGbjlV5HPOFc3Ls1aOzOm0evWKYYwrqhqPnOpxh4o3InlwwIBmpcTr58Z9M6I9MfRQqDCb86X+HYLFJSIrRoH9StF+vn50YjdzkGNjsErY7e1pMVhuKKhsBMEAhIqLQY4DiA1fwkYlMfRROVTfj9n9uxd7TdQAcw9LWHKgAAPx4XOeDwy4YmIQYrQrnGkxy2cQfrvJOpsfjOrUK/+/yQQBcs1ZG9fO9Qdbdxc4s0JYT1QCAYxWNsNolJERrkJ0Q5ffrERER+YMBSifO1rVg+8laKBTA49fk46uHLsfUERmwS46ZIDa7hG8OV6LRZEV2QhQm5CZ1+po6tUre1XiDn3vzVBqM2Fni2J14+qi2K4VuviAXqXFa+XN/+0+EAmeAsvm4I1MkAqn8LH3Yp8YSEVHfwwClE2LTvwsGJCMrIRpxOjWeuWEU4qPU+OFMPZZtK5EzLNeOzfZ5quoVwxx9KN8c9m8eyhd7SyFJjp2JM9vJZERrVbjrskHy5/6u4BEKBjkClAOlBtQ1m3GojP0nREQUPgxQOiGCD/c9X9Ljo/DbacMAAH9ZdRhfO4OMa/3YF0YEKLtKan1ebmyzS1i6+SQA4KcTcrweN/eiPKTG6aBTKzE+L9Hnc3KXro/CeWmxkCRga3GN3CDLFTxERBQODFA6cPxcIw6UGqBWKtoMXrv1ojyMzNbDYLTCZLVjUFqsvBOwL/onxWBwehzsEnxebrzmQDnO1LYgKUaDG8b383pcnE6NL+67BMt/dSnS4wPvFxFlqM1FVR4lHiIiolBjgNKBL53Zk0uHpCI5VuvxnEqpwB9njYJox/jx2Gy/ezP8XW789qZiAI7gKEqj6vDY7MRoDMmI9+t8WhN9KF/+UIYGoxUalQKD0+O69JpERES+YIDihSRJrt6SMe2Xbs7PTcIDU4ciP0uPORfm+v0eYrnx+iNtlxtbbXZUNboGw+09XYcdp2qhUSlw20V5fr9XIC5y9qHUNJkBAEPS46FV80eGiIhCj3cbL74+XIkT55qgUyvlYWjt+fWUIVjxm8uQofe/lHLBwCTEalWoajRhz5k6j+cWLj+IiX/8CvOX7UKFwShnT64dm430AN4rEMmxWgzPdGVh2CBLREThwgClHSfONeL+D/YAAOZcmOt1X52u0qlVuHK4I4siZpsAQJPJig93OOaY/O+HMkx5YYM8Rn/epQNDci7eFLhNxWX/CRERhQsDlFYajBbc9c4ONBitmJCXhN9fPSKk7zfDOctk1f5ySJKjzLP2YAWMFjv6JUZjXE4iGk1W2OwSLhqUjJHZgc01CZRolAW4goeIiMJHHekT6E7sdgkPfLAHx881IVMfhdduHR/ynosrhqVBp1aipKYZB8sMGJmdIPe+zB7fD7+ZOhT/2VaC1QfKsWBGaIOl9lw4MBlRGiUkiSUeIiIKHwYobv627hi+OlQJrVqJf9w+oUtLdH0Vq1Pj8qFpWHOwAqv2lyM7IRobndNlfzwuGyqlArdelIdbw9QY21pCtAbv/eIiSJJjzD0REVE4sMTj5kf5GeiXGI0/3zBa3tE3HGaMduyps3J/OVbuL4fVLmFElh6D07u2TDhYJuQlYeKA5EifBhER9SHMoLgZ1S8Bax+cjBhteL8tVw3PgEalQFFlI17fcBwAcO3YtvvsEBER9RXMoLQS7uAEcJRRLhnsaEYtqWkG4H32ChERUV/AAKWbmDEqU/7z+NxE5CTHRPBsiIiIIsvvAGXjxo249tprkZ3tGO3+2WefeTwvSRKeeOIJZGVlITo6GlOnTsWxY8c8jqmpqcHcuXOh1+uRmJiIefPmobGxsUsX0tP9KD8TKudOyD/2Y9NBIiKi3sjvAKWpqQljx47F4sWL233+ueeew8svv4zXX38dW7duRWxsLAoLC2E0GuVj5s6diwMHDmDt2rVYvnw5Nm7ciLvvvjvwq+gFkmO1+PklAzA2JxGzzve+ESAREVFfoJDEdLBAvlihwKeffopZs2YBcGRPsrOz8dBDD+G3v/0tAKC+vh4ZGRlYunQpbr75Zhw6dAj5+fnYvn07Jk6cCABYtWoVrr76apw5cwbZ2Z1nDwwGAxISElBfXw+9nrM5iIiIegJ/7t9B7UEpLi5GeXk5pk6dKj+WkJCASZMmYcuWLQCALVu2IDExUQ5OAGDq1KlQKpXYunVru69rMplgMBg8PoiIiKj3CmqAUl7u2E8mI8Nzc72MjAz5ufLycqSnp3s8r1arkZycLB/T2qJFi5CQkCB/5OTkBPO0iYiIqJvpEat4FixYgPr6evnj9OnTkT4lIiIiCqGgBiiZmY6lshUVFR6PV1RUyM9lZmaisrLS43mr1Yqamhr5mNZ0Oh30er3HBxEREfVeQQ1QBg4ciMzMTKxbt05+zGAwYOvWrSgoKAAAFBQUoK6uDjt37pSP+frrr2G32zFp0qRgng4RERH1UH6PTW1sbERRUZH8eXFxMfbs2YPk5GTk5ubi/vvvxx//+EcMGTIEAwcOxOOPP47s7Gx5pc+IESMwffp03HXXXXj99ddhsVhw33334eabb/ZpBQ8RERH1fn4HKDt27MCVV14pf/7ggw8CAO644w4sXboUjzzyCJqamnD33Xejrq4Ol156KVatWoWoKNfOwO+99x7uu+8+TJkyBUqlErNnz8bLL78chMshIiKi3qBLc1AihXNQiIiIep6IzUEhIiIiCgYGKERERNTtMEAhIiKibocBChEREXU7DFCIiIio22GAQkRERN2O33NQugOxMpq7GhMREfUc4r7ty4STHhmgNDQ0AAB3NSYiIuqBGhoakJCQ0OExPXJQm91uR2lpKeLj46FQKIL62gaDATk5OTh9+nSfGwLHa+e189r7Dl47rz0S1y5JEhoaGpCdnQ2lsuMukx6ZQVEqlejfv39I36Mv75rMa+e19zW8dl57XxPJa+8scyKwSZaIiIi6HQYoRERE1O0wQGlFp9PhD3/4A3Q6XaRPJex47bz2vobXzmvva3rStffIJlkiIiLq3ZhBISIiom6HAQoRERF1OwxQiIiIqNthgEJERETdTo8IUDZu3Ihrr70W2dnZUCgU+Oyzzzyer6iowJ133ons7GzExMRg+vTpOHbsWLuvJUkSZsyY0e7rrFu3DhdffDHi4+ORmZmJ//u//4PVau30/NavX4/x48dDp9Nh8ODBWLp0qV/nH+prv+KKK6BQKDw+7rnnHo9jSkpKMHPmTMTExCA9PR0PP/ywT9f+0UcfYfjw4YiKisLo0aOxYsUKv88v0te/d+9ezJkzBzk5OYiOjsaIESPwt7/9zafz6+z677zzzjbvPX369G5z7e6qq6vRv39/KBQK1NXVdfnaP/nkE0ybNg0pKSlQKBTYs2ePT9cdzmtv/bxCocD777/f5Wvvys99OK596dKl7V67QqFAZWVll669J/zM99Z/6wFgy5YtuOqqqxAbGwu9Xo/JkyejpaVFfr6mpgZz586FXq9HYmIi5s2bh8bGxi5fe0NDA+6//37k5eUhOjoaF198MbZv3+7z9benRwQoTU1NGDt2LBYvXtzmOUmSMGvWLJw4cQKff/45du/ejby8PEydOhVNTU1tjn/ppZfaHY+/d+9eXH311Zg+fTp2796NDz74AF988QV+97vfdXhuxcXFmDlzJq688krs2bMH999/P37xi19g9erVPp1/uK79rrvuQllZmfzx3HPPyc/ZbDbMnDkTZrMZmzdvxr/+9S8sXboUTzzxRIfntnnzZsyZMwfz5s3D7t27MWvWLMyaNQv79+/3+/wief07d+5Eeno63n33XRw4cACPPvooFixYgL///e9dun5h+vTpHu/9n//8p9tcu7t58+ZhzJgxPp2bL9fe1NSESy+9FM8++6xPr+kunNe+ZMkSj2NmzZrVpWvv6s99OK79pptu8niurKwMhYWFuPzyy5Genh7wtQvd+We+N/9bv2XLFkyfPh3Tpk3Dtm3bsH37dtx3330eI+Xnzp2LAwcOYO3atVi+fDk2btyIu+++u8vX/otf/AJr167Fv//9b+zbtw/Tpk3D1KlTcfbsWb+/F+4X3qMAkD799FP58yNHjkgApP3798uP2Ww2KS0tTXrzzTc9vnb37t1Sv379pLKysjavs2DBAmnixIkex3/xxRdSVFSUZDAYvJ7PI488Io0cOdLjsZtuukkqLCz06fz9Eei1X3755dJvfvMbr6+7YsUKSalUSuXl5fJjr732mqTX6yWTyeT162688UZp5syZHo9NmjRJ+n//7//5dX6+CtX1t+eXv/yldOWVV3Z4TGfXL0mSdMcdd0jXXXedX+/dnlBf+6uvvipdfvnl0rp16yQAUm1tbYfH+3LtQnFxsQRA2r17d6fn0Z5QXnsg/z+G8+c+XD/zlZWVkkajkd55550Oj+sNP/O9+d/6SZMmSY899pjX1z148KAEQNq+fbv82MqVKyWFQiGdPXvW69d1du3Nzc2SSqWSli9f7nHM+PHjpUcffbTji+1Aj8igdMRkMgEAoqKi5MeUSiV0Oh02bdokP9bc3IxbbrkFixcvRmZmZruv4/4aABAdHQ2j0YidO3d6ff8tW7Zg6tSpHo8VFhZiy5YtAV2PP3y9dgB47733kJqailGjRmHBggVobm6Wn9uyZQtGjx6NjIwM+bHCwkIYDAYcOHDA6/t3du3+nF8ggnX97amvr0dycnKHx/j6d79+/Xqkp6dj2LBhuPfee1FdXd3ptXUmmNd+8OBBLFy4EO+8806nm3cJveHnXpg/fz5SU1Nx4YUX4p///Gen28BH8uc+VD/z77zzDmJiYvCTn/ykw/fvDT/zvfXf+srKSmzduhXp6em4+OKLkZGRgcsvv9zje7NlyxYkJiZi4sSJ8mNTp06FUqnE1q1bvb5/Z9dutVphs9na/b525We+xwcow4cPR25uLhYsWIDa2lqYzWY8++yzOHPmDMrKyuTjHnjgAVx88cW47rrr2n2dwsJCbN68Gf/5z39gs9lw9uxZLFy4EAA8Xqe18vJyjxs7AGRkZMBgMHjU/ULB12u/5ZZb8O677+Kbb77BggUL8O9//xu33nprp9cgnvPG29eJr/H1/CJ9/a1t3rwZH3zwQadpz86uH3Ckut955x2sW7cOzz77LDZs2IAZM2bAZrMFeNUOwbp2k8mEOXPm4C9/+Qtyc3N9fn9frj1Ugvn3vnDhQnz44YdYu3YtZs+ejV/+8pd45ZVXOnz/SP7ch+pn/u2338Ytt9yC6OjoDt+/N/zM99Z/60+cOAEAePLJJ3HXXXdh1apVGD9+PKZMmSL3qpSXl7cp4anVaiQnJwf0b7249vj4eBQUFODpp59GaWkpbDYb3n33XWzZsqVrP/MB514iBO2kzXbs2CGNHTtWAiCpVCqpsLBQmjFjhjR9+nRJkiTp888/lwYPHiw1NDR0+DovvPCCpNfrJZVKJcXExEiLFi2SAEjvv/++JEmSFBsbK3+IlOaQIUOkZ555xuN1/ve//0kApObmZp/OP5TX3h6Rxi8qKpIkSZLuuusuadq0aR7HNDU1SQCkFStWSKdOnfK49j/96U+SJEmSRqORli1b5vF1ixcvltLT07t0fuG+fnf79u2TUlNTpaefflp+rCvX39rx48clANJXX33lyyXLQnXtDzzwgHTTTTfJz3/zzTceJZ5gXHuwSzySFPy/d+Hxxx+X+vfvL0lS1649WD/34bj2zZs3SwCkHTt2yI/15p95Seqd/9Z/9913EgBpwYIFHl83evRo6Xe/+50kSZL0pz/9SRo6dGib90tLS5NeffXVLl17UVGRNHnyZPn8LrjgAmnu3LnS8OHDA/oeSJIkqQMPbbqPCRMmYM+ePaivr4fZbEZaWhomTZokp7G+/vprHD9+HImJiR5fN3v2bFx22WVYv349AODBBx/EAw88gLKyMiQlJeHkyZNYsGABBg0aBAAeqxDENtWZmZmoqKjweN2Kigro9fpOfxsJhs6uvT2TJk0CABQVFeG8885DZmYmtm3b5nGMuKbMzExkZ2d7XLsofXi7dvcSWiDn549gXL9w8OBBTJkyBXfffTcee+wx+fGuXH9rgwYNQmpqKoqKijBlyhS/rrW1YFz7119/jX379uHjjz8GALm8kZqaikcffRSPP/540K49mIL59976mKeffhomk6nb/twH+9rfeustjBs3DhMmTJAf680/80Dv/Lc+KysLAJCfn+/xdSNGjEBJSYl8Da1XaVmtVtTU1Mh/h4Fe+3nnnYcNGzagqakJBoMBWVlZuOmmm+TvaUACDm0iBD5EpUePHpWUSqW0evVqSZIkqaysTNq3b5/HBwDpb3/7m3TixAmvr/P4449LOTk5ktVq9XrMI488Io0aNcrjsTlz5oSlcao9ra+9PZs2bZIASHv37pUkydUkW1FRIR/zxhtvSHq9XjIajV5f58Ybb5SuueYaj8cKCgrabZT05/y8CdX1S5Ik7d+/X0pPT5cefvhhn88nkOs/ffq0pFAopM8//9zn95Gk0F17UVGRx/8X//znPyUA0ubNmz1+Hlrz59pDkUFpLdC/99b++Mc/SklJSR2+Vzh/7kN97Q0NDVJcXJz0yiuv+HQ+veFnvj294d96u90uZWdnt2mSHTdunJxVEU2y7tmy1atX+9Qk68+1S5Ik1dTUSAkJCdIbb7zR4XV0pEcEKA0NDdLu3bul3bt3SwCkF198Udq9e7d06tQpSZIk6cMPP5S++eYb6fjx49Jnn30m5eXlSTfccEOHr9neD8Bzzz0n/fDDD9L+/fulhQsXShqNptMfkhMnTkgxMTHSww8/LB06dEhavHixpFKppFWrVvl8/qG89qKiImnhwoXSjh07pOLiYunzzz+XBg0aJE2ePFk+xmq1SqNGjZKmTZsm7dmzR1q1apWUlpbWJlXY2nfffSep1Wrp+eeflw4dOiT94Q9/kDQajbRv3z75mED+bsJ9/fv27ZPS0tKkW2+9VSorK5M/Kisru3T9DQ0N0m9/+1tpy5YtUnFxsfTVV19J48ePl4YMGdJh4BfOa2+tdYkn0GuXJEmqrq6Wdu/eLaeC33//fWn37t1SWVlZt7j2L774QnrzzTelffv2SceOHZNeffVVKSYmRnriiSe6fO1d+bkP59/7W2+9JUVFRXX69+3rtfeUn/ne+G+9JEnSX//6V0mv10sfffSRdOzYMemxxx6ToqKiPMpb06dPl84//3xp69at0qZNm6QhQ4ZIc+bM6fK1r1q1Slq5cqV04sQJac2aNdLYsWOlSZMmSWazudNr96ZHBCjiH83WH3fccYckSZL0t7/9Terfv7+k0Wik3Nxc6bHHHutweawktR+gXHnllVJCQoIUFRUlTZo0SVqxYoXP5zdu3DhJq9VKgwYNkpYsWeLX+Yfy2ktKSqTJkydLycnJkk6nkwYPHiw9/PDDUn19vcf7nDx5UpoxY4YUHR0tpaamSg899JBksVg6Pb8PP/xQGjp0qKTVaqWRI0dK//vf/zyeD+TvJtzX/4c//KHd98jLy+vS9Tc3N0vTpk2T0tLSJI1GI+Xl5Ul33XWXx3LuSF+7t/f05YbV2d/9kiVL2j3/P/zhD93i2leuXCmNGzdOiouLk2JjY6WxY8dKr7/+umSz2bp87V35uQ/n33tBQYF0yy23+HRevlx7T/mZ743/1guLFi2S+vfvL8XExEgFBQXSt99+6/F8dXW1NGfOHCkuLk7S6/XSz372M4/+zECv/YMPPpAGDRokabVaKTMzU5o/f75UV1fX6et2RCFJnaypIyIiIgqzHr/MmIiIiHofBihERETU7TBAISIiom6HAQoRERF1OwxQiIiIqNthgEJERETdDgMUIiIi6nYYoBAREVG3wwCFiHoNhUKBzz77LNKnQURBwACFiLrszjvvhEKhwD333NPmufnz50OhUODOO+8M2vs9+eSTGDduXNBej4i6HwYoRBQUOTk5eP/999HS0iI/ZjQasWzZMuTm5kbwzIioJ2KAQkRBMX78eOTk5OCTTz6RH/vkk0+Qm5uL888/X37MZDLh17/+NdLT0xEVFYVLL70U27dvl59fv349FAoF1q1bh4kTJyImJgYXX3wxjhw5AgBYunQpnnrqKezduxcKhQIKhQJLly6Vv76qqgrXX389YmJiMGTIEHzxxRehv3giCjoGKEQUND//+c+xZMkS+fN//vOf+NnPfuZxzCOPPIL//ve/+Ne//oVdu3Zh8ODBKCwsRE1Njcdxjz76KF544QXs2LEDarUaP//5zwEAN910Ex566CGMHDkSZWVlKCsrw0033SR/3VNPPYUbb7wRP/zwA66++mrMnTu3zWsTUffHAIWIgubWW2/Fpk2bcOrUKZw6dQrfffcdbr31Vvn5pqYmvPbaa/jLX/6CGTNmID8/H2+++Saio6Px9ttve7zWn/70J1x++eXIz8/H7373O2zevBlGoxHR0dGIi4uDWq1GZmYmMjMzER0dLX/dnXfeiTlz5mDw4MF45pln0NjYiG3btoXte0BEwaGO9AkQUe+RlpaGmTNnYunSpZAkCTNnzkRqaqr8/PHjx2GxWHDJJZfIj2k0Glx44YU4dOiQx2uNGTNG/nNWVhYAoLKystN+Fvevi42NhV6vR2VlZZeui4jCjwEKEQXVz3/+c9x3330AgMWLFwf8OhqNRv6zQqEAANjtdr++TnytL19HRN0LSzxEFFTTp0+H2WyGxWJBYWGhx3PnnXcetFotvvvuO/kxi8WC7du3Iz8/3+f30Gq1sNlsQTtnIup+mEEhoqBSqVRyuUalUnk8Fxsbi3vvvRcPP/wwkpOTkZubi+eeew7Nzc2YN2+ez+8xYMAAFBcXY8+ePejfvz/i4+Oh0+mCeh1EFFkMUIgo6PR6vdfn/vznP8Nut+O2225DQ0MDJk6ciNWrVyMpKcnn1589ezY++eQTXHnllairq8OSJUuCOgiOiCJPIUmSFOmTICIiInLHHhQiIiLqdhigEBERUbfDAIWIiIi6HQYoRERE1O0wQCEiIqJuhwEKERERdTsMUIiIiKjbYYBCRERE3Q4DFCIiIup2GKAQERFRt8MAhYiIiLqd/w8bwH73ohxTvQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": [
        "#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": "85120340-89d3-4025-92ba-58244b972c1e"
      },
      "execution_count": null,
      "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": "code",
      "source": [
        "#Take differnce for making stationary\n",
        "dap = ap1 - ap1.shift(1)\n",
        "dap.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "2o5iuXEgGyU2",
        "outputId": "079320bc-05b8-40a1-f3a3-827c5a0dd1d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Month'>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "a7cb0858-4064-4349-9c05-13667898c9ec"
      },
      "execution_count": null,
      "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": [
        "dapS = dap - dap.shift(12)\n",
        "dapS.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "AksUm_4cGshr",
        "outputId": "db5f187c-3caa-4656-cea7-034c1ec0641e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Month'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q:Is there a seasonality in the data?"
      ],
      "metadata": {
        "id": "IEuq91EnKCD7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "dapS=dapS.dropna()\n",
        "result = adfuller(dapS['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": "HO9h3ZoNHs64",
        "outputId": "4cd28930-4a08-457b-cdc4-90d727de86fc"
      },
      "execution_count": null,
      "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": "code",
      "source": [
        "from statsmodels.graphics import tsaplots\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = tsaplots.plot_acf(dapS['Passengers'], lags=36)\n",
        "plt.show()\n",
        "fig = tsaplots.plot_pacf(dapS['Passengers'], lags=36)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 942
        },
        "id": "nqucss19GD6m",
        "outputId": "a767e3d2-7c46-4567-bb20-f0869594fc72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/graphics/tsaplots.py:348: 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",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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": "lkuy9MQ6J9Jr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": 512
        },
        "id": "91jmn1dDHa-r",
        "outputId": "c3eb863c-d8d3-4ffd-a8c9-fbc3e147cc02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/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:                             Thu, 04 May 2023   AIC                           1022.393\n",
              "Time:                                     06:09:09   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>Thu, 04 May 2023</td>         <th>  AIC                </th> <td>1022.393</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>06:09:09</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": 8
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "FHhV-VzSCEBp",
        "outputId": "c5e360be-b35c-4769-f01c-a8b30dbd55a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/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:                             Thu, 04 May 2023   AIC                           1020.639\n",
              "Time:                                     06:10:00   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>Thu, 04 May 2023</td>         <th>  AIC                </th> <td>1020.639</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>06:10:00</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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(ap1['Passengers'],order=(1,1,0), seasonal_order=(0,1,0,12))\n",
        "model_fit=model.fit()\n",
        "model_fit.summary()"
      ],
      "metadata": {
        "id": "u2noR4Jhl7Vy",
        "outputId": "57b4b6db-f516-4d50-a819-29d95c565b12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/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, 0)x(0, 1, 0, 12)   Log Likelihood                -508.197\n",
              "Date:                            Thu, 04 May 2023   AIC                           1020.393\n",
              "Time:                                    06:10:03   BIC                           1026.144\n",
              "Sample:                                01-01-1949   HQIC                          1022.730\n",
              "                                     - 12-01-1960                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -0.3076      0.073     -4.186      0.000      -0.452      -0.164\n",
              "sigma2       137.0108     14.623      9.370      0.000     108.351     165.670\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 7.75\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, 0)x(0, 1, 0, 12)</td> <th>  Log Likelihood     </th> <td>-508.197</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Thu, 04 May 2023</td>        <th>  AIC                </th> <td>1020.393</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>06:10:03</td>            <th>  BIC                </th> <td>1026.144</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-01-1949</td>           <th>  HQIC               </th> <td>1022.730</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>   -0.3076</td> <td>    0.073</td> <td>   -4.186</td> <td> 0.000</td> <td>   -0.452</td> <td>   -0.164</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>  137.0108</td> <td>   14.623</td> <td>    9.370</td> <td> 0.000</td> <td>  108.351</td> <td>  165.670</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>7.75</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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=ap1[:-12]\n",
        "test=ap1[-12:]\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
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        "id": "qfXCZZsY7557",
        "outputId": "d62d512b-9b8e-4c5e-e64c-1dd22b6d9e1b"
      },
      "execution_count": 4,
      "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",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2978cd21-fa17-4929-9b77-71949acac4bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e669334c-fa2f-45b6-9cdd-72e180f67de8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e669334c-fa2f-45b6-9cdd-72e180f67de8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 132,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 132,\n        \"samples\": [\n          \"1953-09\",\n          \"1955-12\",\n          \"1950-08\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106,\n        \"min\": 104,\n        \"max\": 559,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          355,\n          115,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "model=SARIMAX(train['Passengers'],order=(1,1,0), 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 ARIMA');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "-nvFNkCqMbhZ",
        "outputId": "dc559549-2a59-4386-e11e-c91cd52ab0a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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/python3.10/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 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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 ARIMA');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "DMzg0DBZ-g5T",
        "outputId": "16c5e779-8517-4fef-b438-30f7e9938b56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction.columns=['Forecast']\n",
        "test_prediction"
      ],
      "metadata": {
        "id": "hBM8FvHtmVKd",
        "outputId": "61edd9ac-11f2-4518-f34f-44fb049d384e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Forecast\n",
              "Month              \n",
              "1960-01  424.109830\n",
              "1960-02  407.055669\n",
              "1960-03  470.825702\n",
              "1960-04  460.881615\n",
              "1960-05  484.868020\n",
              "1960-06  536.871326\n",
              "1960-07  612.870522\n",
              "1960-08  623.870717\n",
              "1960-09  527.870670\n",
              "1960-10  471.870681\n",
              "1960-11  426.870679\n",
              "1960-12  469.870679"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9073d42-5c3d-4a1b-aab1-5773083afdc8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Forecast</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1960-01</th>\n",
              "      <td>424.109830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-02</th>\n",
              "      <td>407.055669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-03</th>\n",
              "      <td>470.825702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-04</th>\n",
              "      <td>460.881615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-05</th>\n",
              "      <td>484.868020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-06</th>\n",
              "      <td>536.871326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-07</th>\n",
              "      <td>612.870522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-08</th>\n",
              "      <td>623.870717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-09</th>\n",
              "      <td>527.870670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-10</th>\n",
              "      <td>471.870681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-11</th>\n",
              "      <td>426.870679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-12</th>\n",
              "      <td>469.870679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9073d42-5c3d-4a1b-aab1-5773083afdc8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9073d42-5c3d-4a1b-aab1-5773083afdc8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9073d42-5c3d-4a1b-aab1-5773083afdc8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction['Passengers']=test['Passengers'].values\n",
        "test_prediction"
      ],
      "metadata": {
        "id": "s8sm8B7VmbA-",
        "outputId": "cc45dd44-d965-4dc8-ff82-f35a8ebf24ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Forecast  Passengers\n",
              "Month                          \n",
              "1960-01  424.109830         417\n",
              "1960-02  407.055669         391\n",
              "1960-03  470.825702         419\n",
              "1960-04  460.881615         461\n",
              "1960-05  484.868020         472\n",
              "1960-06  536.871326         535\n",
              "1960-07  612.870522         622\n",
              "1960-08  623.870717         606\n",
              "1960-09  527.870670         508\n",
              "1960-10  471.870681         461\n",
              "1960-11  426.870679         390\n",
              "1960-12  469.870679         432"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Forecast</th>\n",
              "      <th>Passengers</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1960-01</th>\n",
              "      <td>424.109830</td>\n",
              "      <td>417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-02</th>\n",
              "      <td>407.055669</td>\n",
              "      <td>391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-03</th>\n",
              "      <td>470.825702</td>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-04</th>\n",
              "      <td>460.881615</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-05</th>\n",
              "      <td>484.868020</td>\n",
              "      <td>472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-06</th>\n",
              "      <td>536.871326</td>\n",
              "      <td>535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-07</th>\n",
              "      <td>612.870522</td>\n",
              "      <td>622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-08</th>\n",
              "      <td>623.870717</td>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-09</th>\n",
              "      <td>527.870670</td>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-10</th>\n",
              "      <td>471.870681</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-11</th>\n",
              "      <td>426.870679</td>\n",
              "      <td>390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960-12</th>\n",
              "      <td>469.870679</td>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1ad7207-f797-4a71-8e55-1606e8677b97 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1ad7207-f797-4a71-8e55-1606e8677b97');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def mae(y_hat, y_true):\n",
        "    return np.mean(np.abs(y_hat-y_true))\n",
        "\n",
        "y_true = test_prediction['Passengers'].values\n",
        "arima_preds = test_prediction['Forecast'].values\n",
        "\n",
        "\n",
        "print('ARIMA MAE: %0.3f' % mae(arima_preds, y_true))"
      ],
      "metadata": {
        "id": "Jec4HMXZmY9z",
        "outputId": "0f569c6a-e3cd-4f53-b348-33d5e6ab138a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARIMA MAE: 18.528\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()"
      ],
      "metadata": {
        "id": "WMG0xaMOWCIT",
        "outputId": "f6f68c19-735d-428a-b22d-d1e15c719c09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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yihJPU28u8VxwwQX49NNPsWDBAixcuBADBw7ESy+9hLlz58rHPPLII2hqasLdd9+Nuro6XHrppVi1apXHvijvvfce7rvvPkyZMgVKpRKzZ8/Gyy+/HLyrIiIi6oMaTK4hbYJYzdOrMygAcM011+Caa67x+rxCocDChQuxcOFCr8ckJydj2bJl/r41ERERdaC9HpRYbc8s8XCzQCIiol7CfYqs4CrxMEAhIiKiCBA9KPqo9ko8PasHhQEKERFRL9FuiceZQenVy4yJiIio+3I1ybpW8fTUZcYMUIiIiHqJhvYmyWp75jJjBihERES9hCjjeEyS7aHLjBmgEBER9RKiB6U3TJJlgEJERNRLNHTQJMsAhYiIiCJCNMl6jLp360GRJCki5xUIBihERES9gMlqg9lqB9D+qHubXYLJ+XxPwACFiIioF3AfxOYeoMRo1W7H9JwyDwMUIiKiXkAsMY7VqqBSKuTHVUoFojU9b5osAxQiIqJeoL0GWaEnTpNlgEJERNQLyDNQdG0DlDgxC6UHbRjIAIWIiKgXEBkU9xU8Qk9caswAhYiIqBdoNLUdcy/IS43Zg0JEREThJO9k3E6JJ7YHjrtngEJERBQE9c0WnGswRez9DR0GKGJYGwMUIiKiPsNsteP6177DVc+vh8G53DfcGtuZIiu4SjwMUIiIiPqMFfvKcOJcExpMVpytbYnIOTT6tMyYPShERER9giRJeHtTsfx5pLIUcgalg2XGzSzxEBER9Q3bT9Zi39l6+fNIDUMTk2TbW8UTw0FtREREfctb357w+DxSS3l9mSTLHhQiIqI+4FR1E9YeqgAADEiJARD5Ek+Hk2TZg0JERNT7LfnuJCQJuHxoGkb1SwAQyRKPD6t42INCRETUuxktNny04zQAYN6lA+XMRaQzKO1OkmWJh4iIqG+oMBjRZLYhSqPEZUNSXUt5I5Sl6HiSLEfdExER9Qn1LY5VM4nRWigUiohmKYwWG8w2O4D2m2S5mzEREVEfYWhx3Oz10Y6AIJKNqO59L3HadpYZc5IsERFR3yBG2idEO5pSYyM4a8S9vKNUKto8L87NYpNgsvaMMg8DFCIiogCIEo/euWomkvvdNHTQfwIAsVqV/OfmHtKHwgCFiIgoAIaW9jMoTebwBwANJu9TZAFArVJCp3bc8nvKNFkGKERERAEQJR69HKCIHpQIlni8BCiAK7vSUxplGaAQEREFwFXiEU2ykSvxdDRFVuhps1AYoBAREQXAtYon8k2yhlb9MO3pabNQGKAQEREFQM6gOAMU9wyKJElhPZeaZse5JMV2EKBoI1eCCgQDFCIiogB4W2ZslwCjxR7Wc6lrNgMAkmK0Xo+JZIYnEAxQiIiIAtC6rBKjcS3lDXcQUNPUeYAiMjzNEVhlFAgGKERERAGobzVJVqlURKyMUuvMoCTHeg9QYpznxgwKERFRL9a6xANEroxS0yR6UDov8bAHhYiIqJcyWmwwWx19Jnq3ACVSS41dPSjem2RZ4iEiIurlRP+JQuG5OV9sBIahSZLkUw9KjI4lHiIiol5NniIbpfHYnC9WDgLCl6VosdhgcmZzOupBieQguUAwQCEiIvKTawaK5+TWSAQBInuiVSvlRtj2iM0MmUEhIiLqpcQUWfcGWSAyjai1okE2RgOFQuH1uFj2oBAREfVu7iUed5FYxVPrw5A2ILKbGQaCAQoREZGf6r3sfROJEo8vM1AATpIlIiLq9cQqnjYlHrnPI3xlFHkFTycBCpcZExER9XLemmQjUUapbep8BgrASbJERES9nrcm2ciUeBzBUnInPSji3MxWOyy28G5mGAgGKERERH6Sm2S9rOIJZ5aiptm3Ek+M20C55jCWoALFAIWIiMhPnTbJhnGSrCjxdNYkq1UroVU5bvsNJkvIz6urGKAQEVGPYrLaUNlgjOg5tLdRIOA+ByX8TbKJnZR4AFcQU91oDuk5BYNfAcqTTz4JhULh8TF8+HD5eaPRiPnz5yMlJQVxcXGYPXs2KioqPF6jpKQEM2fORExMDNLT0/Hwww/Dau0ZDTtERBR5D364Fxc9sw5FlY0ROwfRg+KtSTacJZ46H3tQACBDrwMAVBgiG+D5wu8MysiRI1FWViZ/bNq0SX7ugQcewJdffomPPvoIGzZsQGlpKW644Qb5eZvNhpkzZ8JsNmPz5s3417/+haVLl+KJJ54IztUQEVGvVtlgxMp9ZbBLwOFyQ8TOo7vMQZEkya0HpeNVPACQoY8C0DMCFHXnh7T6ArUamZmZbR6vr6/H22+/jWXLluGqq64CACxZsgQjRozA999/j4suughr1qzBwYMH8dVXXyEjIwPjxo3D008/jf/7v//Dk08+Ca228+iPiIj6rv/94AhOgMg1etrtEho6KfE0m22w2yWPjQRDodlsg9m5UWBnk2QB9wDFFNLzCga/MyjHjh1DdnY2Bg0ahLlz56KkpAQAsHPnTlgsFkydOlU+dvjw4cjNzcWWLVsAAFu2bMHo0aORkZEhH1NYWAiDwYADBw54fU+TyQSDweDxQUREfc8Xe0vlP4ezEdVdo9kqB0mtV/GIDAoQnvPzdaNAITOh52RQ/ApQJk2ahKVLl2LVqlV47bXXUFxcjMsuuwwNDQ0oLy+HVqtFYmKix9dkZGSgvLwcAFBeXu4RnIjnxXPeLFq0CAkJCfJHTk6OP6dNRES9wOmaZuwuqZM/j9SeMmKKrFatRJTGMyjQqZVQObMm4WiUde8/6WijQCE93tGDUt4DAhS/SjwzZsyQ/zxmzBhMmjQJeXl5+PDDDxEdHR30kxMWLFiABx98UP7cYDAwSCEi6mPcsycA0BShke1yg2xU254PhUKBWK0KBqM1PBkUH2egCCKDUtkbSzzuEhMTMXToUBQVFSEzMxNmsxl1dXUex1RUVMg9K5mZmW1W9YjP2+trEXQ6HfR6vccHERH1LV86A5Qs5022OUIZFG9j7oVwNsr6OuZeED0oPSGD0qUApbGxEcePH0dWVhYmTJgAjUaDdevWyc8fOXIEJSUlKCgoAAAUFBRg3759qKyslI9Zu3Yt9Ho98vPzu3IqRETUix2raMDh8gZoVArMOr8fgPBuyOfO2wwUIZzTZH3dKFAQAUp9iwVGS/eeJutXgPLb3/4WGzZswMmTJ7F582Zcf/31UKlUmDNnDhISEjBv3jw8+OCD+Oabb7Bz50787Gc/Q0FBAS666CIAwLRp05Cfn4/bbrsNe/fuxerVq/HYY49h/vz50Ol0IblAIiLq+UR55/KhacgWGZQINckavCwxFmLCOKyt1lni8WUGCgDoo9SI0jhu/d29UdavHpQzZ85gzpw5qK6uRlpaGi699FJ8//33SEtLAwD89a9/hVKpxOzZs2EymVBYWIhXX31V/nqVSoXly5fj3nvvRUFBAWJjY3HHHXdg4cKFwb0qIiLqVf63rwwAcO3YbFhtjiU0kepBESUebxmUuDDuaFzrZw+KQqFAhj4Kp6qbUWEwIS8lNpSn1yV+BSjvv/9+h89HRUVh8eLFWLx4sddj8vLysGLFCn/eloiI+jC7XcLJqiYAwKSBKdhzug5ABFfxGNufIivEasNX4qltcgRLvvagAJADlO7eh8K9eIiIqFura7HIc0eSY7XyOPlILzP2VuIJZ5NsjY8bBboTfSiVDFCIiIgCV93oWBKbEK1xDiRzTWuNBEMnJZ7YcK7iESUeH3tQACDTuR9PeT0DFCIiooCdcwYoqXGOm3C497tpTaziaT1FVnCt4gljk2wAGZSKhu49C4UBChERdWvVjY6bcEqc4zd/MdI9UqPuvW0UKISrSVaSJLkHJdHPHhQAqGAGhYiIKHDVrTIoIkNhtNhhE80pYSQmyXY6ByXEAVST2QazzbFRYGAZFAYoREREAat2NoKmxDoyKKJJFohMFsVV4vGyiidMJSgxRVanViJa0/lGgUKmmCZbb4QkhT/A8xUDFCIi6taq5AyKI0DRqpRQOzfka47ANNnO56CEKUBx6z/xZaNAId3ZJGuy2uVsUHfEAIWIiLq1KrkHxVHGUCgUEetDsdjs8uohbz0o4WqSFUuME/1YwQMAURqV3LPSnWehMEAhIqJurXUPChC5lTxiiTEAxEd52ywwPE2yrgyK7w2yQka8sw+FAQoREVFg5B6UONeebeHc78admCIbp1NDrWr/FhquHpQaeYqsfxkUAMhI6P67GjNAISKibk0sM051C1BinSWecG8Y6Joi632nmHCNuq8NYIqskBHv+F5252myDFCIiKjbMlps8o0+xa3E4+rzCG+AIsoqCR1kLUT5yWS1w+pcBhxskiTh22PnAAA5STF+f71YaswMChERUQDECh6tSol4nStrEalx9+ec01fT4nVej4l1O89QlaB2nqrF3jP10KqVuH58P7+/XpR4Kgzdd5osAxQiImrDZpfwzeFK/HXtUdQ5swaRUO22gsd9KW2kNgwUY/fT4rwHKFq1Elpnf0qohrW9vakYADBrXLZH6ctXosTTnZtkvRfRiIiozzEYLXjv+xIs23YKp2taADhWq/ziskEROZ/qJs8ZKILIUnTHDArgCKDMzfaQBFCna5qx+kA5AGDepYH9vWQmdP9VPAxQiIhI9tsP92LNwQqPxyJ5E2s9A0UQTbJhz6D4HKCoUdtsCUmPzNLNJ2GXgMuGpGJYZnxAryF6UM41mGC12b2uSIqk7ndGREQUMYfKDQCAhwuH4ddThgAAapstHX1JSIkeFDHmXhA9KOEe1OZrgBKqOS0NRgs+2H4aAPDzSwcG/DqpcTooFYBdci3j7m4YoBAREQDAbpdQ7tzh9rpx2eiX6PgtuzaCNzDXEuNWGRRnD0q4R9370oMCAHrnGPz6luAGdx/uOINGkxXnpcXi8iFpAb+OSqmQg6zuWuZhgEJERACAqiYTLDYJCoWjBCBGqNdEtEm24x6UcC8z9jWDIgIqEWAFy9eHHeW32y7Kg1Lp+/477Ul3TpOt7KYreRigEBERAKCszvGbdHq8DhqVUh4AFtEMSpO3HpTwN8kaLTY0OCfJdhagiJKUKFEFy9laR+PyiCx9l19L7McT7CxPsDBAISIiAEBZvePml5UQDcA1Qj2yPShtx9wDiMhmge4zWTqaJAu4AqqqIGZQ7HYJpc4SXHZidJdfT+zGXMcAhYiIurMy+ebnSP2LDEp9iyVkE1E742qS9cygRGKzQPfyjvtMlvaIklR1EDMo1U1mmK12KBSuZcJdwQwKERH1CCJAERmUhGgNxH04Er9l2+0SapwlntYllUhsFigClNROyjuAWw9KEMtjIsMlSnBdJTIo9RHsMeoIAxQiIgIAlNaJEo/jt3OVUiHfxCLRh1LfYoHNLgFou2NvJDYL9HUFD+AqSQUzgyL+foJR3gGAxGhXhqw7YoBCREQA3Es8rhtgcgT7UMQU2YRoDbRqz9tVbAQzKJ01yAKuklQwe1BKnU3M2QnBCVDYg0JERD1CWasMCgAkOW+0NRHIoHibIgu4VvGYbXaYreHpj/ErQHFmUBpNVhgtwQmiXBmUrvefAEACe1CIiKi7s9klVDhvwFluv6EnOW9itRHoU6jyMgMFAGKcg9oAoCVMS439CVD0UWp5w8Bg9aGU1ge3xOPqQWGAQkRE3VRlgxE2uwS124RRwNX7EYkMircpsgCgUSnlsk+odgxuzZ8eFIVCIWd+gtWHIko8WUEq8XAVDxERdXvi5pehj4LKbUKpWGpcF4EMSrWXfXgEuVE2TEuN/cmgAO6zUIIVoDgyKP2C3CRb12KBJElBec1gYoBCRERuQ9o8+xtcPSjh/y27yssUWcG1YWDoSzySJMkBSrqvAYo8TbbrwZ3ZapczOEHrQXGWeGx2KSzfQ38xQCEiInnMfVar384j2oPS4L0HBQjvsLYGkxUmZzOut/NpzTWsrevfuwqDEZIE6NSuLQi6KkrjKpNFIkPWGQYoRETkasBsnUGJZA9Kk/ceFMDVKBuOAEVkT+J0akRrVZ0c7ZAaxB6Us24zUDqbYusrhcI156Y79qEwQCEiIlcGpVWA0i16ULxkLMK5YWCVn/0nQHB7UIK9xFhI7MYreRigEBGRqweldYkngnNQRGmk9T48Qqwzg9IYjgyKHyt4BNGDEoxlxq23IQgWZlCIiKhbk3fJTWjdg+IIDgxGKyxh3DDQaLGhwRl4eNv7xpVBCV+JJ7AMStcDlLNBHnMviKXG3XGaLAMUIqI+zmy1y2WIrFYlBI8NA8NYBhBNuRqVAvHOZtjWXD0ooS/xBBKgBHNHY7nEE4RdjN3pxbh7lniIiKi7EStEtCqlvPeOoFIq5D6FcPahiJKDI0BqvylUZFDC2SQbUIDSZIbd3rU5I6JHKOgZlHY2DPz396dw0TPr8Oyqw0F9L38xQCEi6uPEb+eZCVFQKtsGA5HoQxG/0Yvf8NsjbxgYhibZQHpQRIOxzS51uccj2DsZC64eFNff7ZmaZpQbjEHbQyhQDFCIiPo4VwNm++WDJHlH48hkULyJEZNku2kPilathD7KEUSJnZkDYTBa5H6coK/iaWfcvehHCtbE2kAxQCEi6uM624TONQslfH0K4oaZ6EsGpZuWeABXg29XGmVFeScxRiNPzw2WhHZ6UEKVrfEXAxQioj7O2wwUITk2/NNkDT5kUFwBSmhLETa7JC8V9jtAie36NFlXg2zwA4aE9jIode1vexBuDFCIiPo4ucTjLYPi7KWojUAPSocBSphKPLXNZtjsEhQK+D1mPhjD2s6GaEgb0DaDYrXZUWFgiYeIiLqBMi9j7gW5xBOJHpQY7wGBKHeEelCbKO8kx2ihUfl320wJwrj7sk5KcF0hSmgiY1XRYIJdcizv9nXPoVBhgEJE1MeVOzMomd5KPDHhz6D40iQrNgsM9aj7c51sWtgReUfjLnzvSkO0xBhwfX8bTFZYbfZOV3SFEwMUIqI+zG6X5N4SbzdgucQTxmFedb6s4gnTZoGBNsgCribZrmRQzoawJ8T9+2swWkPa7+IvBihERH1Yg9EKMUPMWzAQiSZZXzIo8qA2sw2S1LVBaB05WtkAAMhLifH7a1NjfR9332K2wdbOQLeztY6gIRQ9IWqVUp7UW9dsDmm2xl8MUIiI+rA654CuaI0KURpVu8ckxoR/UJvoiRBzOtojNgu02SWYrKHbJ2jfmXoAwJj+CX5/bYqP4+7L64249NmvceeSbR6PN5qscgblvLQ4v9/fF3q3DQNDtWtyIBigEBH1YaJsk9RBICB6UBrCuGGgb4PaXDNBQtWHYrdL2HfWEaCM7pfo99e7mmQ7Du6WbStBdZMZm4qqPJp+j1Y4sjfp8Tq51BZs7hsGhrIh118MUIiIIqC83oi3vj2BBmNkN2kTZZuOVsvoozVQhnHDQEmSfApQVEoFojSO21io+lBO1TSjwWiFTq3EkAz/Mxiir6fBZPU6Ot5is+P9bSUAAEkCjpQb5OeOljsClGGZ8X6/t68S3FbynK1rf1frSGCAQkQUAS9/fQx//N8h/P7T/RE9j3ofMigqpUIu84SjD6XRZJV7MToKUADXSp6mEM1CEdmT/Gy930uMAUAfpYZG5Yjuqr2UyNYdqkRlg6sEdLCsQf7zYRGgZIQuQJEzKM2WbjNFFmCAQkQUESfONQIAvtxbik3HqiJ2HiLgSOoggwK4bmLh6EMR2ROdWum1L0aI0YZ2muy+M3UAgNH9/O8/AQCFQiEvNfbWh7LMmT0Rg+cOlrplUJwlnqFhyKCU1rfI33v2oBAR9VFnnCszAOCJz/fDZI3MzrHyxNYOMihAeGeh+DJFVgj1hoE/nBH9J4EFKEDHfSgl1c3YePQcFArgvquGAAAOlrkClCPODMrwkAYojvM77MzcxOvUiI/q/HsfagxQiIjCzGqzy+Pl46PUOFHVhDc3nojIudTJGZSOb0jhnIXiyz48QlwINwy02yXsPytW8CQG/DqiD6W9cfcie3LZkDQUjswAABwuM8Bqs6Oq0YTqJjMUCmBIeugzKIecgVF3KO8ADFCIiMKu3GCEzS5Bq1LiqR+PBAC88nURTtc0h/1c6uRdgzsu8SSHsQel3oclxkJMEDcMfGfLSVy8aJ0clJyoakKT2YZojQrnpcUG/Loig3KuVYBittrx0Y7TAIC5k3KRlxKLGK0KJqsdJ6ub5OxJXnIMorUdl7q6QnyfRR9MdyjvAF0MUP785z9DoVDg/vvvlx8zGo2YP38+UlJSEBcXh9mzZ6OiosLj60pKSjBz5kzExMQgPT0dDz/8MKzW0G+XTUTUHYjyTnZiFK4/vx8uGpQMk9WOt74NfxZFZEQ6CwYSY8Pfg+JLBiWYGwau2FeG0nojnlt9BACw72wdAGBkth7qABpkhUy944Zf4cyaCd8VVaG6yYwMvQ5ThqdDpVTIpZyDZQ1ygDI0hA2yQNvvs7dNI8Mt4O/49u3b8cYbb2DMmDEejz/wwAP48ssv8dFHH2HDhg0oLS3FDTfcID9vs9kwc+ZMmM1mbN68Gf/617+wdOlSPPHEE4FfBRFRDyIClP5JMVAoFLj+/H4AHEtaw63OxybZ5DAOaxNZHb0PAUp8lJiC2vXSU22T4zU2Hj2HPafrXP0nAQxocydG1Je1ClBOVjcBACbkJckBUH62HoCjUVY0yIay/wRwbRgoRHoXYyGgAKWxsRFz587Fm2++iaSkJPnx+vp6vP3223jxxRdx1VVXYcKECViyZAk2b96M77//HgCwZs0aHDx4EO+++y7GjRuHGTNm4Omnn8bixYthNodvSiERUaSclQMUx40gPd5xA6s0BL5fS6DqfMygyBNRw5hB6azsBAAZIjvRYOzkyM65l6/+/nWRW/9J1wKUTOdMkXKD5zmKgCXLbeZIfpbjvQ6WGeQlxqFcwQO0DQRDsedPIAIKUObPn4+ZM2di6tSpHo/v3LkTFovF4/Hhw4cjNzcXW7ZsAQBs2bIFo0ePRkZGhnxMYWEhDAYDDhw40O77mUwmGAwGjw8iop7qTK0jUyIHKHrHzd99Fka4iJtyYicZFNdKlNCfoz8lHrEDc3l91wIUSZI8ApSvDlVgz+k6AF1bwQO4Sjytz7G0nU0AXRmUehyrCP0MFKBtcNpjm2Tff/997Nq1C4sWLWrzXHl5ObRaLRITEz0ez8jIQHl5uXyMe3AinhfPtWfRokVISEiQP3Jycvw9bSKibsO9xAO4MijVTSZYwzRKHnCsJmowOno3OsugpMqzPMKQQZGXGas7OdJ7+cRfTWYbLDbHcLipIxz3JItNQqxWhYGpXdsDRwRR5xpNHlsFiHN2DwiGZcRDqXBsLthktkGrUmJAauANur5oHQj2yBLP6dOn8Zvf/AbvvfceoqLClwJasGAB6uvr5Y/Tp0+H7b2JiILtTJ1nBiUlVguVUgFJ8m3X22ARmQqgbR9Ca3IGpckU0p2D3c+rs9ksgFuJx9C1AEXMd9Gplfht4VD58ZH9EqASc/4DlBKrhUbl+Ps955YlK2sngxKtVWGgW0AyKC02oAm2/ojTqeVrVChc39NI8+uqd+7cicrKSowfPx5qtRpqtRobNmzAyy+/DLVajYyMDJjNZtTV1Xl8XUVFBTIzMwEAmZmZbVb1iM/FMa3pdDro9XqPDyKinshqs6PMud+JyKAolQqkOgOAyiD0UvhKNKPG69SdrlJJds5BsdgkNIRo3xvBnx4U0b9R1Wju0rA7Ud5JjtVieKZenklyfm5iwK8pKJUKOUsmsiY2u4QKeVmvZ8YiP9tVUgp1gyzgmHYrsihpcTpo1d1jAolfZzFlyhTs27cPe/bskT8mTpyIuXPnyn/WaDRYt26d/DVHjhxBSUkJCgoKAAAFBQXYt28fKisr5WPWrl0LvV6P/Pz8IF0WEVH3VNFggtUuQaNSID1eJz8eiUZZsYJHLCHuSJRGJQ9FC3WZp96PVTxJMRr5htqV751YnSR6cf58wxj8bsZw3Hv5eQG/prusVr0ylQ2OWThqpUIe5CbkZ7l+CQ91g6wgMmjdpf8EADov8LmJj4/HqFGjPB6LjY1FSkqK/Pi8efPw4IMPIjk5GXq9Hr/61a9QUFCAiy66CAAwbdo05Ofn47bbbsNzzz2H8vJyPPbYY5g/fz50Ol2b9yQi6k3OOJcSZydGQ+lWOhDBSjgbZcWy2s6WGAspcVo0mqyobjR5lCGCTQROvjTJKhQKZOqjUFLTjHKDETnJMQG+p+N7kewM1pJitbgnSMEJAGSIAMVZiip1ZtEy9FFtSkiiURYIfYOsIMpp3WVIGxCCSbJ//etfcc0112D27NmYPHkyMjMz8cknn8jPq1QqLF++HCqVCgUFBbj11ltx++23Y+HChcE+FSKibudMqyXGgmslT/hLPL4EAoCjlwIIbZ+M3e4qIfkySRZwNaF2pVG2dQYl2LLklTyOv/+yetewvtbcMyjDwpRBET8D2Qk9NIPSnvXr13t8HhUVhcWLF2Px4sVevyYvLw8rVqzo6lsTEfU4coCS6Pmbfpoo8YQxg+LrkDbBNQsldOfYYLRC9OD6Gji5yictnRzpnfheJIcoQJGXQzvLUKIPKaudgCAtXocHpg6FxWaX+5RCTazcGZzetRVLwdTlAIWIiHzXegaKkCEyKF1cjeIPX4e0CSKDEsoelLoWx2vHaFU+r15xzRkJPHASI/872zQxUJmtgqhS53+zvJRUfjN1SEjOw5sHfzQUFw5MxvRR7S9WiQQGKEREYSRnUJJblXgikEHxdUibIJYah3LcvT9D2gRXdiLwDEqNyCbFhqjE06oMJTIo3aWkkhKnw3Xj+kX6NDx0j7VERER9hGsGimfqXm6SDecqHnk5r68ZFMc5VoVwmmwgAUowhrX5W+7ylxh3X2kwwW6X5B6U7jJWvjtigEJEFCY2u+Q2A6X9JtmqRscNLBzkm7IPy4wB93H33SuDkuFlt2B/1IgVTSHKoKTH66BQAGabHTXNZpS2sw8PeWKAQkQUJhUGI6zO2ReipCOkxjluYFa7JJcbQk0sM/a1xJMahibZuuZAMiiOm3xFgwm2AIM7VwYlND0oGpVS/v6V1DTLWShvPSjEAIWIKGxE/0l2YnSb2RcalVJeQRKuMk+9vyWeMGZQfG3cBRyrXlRKBWx2KeDyk+irCVWJB3CVc/aeroMkAVq1Um48prYYoBARhYm3FTxCWnx4Z6HU+tl3Icbd1zSbA85UdMYQQIlHpVQgzZmdCGRX4xazDSarYxO/UJV4AFcpandJHQBHwKJQdG2fn96MAQoRUZh4G9ImiBtYODIoJqsNzWbH3jW+ZitEhkeSXCWRYAukBwXo2rA2UVLTqBSI1ar8/npfiQzKrpJaj8+pfQxQiIjCxJVBaX/4VnoYMyj1zl4PhQLQR/kWDKhVSrlHozpES40D6UEBujasrdatvBPKjIYIouRSHxtkO8QAhYgoTMSNqZ+XDdlc4+5Dn0FxH3OvVPp+UxbTZEO11FjOoPjZCyKyT+UBZJ/8LXUFSgyUE9gg2zEGKEREYXLOGXhkeknth3NH49oAm0JDPU020BJPlzIoYoqsj8utA9X6751LjDvGAIWIKExEWUSshmktnCWeugBWywDuK3lCnEEJYw9KoMGav1pnULrTzsHdEQMUIqIwsNrscilBTGRtLawlHjHm3s9AQJx7qHpQ/F36LIibf0UAexnVhnjMvcAMin8YoBARhUFNsxmS5GhK9TYMzH0/HkkK7TRZ1+Z4fpZ4RAYlBAGK1WZHo8kKIJASj+NmX1Zv9Pt758qghLbEE6NVe1wXm2Q7xgCFiCgMRM9GcowWai+79Io5KGarXc4khIq8WsbvEo8zgxKCEo/BaJX/rPczQBHZJ5PVLl+brwIN1gIhMj0xWhX00dyvtyMMUIioV6tsMOK+Zbvww5m6iJ6HCFC89Z8AQJRGJf+GHeoyT6Cb46WGsElWnFO8Tt1m0m5nojQqeZBcuZ9lnnCt4gFcZR4OaescAxQi6tU+2nEGy38ow59XHo7oeYj9a7z1nwjh2tW4Ts4aBJhB6UKJp9FkxSe7zsBosXk8LjIZ/mZPBJGd8HeabK2fmyZ2hVhtlO1lqTm5MEAhol5N3Ky2n6yR+xsiocqHDArg3igb2pU84qbs77wRkaXoyhyUt78txoMf7sVzq454PL65qAoAMDQjLqDXzfJxJY/RYkOL2RUciU0Tw5FBEVOEc5LbH9ZHLgxQiKhXEzd6i03Cd84bYCSIG7rY0dYb90bZUBI9Lv5mUFKdAVaD0QqT1dbJ0e07VtkAAPhi71lYbXb58ZX7ywEA00dlBvS6GWIWSgclHptdwrWvbMK0lzbIQUo4SzxzLszFfVcOxj2Tzwv5e/V0DFCIqFdzv9GvP3IuYudRLQconWRQwlTiqZWXGft3U9ZHaaB29ofUBFjmEVmtqkYzvj9RAwAoqW7GwTIDVEoFfpQfWICSpe98WFtRZSOOVTbidE0Ltp2sgdHi2pMo1MuMAUeJ7LeFw5CbwgxKZxigEFGv5n6j33CkMuTLd71xNcl2nEEJ5o7GRosNr64vwolzjR6PS5Ik93v4O6hNqVTIZZ5AG2XdSzBf7D0LAFi5vwwAMGlgsvz6/vJljsye07XynzcdOyf34qiUCuijuKqmO2GAQkS9liRJHjf60nojjlU2dvAVoVMlpsh2cvMN5o7GK/aV4blVR3DHkm0eDakNJivMVkdpxd8ABehao6zNLnkMU1u5vxwmq00u78wIsLwDAOnysLaOApR6+c/fHqtyK+9ouKqmm2GAQkS9Vm2zBRabI2Ny8XkpAID1Ryojci6ixNNZBiWY4+5L6xyljtM1LXh1/XH58edXO5pT+ydFI07nf9bAtR+P/0FUdaMJVrsEpQLI0OvQYLTig+2nsed0HRQKoHBkFwIU5/fuXAffu72n6+Q/Hy5vwNEKRz9MYhj6T8g/DFCIqNcSN/mkGA2m5WcAAL45HP4+FEmS5CbZtM4CFH3wmmSr3Eowr68/juKqJqw/Uol3tpwCADxz/eiAsgau/Xj8z6CUOss7GfooXDMmGwDkJeATcpPk6w+EyD5VNZphcWu+FVrMNhxxBiRiSfLyHxylpWQGKN0OAxQi6rVEmSRDH4UrhqUDAHacCv9y42azDUaL44bZ6TJjZxag2Wzr8nmecwZFWpUSZpsdv/vvD3jk4x8AAHcU5GHy0LSAXlfMcqlq8j+IEg2smQlR+PFYR4AimlQDXb0jJMdo5Qbe9pZBHyith80uIS1eh+vOd7z3BmfjdCClLgotBihE1GuJXoe0eB0GpMZiQEpMRJYbi0xDlEaJGK2qw2NjdWrEOo+pDGDjO8/3ddykf3XVYGjVSmwtrkFlgwmD0mLxuxkjAn7drmRQRINsdkI0xvRPQJ7bapauBihKpUJext1eD88eZ3lnbP9ETB7iCM7MzkxLoI25FDoMUIio1xJlEjFbRGRRwr3cuMptiqwvJZVglXlEiWfCgCTce7lj7oZaqcBLN41DdCeBUkfEUulAlhmLJcaZzlHvIosypn8C+id1felthnMlT3u7Gu8942iQHZeTgAl5SdCpXbdA9qB0P1xTRUS91rkGUeJx3LQuG5KKpZtPYtep2o6+LOhEpiE1vuP+EyEtXofiqqYgBCiu4XD3XnEemkxWjMlJxJj+iV16XTlLEUAjr+hBEVNf75o8CPUtFtwwvn+XzklIi48CUN/u9040yI7LSUKURoULBybj22OObFpyGMbck38YoBBRryV+ixZ9HeelOUaon6ppgiRJYVtWKgcKPpYRXEuNAy/xWGyuXX1T43SI0qjw2DX5Ab+eu6wEx7j2sjr/z8+9BwVwDH5beN2ooJwX4ApGWwcoNU1mlNQ0AwBG908A4AhYRYDCDEr3wxIPEfVaconHecPvlxQNlVIBo8Ue8lHy7lxLjH27CbqWGgd+jqL8olIqkBjg5nveZCc6vp/VTeY2G/51pkzOoIRmszx5q4BWwd1e527Wg9Ji5R2jLx3sahIOx5h78g8DFCLqtUQGRfxWrVEp0c+5i+zJqqawnUeVj1NkBde4+8AzKCJrkxyrhVIZ3ExRQrRGbvYVs1baY7La5DIbANjdhrSJEk+weZsmu6ekDgAwzq28NTwzXv5eh+p8KHAMUIioV3JMkfVskgUgrxo55Uz3h4OYuNrZRoGCLyPbOyOCIl/f0x8KhQLZzkDP287BR8obMPXFDbj4z+tQUu34Xlc1mWCxOYa0pfnYj+Mvb02yIoMyNidRfkypVOC1Wydg0Q2jMapfQkjOhwLHAIWIeiVDi2ucu/vNUA5QqsOYQWnwbaNAIRg7Gvv7nv4SGYez7WRQ1hwoxw2vfofTNS2w2CR8W+RYNSVW8KTF66BRheb20973TpIkuUHWPUABgAl5SZhzYW5IzoW6hgEKEfVKFc4VJgnRGkRpXEtqB6TEAgBOVYczg+JaZuyLYJR4xHuGIoMCQC6VtS7x/HNTMf7fuzvRZLbJ81x2O8sroe4/AVzZp+pGE6zOGSena1pQ22yBRqXAiKz4kL03BRcDFCLqlcSgrvRWpYTcZJFBCWOAIveg+JdBMRitfjehCnLfS4gGkMklHreVPC1mGxatPARJAm4vyMNfbxoHANhVUus81hHMhLLfIyVWB6UCsEuu0trBMgMAYGhGPHTqwOe/UHgxQCGiXsnVIOt5MxyQ6signKx2LDUONZtdQk2zfwGKPlotDxE7F2CZRy7xhKjXQwQZpfWuDMqJqkZYbBKSYhxLhy8YkOx4/FwT6prNKDO4hrSFiqqdabKHyx0ByvBMfcjel4KPAQoR9UquBtn2MygNRqs8JySUapvNkCRAofB9QzqFQiGXKtqbiOqLKj8bc/0lSjzuPSjHzzn6esS8maRYLQY6A8Ldp+vkHpTsEJZ4AFdQKr53h8scGwSyvNOzMEAhol5JTDltvTtulEYl72R7MgyNsmK5b1KMFmo/GkO72igrMii+Zm385V7iEZmo45WNAFwBCgCcn5sIANh9qlYuB4UygwK0nSPDDErPxACFiHolbz0ogGslT0kYlhpXB9gL0tVGWREYpYUogyKCjBaLTc5EHT/nDFDSY+Xjzs9NAuDIoJQZQt+DArjvZWREk8kqLylnBqVnYYBCRL2SK4PiPUA5WRX6AKXKzymyQlemydrtkjxJNlQZlCiNSl7CLPpQRIlncLorgzLemUHZU1KHinrHtYQrg1JhMOFoRQMkybG02ddBedQ9MEAhol6pwiA2Cmx7M8yTlxqHvsRTHeDAtK7saFzfYoHV7ii7+Lq0ORBiuXBpnRE2u4QT59qWeIZlxCNGq0KDyQqzzQ6Fov2/k2ASQem5BiMOlzv6T4ZnMnvS0zBAIaJexzFF1nOjQHfhnCYb6DyStC5kUMR7JkRroFWH7p95sSdPaV0LSutaYLLaoVUp0T8pRj5GrVJiTH/XlNa0uNANaRMy3Pp3DjuXGI/IYv9JT8MAhYh6nQaTFUaLY0iX+5h7YUAYMyhVDeHvQTnXENryjiAaZUvrW1DkzJ4MTI2FqtXeP6IPBQjPnjfuK6AOMYPSY6kjfQJERMEmburxUWpEa9sO5sp1ZlCqGs1oNFkRpwvdP4XyFFk/MygZXSjxiL6XUC0xFrLdSjzyCh63BllhvFuAEur+E8D1vatqNKPZ7Bh0xxU8PQ8zKETU63S0ggcA9FEaJDszGqHOorg27Qssg1LTZJb3FPJVtRyghCmDUtfSZgaKO7HUGAjtmHshJVYLhcIxJK/BaIVaqWg3cKLujQEKEfU6Yh+ejpoxxcC2kiCNvD9+rhH1LZ6D30xWm7xXjb8ZlKQYLdTOUonIiPgqlDsZuxM9KGV1LfISY/cVPEJqnE7+foejxKNWKT2ag89Li+OI+x6IAQoR9TqdZVAAYIBYahyEAKWkuhlTX9yAq55fjx0nawAAFpsd9y3bjcoGE2K1KgxuJ7PQEaVSEXCjbNhKPM4MSrnBiGMVjl6P9jIoAFA4MgNKhWP34HDIcFtezvknPRMDFCLqdeQx9x1lUILYKHugtB6Sc3O6OW9+j/9sK8H97+/B2oMV0KmV+MftE5EQo/H7dX1tlP3npmIULFonT0yt8nNzwkA5VuQoYJeAWuewNjHavrXfXz0Cux+fhonO/XlCzT04Hc4VPD0SAxQi6nXKDd6XGAsigxKMXY3FfjQ6tRIWm4QFn+zD//aVQatS4o3bJuCSwakBvW6aD+PuS+ta8OdVh1FWb8SSTScBhC+DolQqPJpesxOiEOul4VihUAQUpAXKffUWV/D0TAxQiKjXEZvSddSQGcxhbWdqHQHKnRcPwANThwIA1EoFFs8djyuGpQf8umK5bEcByl/XHpWbaFfuL4PZancLUEKbQQE8v8fntdN/EimeJR5mUHoiLjMmol5HBCgdLWkVTZtlBiNMVluXmihFBqV/UjRuKxiAK4alQatWdvnGKA8c81LiOVLegP/uOgMAiNOpYTBasfHouYCn1wZC7GoMeO8/iYQ0Z3kvKUbTYSaNui9mUIioV7HbJVQYRAbFe4CSGqdFtEYFSXLM8eiKs84MSr8kx816bE5iUH5r7yyD8pfVh2GXgOkjM3HjxBwAwPvbT6PF4pj9EY4ARazkAbpXBmVYhqOsMyEvGQqFopOjqTtigEJEvUp1kxlWuwSFwjUuvj0KhQL9nQHF6S6OvD9T6/j6fokxnRzpH9eGgW0DqG3FNfjqUCVUSgUenj4MPx6XDQBYd7gCABClUSKmnSF1weZR4knrPrNGLhiQhP/eW4Dnfzom0qdCAWKAQkS9iijv+LLnizwLpQsBSoPRAoPRCsCVQQkW0egpNj509/K6YwCAmy7IwXlpcRjbPwG5yTGQHHsEIjVOF5bMgXuJx9+l1KGkUCgwIS8ZiTGh78Oh0PArQHnttdcwZswY6PV66PV6FBQUYOXKlfLzRqMR8+fPR0pKCuLi4jB79mxUVFR4vEZJSQlmzpyJmJgYpKen4+GHH4bVag3O1RBRn1dW7yi3+DJSPccZoJyuDTxAEf0nCdGaoI/MF+WTcw0mGJ1lG8CxGeLe03UAgNsL8gA4bsjXjs2Sj/F3MFygxLYBiTGaDjNWRP7yK0Dp378//vznP2Pnzp3YsWMHrrrqKlx33XU4cOAAAOCBBx7Al19+iY8++ggbNmxAaWkpbrjhBvnrbTYbZs6cCbPZjM2bN+Nf//oXli5diieeeCK4V0VEYSdJEv695SQOOXePjRTRf5LZwQwUQZR4ztS0BPx+ov+kf5CzJwCQHKtFvDPocS9D1TSZ0WCyQqFwbXwIAD8e20/+c1oYVvAAjsbYp2eNwt9uPp+9HhRUfgUo1157La6++moMGTIEQ4cOxZ/+9CfExcXh+++/R319Pd5++228+OKLuOqqqzBhwgQsWbIEmzdvxvfffw8AWLNmDQ4ePIh3330X48aNw4wZM/D0009j8eLFMJvNIblAIgqP74qq8fjnB3D7P7eh2Ry5rGhZfecNskIwMyjupY5gUSgUcobCfeLtKWewkqmPQpTG1WcyLDNebg4NR4OscNtFebh8aFrY3o/6hoB7UGw2G95//300NTWhoKAAO3fuhMViwdSpU+Vjhg8fjtzcXGzZsgUAsGXLFowePRoZGRnyMYWFhTAYDHIWpj0mkwkGg8Hjg4i6l6JKx6jzcw0mvP1tccTOQ/SgZPgSoCQ5A5Qu9KC0XsETbAPamdci/pyX0rYp9+eXDgAAXBCmia1EoeJ3gLJv3z7ExcVBp9Phnnvuwaeffor8/HyUl5dDq9UiMTHR4/iMjAyUl5cDAMrLyz2CE/G8eM6bRYsWISEhQf7Iycnx97SJKMRO17rKJG9sPCHvqBtu5T4sMRZykh1BRW2zBQ1GSydHt+9MCDMogCsIcZ94e7LK8ee85LarZm66IBd7nvgRbhjfr81zRD2J3wHKsGHDsGfPHmzduhX33nsv7rjjDhw8eDAU5yZbsGAB6uvr5Y/Tp0+H9P2IyH8iC6FQAI0mK/7+TVFEzkMe0qbvPGCIj9IgyTl+/XSAfSih7EEB3AIUtyyPWHWUl9r+subEGC37QajH8ztA0Wq1GDx4MCZMmIBFixZh7Nix+Nvf/obMzEyYzWbU1dV5HF9RUYHMzEwAQGZmZptVPeJzcUx7dDqdvHJIfBBR9yIyKD+/ZCAA4N3vT3V5voi/JEmSe1B8WcUDdL0PxdWDEtwZKEJ7I/lPihJPOxkUot6iy3NQ7HY7TCYTJkyYAI1Gg3Xr1snPHTlyBCUlJSgoKAAAFBQUYN++faisrJSPWbt2LfR6PfLz87t6KkQUQWJY2ZwLc3DZkFRYbBJeWHMkrOdgMFrlKaq+rOIButaHYrTYcM455TVUPSgig3K2tgUWm2PPnRJnuae9HhSi3sKvRfsLFizAjBkzkJubi4aGBixbtgzr16/H6tWrkZCQgHnz5uHBBx9EcnIy9Ho9fvWrX6GgoAAXXXQRAGDatGnIz8/Hbbfdhueeew7l5eV47LHHMH/+fOh0XD9P1FPVN1vQIIaVJcbgoWnD8O2xKqzYV44XbpSgUoan3CDKO4kxGkT7OEW1v7MP5Uyt/yWeUmf2JFqjkktFwZYRHwWdWgmT1Y7SuhYkx2pR3eRY9cgAhXozvwKUyspK3H777SgrK0NCQgLGjBmD1atX40c/+hEA4K9//SuUSiVmz54Nk8mEwsJCvPrqq/LXq1QqLF++HPfeey8KCgoQGxuLO+64AwsXLgzuVRFRWInySGqcDtFaFUb3S4BWpYTZ5ripijJKqMlD2nzMngBdy6C4bxIYqp4PpVKB3OQYHKtsxKnqZjkQTInVIj4qNEERUXfgV4Dy9ttvd/h8VFQUFi9ejMWLF3s9Ji8vDytWrPDnbYmomxM3d7EqRqV0zO8oqmzEyeqmsAUo8pA2H/tPANe4+0B6UEK9xFjIS4l1BihNcoCSy+wJ9XLci4eIukyUR0Q2AgAGtDNgLNT8GdImyE2yNS2QxEY2PgrlkDZ3A9yWGp+qaXI+xgZZ6t0YoBBRl4nsg/tSW3EDPVnV1O7XhII8pM2PEk92YhQUCqDFYkNVo38TrcOXQXEFe6ecM1Byw5SVIooUBihE1GWuEo/rppmXGoEAxY8hbYJOrZJ7Vjor82w6VoUb39iC74qqAIR+SJsglhqX1DS5MiheZqAQ9RYMUIioy063U+IZKDIo1d07gwL41ihbVNmAe97diW3FNbhv2S5UNhhDPqRNcJ8mW+wM+HI5A4V6OQYoRNQlkiTJM1A8Sjyprt4Om92/3o5AuTIo/gUMnS01rm+x4K53dqLRuYNwbbMFj3z8g/x+oRrSJvRLjIZaqYDJakeFwTF3ZQCbZKmXY4BCRF1S1WiG0WKHQgFku5U6shKiPZYah1qL2Ya6Zsd+Ov6s4gFc/Rwl7TT02uwSfv2f3SiuakK/xGgs+8VF0KqVWH/kHGx2CRqVAunxoZ3jpFYpPfpc4nRqJMdqQ/qeRJHGAIWIukT0bWTpo6BVu/5JEUuNgfCUeUQ2I1qjgj7KrwkKrhJPOz0ob357AhuOnkOURok3bpuAgvNS8EjhMPn57MRoKMMwiC7PbdVOXkoM99qhXo8BChF1iejb6N/OqhJ5qXEYGmXL3ZYY+3vz7mg/nvVHHFtzPFw4HKP6JQBw7DdUMCgFQOj7T4S8ZPcl3Ow/od6PAQoRdcmZDhpF5aXGYZiFUm5wTpH1s7wDuAbMldYZYXXudyOU1jkCn9HO4ARwTHd96eZxmD2+P+ZfOTjQU/aL+1h7DmmjvsC/PCgRdTtWmx1KhSIsZYb2iAZZ9xU8woAwLjWWdzH2cwUP4NjvRvTLlNUb5YyK3S7J4/NbzzrJ0EfhhRvHdvGsfeeeNWGDLPUFzKAQ9WAmqw2zX9+Cq15YD6NzF99wO13jXGLcboknfEuNK+r9H3MvKJUKOQPkXuY512iCxSZBqQAyQtwI2xmPDAqXGFMfwACFqAf7x4YT2Hu6Dierm1FU2RiRc2hviqwQ6qXGRosN35+oxj82Hsf6o+cA+DekzV3/5LazUMQo+0x9FNSqyP5zmZMcA7UzSzYojQEK9X4s8RD1UKdrmvH3b4rkz09WN8lNnOFis0vyEuL2MiitlxoHa9PAospGvLf1FP678wwMzs3zhGGZ+oBeM0dkUGpcS6LDNcreF1EaFZ77yRg0mqx+D6Ij6okYoBD1QJIk4Q9fHIDJ6mroPBXGTfmECoMRFptjFkh7vR/B3tVYkiTc95/d+N8PZfJjGXodzs9JwpicBFw0KAXjc5MCeu32VvKI4Cs7xKPsfXXD+P6RPgWisGGAQtQDrT1Yga8PV0KjUmDGqCx8sbcUp8I4Ul4Q5ZDsxGiovDTpDkiJdQQoVU24bEhal96vtN4oBydTR2TgtoI8XDY4NSgNwu2Nu+9uAQpRX8IAhaiHMVvteOrLgwCAuy4bhGGZ8fhib2lYlvK2dtqHvWgGuO3E21ViNdCgtFi8dcfELr+eO7HU+LTbuPuzYdoMkIjaYpMsUQ+z53Qdzta1IDlWi/uuGtzhmPZQO+nDxnXBXGosNsobGIJBZeL7eK7BhBazY0XU2Tqx1w4DFKJwY4BC1MNsP1kDAJg0MBkxWrW8lLfcYAz7UmOxcmhwepzXYwY6A5TiIJSgRBkrLwQBSkK0BvE6R1JZzHZhiYcochigEPUwO0/VAgAmDkgGACTGaOS9Z0pqwptFOVbZAAAY0kGAIuZ3nK5p7vJS4+Iqx/UNTA3+oDKFQuFaalzbjEaTFfUtjs0HsxO5aoYo3BigEPUgdrvkClDyHKtVFAqFnFEIx8RWwWy1yyuHOsqgZCdEQ6tWwmKT5MxEoEKZQQE8lxqL7Ik+So34KE1I3o+IvGOAQtSDHD/XiPoWC6I1KuRnu+Z9iCxFODMop6qbYLVLiNWqOhyOplQqMMhZ5unKMDm7XcKpGpFBCVGA4jas7SzLO0QRxQCFqAfZftKRPRmbkwCN22TTPHmlTPgyKO79J53tHjwkI97jawJRWt8Cs9UOjUoR8LTYzuS4jbs/68MKJSIKHQYoRD3IjlOOBtkLnP0ngih5hHNY2zE5QInv9NjBaXEeXxMIcW05yTEhGzufm+Iazc8GWaLIYoBC1IOI/pMJeZ7TUvOcpYlwBii+rOARhmTEeXxNIEK5xFhwH9bGGShEkcUAhaiHqGww4lR1MxQKYHyrAEXMGjlb1wKLzd7elwedyIZ0tIJHEEFMUWUjJCmwlTyiAThUDbIA0N8ZoDSYrDhUZgDADApRpDBAIeohdjr7T4ZlxEPfalVJerwOURolbHZJ7p0IJZtdwolzvmdQBqTEQqVUoNFkRYXBFNB7ikm0oVhiLERrVUiN0wEAjlY4ro8BClFkMEAh6iF2yPNP2m6Gp1AokOec5hqORtkztc0wWe3QqpU+bQCoVSvlRl4xO8VfJ0O8xFgQI+8FNskSRQYDFKIeQg5Q8pLbfT6cS41FL8mg1FivmwS2JhplA+lDsdkleZR/qJYYC7luAZdGpUCaM6NCROHFAIWoB2gx23DgbD2Atg2ygrzUuCr0AYrcf5LR+QoeQTTKBrKSp6y+BWZbaJcYC6JRFgAyE6KCslMyEfmPAQqRj7YV1+DvXx+DvYvj2gOxq6QWVruEDL3Oa8lBlD5KakJf4pFX8KR13n8iuDfKdqbFbMNzqw5j3xlHUCaCrlAuMRbcSzxcwUMUOQxQiHxgttrxy/d24vk1R7Hx2Lmwv//yH8oAAFcMTfc6FM01rC2cGRTfA5Qh6b4Pa/vnd8V4df1x3PPuThgtNrn/JJRLjAX3DAobZIkihwEKkQ/WHCxHVaMZQNdmeQTCbLVj5X5HgPLjcdlejxsgZ1CaQ5rlkSQJx/2YgSIMSnOcX02TGdWN3lfySJKE/+48A8CxbPrd70+FZYmx4N70ywwKUeQwQCHywXvfl8h/Lg7jhnwAsKnoHOqaLUiL1+GiQSlej8tKiIJaqYDZake5wRiy8yk3GNFoskKlVMhBkS9itGq5PNVRkLerpA4n3L7Hf/+mCD84+29CucRYyEqIkht/GaAQRQ4DFKJOHD/XiC0nquXPgx2gbDpWhV++txN/WX0Yqw+Uo6JVcPHFnlIAwMzRWR2umFGrXEt+g7WrcXtD30RwkZcSA63av39C5D6Uc94DlP/ucmRPrhuXjSHpcahrtmBbcY3zPUOfQVGrlHIg1T8p9AEREbVPHekTIOru/rPVkT3JTohCab0x6AHKS18dlZcQA4BSATz145G4rWAAWsw2rDlYAaDj8o4wNCMOxVVNOFTegIsHp3bpvO59dyd2ldTi43su9ih7HKvwfYJsa0PS47D+yDn5NVozWmz4cq8jILvpghw0m2z4xTs75OdDvcRYeGxmPrYcr8ZFg9pf0k1EoccMClEHjBYbPnb+Rv/bwmEAgLJ6I1rMtqC9x+laR1Pr1BEZGJYRD7sEPPnlQXxXVIV1hyvQbLYhJzka5+ckdvpa+VkJAICDpYYunZMkSfj6cCUqDCY89eVB+XG7XcKq/eUAgKF+LDEWOlvJs+ZgBRqMVvRLjMZFA1MwZUQ6LnAOpgvHEmPhR/kZeOLa/JCvGCIi7/h/H1EHVuwrQ12zBf0So3HduH5IiHaMmA/WtFaz1Y7KBkfD6LOzR2PV/ZfhhvH9YLNLmL9sF5Z8dxIAcO2YbK+rd9zlZ+sBAAfLuhag1LdYYLI6yjtfHarAWmcW5+1Nxdh2sgYxWhV+OiHH79cd3MlKno+dzbGzx/eDUqmAQqHA72aMgFqpwPk5SQwYiPoQlniIOvCes7wz58IcqJQKDEyNxZ7TdSiuasKILH2XX7+svgWSBERplEiO1UKhUOCZ60fjeGUj9p6pl3cvvnZs5+UdABiRJQKABpido+gD0Xq/nCe/OIDUOC3+svoIAODxa/KRm+J/f4bIoJQbjDAYLR57CpXXG7HJuYR79oT+8uMT8pKw7qHLkRit9fv9iKjn4q8jRF4cLjdg56laqJUK3DjRkS0Y5OyBCFYfyhnnxn79EqPlDEmURoU3bpuItHjHiPUh6XEYnulbOaVfYjT0UWpYbFLAe94AkFcB5aXEoF9iNM7WteCmf3wPs82OKcPTcfMF/mdPACAhWoN053W1zqIs/6EUdgm4cEBym2bYvJRYJMR4bpBIRL0bAxQiL5Y5syfTRmYgXe/ofRgY5ABF7Dzcr9VqkcyEKLx5+0SMzUnEQ9OG+lTeARybBsplni70oVTIAUosnrg2H4CjHJUcq8Wi2aN9Pp/2DHMGW4fLPAOo3SV1AIApI9IDfm0i6j0YoBC1o8lkxSe7zgIAbrkwT358QLAzKHWuDEpr43IS8fn8SzB9VJZfrykaZQ+VBZ5BqXQGKJl6HablZ2D6yEyolAr8+YbRSI/vWqNqfpbok6n3eFz0zYgAi4j6NvagELXjy72laDRZMSAlBhef5xqOFqoMirf9dQLhapSt7+RI70SJJ0MfBYVCgVduOV8eFhes83MPoBpNVrnxOBi9PUTU8zGDQtSOZdsc5Z1bJuV67GYrApSaJjPqmy1dfp+zdY4lxsGcWCpnKEoNkKTARt6LJtkMZ2lLo1IGJThxP79DZQZ5JP+RcgMkCcjQ65AaF5z3IaKejQEKUSs/nKnDD2fqoVUp8ZNWS2ljdWq5ybM4CEuNz4oSTxAzKIPT46BRKWAwWuXX91eFWwYl2AamxkKnVqLZbMOpGkeAJvpl8pk9ISInBihErYjm2BmjM5Ec23Zpq6vM07VNA212CWV1jkAgmBkUrVopzxsJtFG2Qu5BCX6AolYp5VVJ4vzYf0JErTFAIXLTbLbic+feN3Mn5bV7jNiVt/hc1zIolQ1GWO0S1EpF0DMVrkZU/wMUq82Ocw2ixBOackvrPpmDzn4U0eBLRMQAhchNUWUjWiw2pMZp5RHrrYkdfIurm7v0XmIGSlZiVIebAAaiK0uNq5vMsEuASqlASoj6QVx9KA2w2uw4zAwKEbXCAIXIzUln0DEwNdbrrI9glXjO1npfYtxVXcmglNc7yjtpcbqgB07CCLdG3pPVTTBZ7YjRqpCXzN2DiciBAQqRm1PO5cO5yd53zXUv8QS6SgZwa5BNDP5NWQQoZ2pbUN/i32ojuUE2hBvzDXeeX7nBiG+PVTkey4z3WDFFRH0bAxQiN2JVyYAO9pnJSY6BUgE0mW0412jyelxn5DH3QVzBIyTEaOTMzGE/syhygBKkZcXtidOp5e/xf527RbO8Q0TuGKAQuTnlXDrc0UZ4OrVKDiq60igrMij9Q1DiAVxllEN+ByiOoCszhBkUwBWQ7D8rlhizQZaIXBigELk5VS0yKN5LPAAwMNWxK2/RucD7UM7WOoe0hSCDAgBDMhzneNzPIKo8hDNQ3LWeecIMChG5Y4BC5NRstqLSubw2r4MMCgCM7e/4bX/riZqA3kuSJLcelNAEKOeliQDFvyAqlEPa3LkHJEoFMCzDtx2biahvYIBC5FTi7D9JiNYgMabtgDZ3lw5OBQB8V1Qlj2v3R02TGUaLHYBjmXEonOds5g08QAntyHn3ks6gtDhEa1UhfT8i6ln8ClAWLVqECy64APHx8UhPT8esWbNw5MgRj2OMRiPmz5+PlJQUxMXFYfbs2aioqPA4pqSkBDNnzkRMTAzS09Px8MMPw2q1dv1qiLrgZJUjQOksewIA5+cmIUarQnWTGYfK/V/KKxpk0+N10KlDc2M+L92RQakwmGAwtl3JI0kSvthbiml/3YCnvjwgPy73oIQ4g5Kh1yEpRgOAGwQSUVt+BSgbNmzA/Pnz8f3332Pt2rWwWCyYNm0amppcNe4HHngAX375JT766CNs2LABpaWluOGGG+TnbTYbZs6cCbPZjM2bN+Nf//oXli5diieeeCJ4V0UUgJIax89xXif9J4BjnPxFgxy7HG9yLpP1h9wgG6L+EwDQR2nkfYNOtOpDKa5qwu3/3IZf/2c3jlY04l+bT6K2yQyjxSYvS04PcYCiUCgwMtuRReEePETUml8ByqpVq3DnnXdi5MiRGDt2LJYuXYqSkhLs3LkTAFBfX4+3334bL774Iq666ipMmDABS5YswebNm/H9998DANasWYODBw/i3Xffxbhx4zBjxgw8/fTTWLx4Mcxmc/CvkMhHYkibr8PCRJlnU1EAAYq8xDi0g8nkPpRKV5mnqLIBM/62Ed8eq4JWrURyrBZ2CVh/tFIu70RrVNBHqUN6bgDw4LSh+MmE/rjlwtyQvxcR9Sxd6kGpr3fso5GcnAwA2LlzJywWC6ZOnSofM3z4cOTm5mLLli0AgC1btmD06NHIyMiQjyksLITBYMCBAwfQHpPJBIPB4PFBvcuKfWWY+9b3mPMPx8fd7+xAWX1gO/EGSiwx9qXEAwCThzoClG3FNTBabH69V6gbZIXz0tv2oXyxtwxGix2j+umx9oHJcnDw1cFKubyTodd5naQbTONzk/D8T8ciwVnqISISAg5Q7HY77r//flxyySUYNWoUAKC8vBxarRaJiYkex2ZkZKC8vFw+xj04Ec+L59qzaNEiJCQkyB85OTmBnjZ1U3/63yF8V1SNLSccH2sOVuDtb4vDeg7yEuPUzks8gCM7kamPgslqx46TtX69VyiHtLlrbyXPzlOOlUdzLsxFXkospuY7/v/bcPQcTjsbhUO9goeIqDMBByjz58/H/v378f777wfzfNq1YMEC1NfXyx+nT58O+XtS+JTWteBsXQtUSgVeumkc7rtyMABg5f7yLo2S94fZakepM6vha4lHoVDg0iGOLMq3x855Pc5ul7DpWBXmL9uFa1/ZhGtf2YTNxx1loVANaRNcAYojO2S12bG7pA4AMDHPkfkc0y8BafE6NJqs+HyvYydnBihEFGkBFZnvu+8+LF++HBs3bkT//v3lxzMzM2E2m1FXV+eRRamoqEBmZqZ8zLZt2zxeT6zyEce0ptPpoNOFdskjRc6OU47sQ36WHrPO74cWsw1vbyrG2boW7D9rwOj+oZ8weqa2GXbJ0XuR5seI98uGpOLjnWfw7bEqLGj1nCRJeHdrCf65qRjFVW2HpamVCgzPCu3sD7GS52RVEyw2Ow6XNaDZbIM+So0hzueUSgWmjkjHf7adxsajjkAr1FNkiYg641cGRZIk3Hffffj000/x9ddfY+DAgR7PT5gwARqNBuvWrZMfO3LkCEpKSlBQUAAAKCgowL59+1BZWSkfs3btWuj1euTn53flWqiH2nnSUXKYkJcEAIjWqnDl8DQAwMr9ZUF7n+0na3Dj61twoLS+zXOivJOXEuNX78UlzkbZg2UGVLXal2f/WQMe/2w/iquaEKdT4/aCPLx1+0QsufMCLLnzAqy6fzKyEkKbQcnSRyFGq4LVLqGkphk7Trm+1+4b800d4Vl2TQ/hPjxERL7wK0CZP38+3n33XSxbtgzx8fEoLy9HeXk5WlocqfGEhATMmzcPDz74IL755hvs3LkTP/vZz1BQUICLLroIADBt2jTk5+fjtttuw969e7F69Wo89thjmD9/PrMkfdR2Z//GxAFJ8mPTR2UBAFYFsczz7MrD2HayBn9ZfaTNc/42yAqpcTp5hsd3rVbzbHMGXhcOTMbW30/BwutGYWp+Bq4cno4rh6djsDODEUpKpULeffl4ZaOcrZo4INnjuEsGpyJK4/rngBkUIoo0vwKU1157DfX19bjiiiuQlZUlf3zwwQfyMX/9619xzTXXYPbs2Zg8eTIyMzPxySefyM+rVCosX74cKpUKBQUFuPXWW3H77bdj4cKFwbsq6jEaTVYcdg46Ez0RAHDV8HRo1UqcqGrC0YrA97sRiqua5JuzezOoIC8x9mEGSmuXDXFNlXW3u8TxfpcPTUOsLvRLdr0RfShF5xqxo1W2SojSqHDp4DT5c/agEFGk+fWvpi+/yUZFRWHx4sVYvHix12Py8vKwYsUKf96aeqndJbWwS46BZe6/tcfp1Jg8JBVfHarEyv1lGJbZtV6N/+48I/9ZkoD/bCvBI9OHy4+JMff+ZlAA4OLzUvCPjSew5US1x+OiGfX83ET/TziIRICy8eg5VBhMUCsVGNu/7Tn9KD8dXx1y9oMxQCGiCONePBRRYnnuxFa/0QNA4UhH0/Sq/e0vP/eV3S7hk12OAGXmGEfp6MMdp2G22uVjTooST7L/GZQLBiRDrVTgdE2LnJmpMBhxtq4FSgXaDQbCSQQo3zs3NhzVL6HdfW+uGp4BrUqJOJ3ar0ZhIqJQYIBCEbXTWXaZ0KonAgB+lJ8BtVKBw+UN7a6C8dWWE9UorTciPkqNZ2ePQVq8DlWNZqw96MgW2OwSztQ4lxgHkEGJ1akxNidRfi/AVd4ZlqmPaHkHcA1rE9oLBgEgLV6HZXdNwjvzLkSUhhv3EVFkMUChiLHa7NhV4j2DkhijRcF5jv1uupJF+dhZ3vnx2GzE6dS4+QLHoL/3tp4C4JjDYrbZoVEpkB3gXJIC5748W447ApRd3aS8AwADUmLhtmDHoxm5tYkDkjE+1/vzREThwgCFIuZwuWMmR3yUGkMz2u8xmTI8HQDwfav+Dl81GC3yUuXZExwze26+MBdKBbD5eDWe/OIArn/1OwBATlIMVMrAxrtf7AykNh+vgiRJcgalO9zsozQq5LgNn5uQ1zZbRUTU3TBAoYgRK0rG5yZ5DQzEzXR3SS3sdv+XG6/cVw6jxY5BabE431mG6ZcYjSuGOQKfpZtPoqrRjPR4HR740dAArsJhfF4StColKgwmHK1oxA9nHLNWukMGBXD1oQxIiWF/CRH1CAxQKCSsNnunq77kmRxeeiIAYHhWPKI0ShiMVpzwsw/FbpewdPNJAMDs8f09BrDdd9VgpMRqUTAoBa/OHY/vfncVrh2b7dfru4vSqDA+LxEA8M9NxTBZ7UiM0WCQj/v6hNqQDEeA0nr+CRFRdxXZ7j3qlXaeqsVPX9+Mh6YNw3znvjqt1Tdb5H6NCR30RGhUSozpl4htJ2uwq6TWr+FmX/5QioNlBsTp1Jjj3LFXGJ+bhJ2P/8jn1/JFwaBUfH+iBp/sdvS8nJ+TGJYdgX3xi0sHwWKV8IvLBnZ+MBFRN8AMCgXd2oMVsEuO8kl7ZRmbXcKv39+N6iYz+iVGd9qncb4zMyHmivjCZLXJE2PvuXwQkmO1Pn9toERDr8XmuObzu0H/iZAWr8MT1+YH3ARMRBRuDFAo6I44J8OeazDJq3TcPbf6MDYcPYcojRL/uH1Cp0taz89x3Oh3t/Na3izbWoIztS1Ij9fh55eGJ2swLifRY1x8d2iQJSLqqRigUNC5j6Zf2Wp58Od7zuKNDScAAH/5yViMzO58p+LxzkbTIxUNaDBaOj2+wWjBK18XAQDunzoUMdrwVDK1aiUucPZ4KBTA2JzQ78JMRNRbMUChoDIYLThb1yJ/7r7Z39m6Fvzff38AANx7xXk+N6Wm66PQLzEakgR5dUxH3thwAjVNZgxKjcWNE/sHcBWBE2WeoenxiI/ShPW9iYh6EzbJ9jFf7i3FwuUH5THvWrUST183Ut49uKuOVTQAAFLjtGg223C2rgX7ztZjTP9EvLjmKIwWOy4YkITfThvm1+uOz0vC2boW7DpVi0sGp3o97vM9Z/Hqekf25JHpw6BWhTcGv3FiDr4/URP2wIiIqLdhBqWPeWfLSZxrMKG+xYL6FgvONZjw/JqjPm0E6YvD5Y4AZWR2Aq50zhpZub8ch8sN8uqWR2fm+z0QTZR5dp+u83rMyn1lePDDvbBLwJwLc+W9fMIpNU6Hd35+Ia4ZE/iSZSIiYoDSp5isNux1lkjenTcJK39zGaI1KhRVNmL7Sd8bUDty1BmgDMuMx/RRrs3+nl15GJIEzBydhXHOgWn+ECtidpfUthtMfXWwAr/6z27Y7BJ+MqE//jRrVLdZ4ktERP5jgNKH7D9rgNlqR0qsFpcMTsGILD2uG+f4TV/sS9NVIoMyLCMeVw5Ph1atRHFVE745cg4qpQK/LfSvtCPkZ+mhVStR22zByepmj+eazVbc/8EeWO0SrhuXjWdnj4EywJH1RETUPTBA6UPk0fJ5SXJ2Ye6kPACOkfA1TeYuvb4kSTha4cqgxOnUmDwkTX5+zoU5GBjgZFWtWonR/RyrYnad8sz27C6pQ6PJiqyEKLzw07EB76dDRETdBwOUPkSMlr/AbXLr6P4JGNM/AWabHR/vPN2l1z/XYEJtswVKBeSJrzOcZZ4YrQq/njKkS68v+lBaz1bZ6txI8KJBKWFviiUiotDgv+Z9hCRJcuah9W62tzjHwC/bWhLQhnzCEWf2ZEBKrDx87dqx2bjrsoH4283nIz0+KuDXBoAJzj17xIh8YWuxIzN04UDuM0NE1FswQOkjiquaUN1khlatxKh+eo/nrh2bjXidGierm7G51c3fH0fcGmQFrVqJR2fm40f5GQG/rnDJ4FRoVAqcqGrC8XOOYXAmq01e2cMAhYio92CA0kfscK7SGdc/ETq152j5WJ0a14/vBwD4YEfgZR4RoAzNiO/kyMDER2lw0SDHILR1hyoAOAa3ma12pMZpu83OwURE1HUMUPqIHaccZRBvOwfPHO0Y1CYaaQMhSjzDM0MToACQMzFfHawE4Oo/uXBgMpcVExH1IgxQ+gjRIDsxr/0AZaRzhUxZvRHVjSa/X99ud63gGRrCAOWq4Y7hbztO1aC2ySz3n0wamBKy9yQiovBjgNIHVDeacOJcEwBXo2lrcTq1vAT4QKnB7/coqWmG0WKHVq3EgJTQlVr6J8VgRJYedglYe6gCO52BF/tPiIh6FwYofYC4iQ9Jj0NijNbrcSOzHc2z+0s735CvNVHeGZIeF/I5JD8a4ciivPpNEZrNNuij1BgWor4XIiKKDAYofYAIUCZ66T8RRjnLPAfO+p9B2eYstYQjUJjq7EMRE2UvHJjMybFERL0MdzMOkfJ6I15cewRNZhsAQKVQ4OYLc3Dxed534g2VnV7mn7Q2KtsZoPiZQVm2tQRvbyoGAFw2NPTXNyo7AenxOlQ2OHplWN4hIup9GKCEyN/WHcOHO854PLbzVC02PnJlWEexW212uWRzvnMSqzeixHOyuhkGowX6KE2nr//xzjN49LN9AIC7LhuIWeP6de2EfaBUKjBlRAb+s60EABtkiYh6I5Z4QsBosWH53lIAwL1XnIenfjwSiTEanK1rwcaj58J6LkXnGmG02B1NsJ00rybFatEvMRoAcNCHRtnP95zFIx/vhSQBd148AL+/ekTYlvr+KN/RhxKrVcmBFRER9R4MUEJgzcEKNJis6JcYjYenDcMdFw/A7PH9AQRv12Bf/XDGkT0Z1U/vU5+GuNl3tpJn5b4yPPjhXtglYM6FufjDtflhnUNy+dB0/PKK87Bo9hjuv0NE1AvxX/YQ+Hino7Qze3w/OSi4ZZJjv5uvD1eitK4laO9ltNg63D9nnzNAETsBd8bVKOu9D+WrgxX41X92w2aX8JMJ/fGnWaPCPiRNpVTgkenD8eOx2WF9XyIiCg8GKEFWXm/EpmOOMs7sCf3lx89Li0PBoBTYJeD97V3bNVg4Vd2E8U+vxW3/3Aqz1d7uMfucgcbo/ok+vabYp8fbUuONR8/hl+/tgtUu4cdjs/Hs7DFcQUNEREHHACXIPt19FnYJuGBAEvJa9XzMvciRRXl/WwkstvYDCn98sussms02fFdUjT98caDN8xabHQfLHKWaMT5mUEY6V/IUVTaixbkCyd3jn++H2WbHjFGZePHGsWFt+CUior6DAUoQSZKEj3c6siM/ccueCNPyM5Eap0VlgwnrDlV2+f1WHyiX//yfbSV493vP/pajFQ0wW+2Ij1IjLyXGp9dMj9chNU4HuwQcLvfsQ6lrNuOUc/bIsz9h7wcREYUO7zBBtOd0HY6fa0KURomrnZvvudOqlfjpxBwAwDLnEtlAFVc14XB5A9RKBe65/DwAwJNfHJAHpgGu/pMx/RN87hFRKBRuZR7PAEVkY3KTY3xagkxERBQoBihB9MmuswCA6SMzEe/lBn7zBY4A5buiKtQ3WwJ+r5X7ywAABeel4P+mD8M1Y7JgtUu4b9kuuTTzg+g/6Zfo12vLA9taNcqKpcf5WVzWS0REocUAxUfl9Uaca/C+y6/FZsf/9jmChuvHty3vCHkpsRicHgebXcK3RYHPRFm131HemTEqCwqFAs/9ZAz6J0WjssEkDzBzz6D4w1ujrMigjGCAQkREIcYAxQfVjSYUvrQRl/z5a7y49iiMlrbNo98VVaGmyYyUWC0uOa/jyaZXDE0DAKw/EliAcqa2GT+cqYdSAUwb6diXJkarxr1XOEo9b2w8jgajRe4h8XWJsTDGueLnUFkDGk1W+XE5g8LBaEREFGIMUHywYl8Z6lssMNvseHndMRS+tBGbjlV5HPOFc3Ls1aOzOm0evWKYYwrqhqPnOpxh4o3InlwwIBmpcTr58Z9M6I9MfRQqDCb86X+HYLFJSIrRoH9StF+vn50YjdzkGNjsErY7e1pMVhuKKhsBMEAhIqLQY4DiA1fwkYlMfRROVTfj9n9uxd7TdQAcw9LWHKgAAPx4XOeDwy4YmIQYrQrnGkxy2cQfrvJOpsfjOrUK/+/yQQBcs1ZG9fO9Qdbdxc4s0JYT1QCAYxWNsNolJERrkJ0Q5ffrERER+YMBSifO1rVg+8laKBTA49fk46uHLsfUERmwS46ZIDa7hG8OV6LRZEV2QhQm5CZ1+po6tUre1XiDn3vzVBqM2Fni2J14+qi2K4VuviAXqXFa+XN/+0+EAmeAsvm4I1MkAqn8LH3Yp8YSEVHfwwClE2LTvwsGJCMrIRpxOjWeuWEU4qPU+OFMPZZtK5EzLNeOzfZ5quoVwxx9KN8c9m8eyhd7SyFJjp2JM9vJZERrVbjrskHy5/6u4BEKBjkClAOlBtQ1m3GojP0nREQUPgxQOiGCD/c9X9Ljo/DbacMAAH9ZdRhfO4OMa/3YF0YEKLtKan1ebmyzS1i6+SQA4KcTcrweN/eiPKTG6aBTKzE+L9Hnc3KXro/CeWmxkCRga3GN3CDLFTxERBQODFA6cPxcIw6UGqBWKtoMXrv1ojyMzNbDYLTCZLVjUFqsvBOwL/onxWBwehzsEnxebrzmQDnO1LYgKUaDG8b383pcnE6NL+67BMt/dSnS4wPvFxFlqM1FVR4lHiIiolBjgNKBL53Zk0uHpCI5VuvxnEqpwB9njYJox/jx2Gy/ezP8XW789qZiAI7gKEqj6vDY7MRoDMmI9+t8WhN9KF/+UIYGoxUalQKD0+O69JpERES+YIDihSRJrt6SMe2Xbs7PTcIDU4ciP0uPORfm+v0eYrnx+iNtlxtbbXZUNboGw+09XYcdp2qhUSlw20V5fr9XIC5y9qHUNJkBAEPS46FV80eGiIhCj3cbL74+XIkT55qgUyvlYWjt+fWUIVjxm8uQofe/lHLBwCTEalWoajRhz5k6j+cWLj+IiX/8CvOX7UKFwShnT64dm430AN4rEMmxWgzPdGVh2CBLREThwgClHSfONeL+D/YAAOZcmOt1X52u0qlVuHK4I4siZpsAQJPJig93OOaY/O+HMkx5YYM8Rn/epQNDci7eFLhNxWX/CRERhQsDlFYajBbc9c4ONBitmJCXhN9fPSKk7zfDOctk1f5ySJKjzLP2YAWMFjv6JUZjXE4iGk1W2OwSLhqUjJHZgc01CZRolAW4goeIiMJHHekT6E7sdgkPfLAHx881IVMfhdduHR/ynosrhqVBp1aipKYZB8sMGJmdIPe+zB7fD7+ZOhT/2VaC1QfKsWBGaIOl9lw4MBlRGiUkiSUeIiIKHwYobv627hi+OlQJrVqJf9w+oUtLdH0Vq1Pj8qFpWHOwAqv2lyM7IRobndNlfzwuGyqlArdelIdbw9QY21pCtAbv/eIiSJJjzD0REVE4sMTj5kf5GeiXGI0/3zBa3tE3HGaMduyps3J/OVbuL4fVLmFElh6D07u2TDhYJuQlYeKA5EifBhER9SHMoLgZ1S8Bax+cjBhteL8tVw3PgEalQFFlI17fcBwAcO3YtvvsEBER9RXMoLQS7uAEcJRRLhnsaEYtqWkG4H32ChERUV/AAKWbmDEqU/7z+NxE5CTHRPBsiIiIIsvvAGXjxo249tprkZ3tGO3+2WefeTwvSRKeeOIJZGVlITo6GlOnTsWxY8c8jqmpqcHcuXOh1+uRmJiIefPmobGxsUsX0tP9KD8TKudOyD/2Y9NBIiKi3sjvAKWpqQljx47F4sWL233+ueeew8svv4zXX38dW7duRWxsLAoLC2E0GuVj5s6diwMHDmDt2rVYvnw5Nm7ciLvvvjvwq+gFkmO1+PklAzA2JxGzzve+ESAREVFfoJDEdLBAvlihwKeffopZs2YBcGRPsrOz8dBDD+G3v/0tAKC+vh4ZGRlYunQpbr75Zhw6dAj5+fnYvn07Jk6cCABYtWoVrr76apw5cwbZ2Z1nDwwGAxISElBfXw+9nrM5iIiIegJ/7t9B7UEpLi5GeXk5pk6dKj+WkJCASZMmYcuWLQCALVu2IDExUQ5OAGDq1KlQKpXYunVru69rMplgMBg8PoiIiKj3CmqAUl7u2E8mI8Nzc72MjAz5ufLycqSnp3s8r1arkZycLB/T2qJFi5CQkCB/5OTkBPO0iYiIqJvpEat4FixYgPr6evnj9OnTkT4lIiIiCqGgBiiZmY6lshUVFR6PV1RUyM9lZmaisrLS43mr1Yqamhr5mNZ0Oh30er3HBxEREfVeQQ1QBg4ciMzMTKxbt05+zGAwYOvWrSgoKAAAFBQUoK6uDjt37pSP+frrr2G32zFp0qRgng4RERH1UH6PTW1sbERRUZH8eXFxMfbs2YPk5GTk5ubi/vvvxx//+EcMGTIEAwcOxOOPP47s7Gx5pc+IESMwffp03HXXXXj99ddhsVhw33334eabb/ZpBQ8RERH1fn4HKDt27MCVV14pf/7ggw8CAO644w4sXboUjzzyCJqamnD33Xejrq4Ol156KVatWoWoKNfOwO+99x7uu+8+TJkyBUqlErNnz8bLL78chMshIiKi3qBLc1AihXNQiIiIep6IzUEhIiIiCgYGKERERNTtMEAhIiKibocBChEREXU7DFCIiIio22GAQkRERN2O33NQugOxMpq7GhMREfUc4r7ty4STHhmgNDQ0AAB3NSYiIuqBGhoakJCQ0OExPXJQm91uR2lpKeLj46FQKIL62gaDATk5OTh9+nSfGwLHa+e189r7Dl47rz0S1y5JEhoaGpCdnQ2lsuMukx6ZQVEqlejfv39I36Mv75rMa+e19zW8dl57XxPJa+8scyKwSZaIiIi6HQYoRERE1O0wQGlFp9PhD3/4A3Q6XaRPJex47bz2vobXzmvva3rStffIJlkiIiLq3ZhBISIiom6HAQoRERF1OwxQiIiIqNthgEJERETdTo8IUDZu3Ihrr70W2dnZUCgU+Oyzzzyer6iowJ133ons7GzExMRg+vTpOHbsWLuvJUkSZsyY0e7rrFu3DhdffDHi4+ORmZmJ//u//4PVau30/NavX4/x48dDp9Nh8ODBWLp0qV/nH+prv+KKK6BQKDw+7rnnHo9jSkpKMHPmTMTExCA9PR0PP/ywT9f+0UcfYfjw4YiKisLo0aOxYsUKv88v0te/d+9ezJkzBzk5OYiOjsaIESPwt7/9zafz6+z677zzzjbvPX369G5z7e6qq6vRv39/KBQK1NXVdfnaP/nkE0ybNg0pKSlQKBTYs2ePT9cdzmtv/bxCocD777/f5Wvvys99OK596dKl7V67QqFAZWVll669J/zM99Z/6wFgy5YtuOqqqxAbGwu9Xo/JkyejpaVFfr6mpgZz586FXq9HYmIi5s2bh8bGxi5fe0NDA+6//37k5eUhOjoaF198MbZv3+7z9benRwQoTU1NGDt2LBYvXtzmOUmSMGvWLJw4cQKff/45du/ejby8PEydOhVNTU1tjn/ppZfaHY+/d+9eXH311Zg+fTp2796NDz74AF988QV+97vfdXhuxcXFmDlzJq688krs2bMH999/P37xi19g9erVPp1/uK79rrvuQllZmfzx3HPPyc/ZbDbMnDkTZrMZmzdvxr/+9S8sXboUTzzxRIfntnnzZsyZMwfz5s3D7t27MWvWLMyaNQv79+/3+/wief07d+5Eeno63n33XRw4cACPPvooFixYgL///e9dun5h+vTpHu/9n//8p9tcu7t58+ZhzJgxPp2bL9fe1NSESy+9FM8++6xPr+kunNe+ZMkSj2NmzZrVpWvv6s99OK79pptu8niurKwMhYWFuPzyy5Genh7wtQvd+We+N/9bv2XLFkyfPh3Tpk3Dtm3bsH37dtx3330eI+Xnzp2LAwcOYO3atVi+fDk2btyIu+++u8vX/otf/AJr167Fv//9b+zbtw/Tpk3D1KlTcfbsWb+/F+4X3qMAkD799FP58yNHjkgApP3798uP2Ww2KS0tTXrzzTc9vnb37t1Sv379pLKysjavs2DBAmnixIkex3/xxRdSVFSUZDAYvJ7PI488Io0cOdLjsZtuukkqLCz06fz9Eei1X3755dJvfvMbr6+7YsUKSalUSuXl5fJjr732mqTX6yWTyeT162688UZp5syZHo9NmjRJ+n//7//5dX6+CtX1t+eXv/yldOWVV3Z4TGfXL0mSdMcdd0jXXXedX+/dnlBf+6uvvipdfvnl0rp16yQAUm1tbYfH+3LtQnFxsQRA2r17d6fn0Z5QXnsg/z+G8+c+XD/zlZWVkkajkd55550Oj+sNP/O9+d/6SZMmSY899pjX1z148KAEQNq+fbv82MqVKyWFQiGdPXvW69d1du3Nzc2SSqWSli9f7nHM+PHjpUcffbTji+1Aj8igdMRkMgEAoqKi5MeUSiV0Oh02bdokP9bc3IxbbrkFixcvRmZmZruv4/4aABAdHQ2j0YidO3d6ff8tW7Zg6tSpHo8VFhZiy5YtAV2PP3y9dgB47733kJqailGjRmHBggVobm6Wn9uyZQtGjx6NjIwM+bHCwkIYDAYcOHDA6/t3du3+nF8ggnX97amvr0dycnKHx/j6d79+/Xqkp6dj2LBhuPfee1FdXd3ptXUmmNd+8OBBLFy4EO+8806nm3cJveHnXpg/fz5SU1Nx4YUX4p///Gen28BH8uc+VD/z77zzDmJiYvCTn/ykw/fvDT/zvfXf+srKSmzduhXp6em4+OKLkZGRgcsvv9zje7NlyxYkJiZi4sSJ8mNTp06FUqnE1q1bvb5/Z9dutVphs9na/b525We+xwcow4cPR25uLhYsWIDa2lqYzWY8++yzOHPmDMrKyuTjHnjgAVx88cW47rrr2n2dwsJCbN68Gf/5z39gs9lw9uxZLFy4EAA8Xqe18vJyjxs7AGRkZMBgMHjU/ULB12u/5ZZb8O677+Kbb77BggUL8O9//xu33nprp9cgnvPG29eJr/H1/CJ9/a1t3rwZH3zwQadpz86uH3Ckut955x2sW7cOzz77LDZs2IAZM2bAZrMFeNUOwbp2k8mEOXPm4C9/+Qtyc3N9fn9frj1Ugvn3vnDhQnz44YdYu3YtZs+ejV/+8pd45ZVXOnz/SP7ch+pn/u2338Ytt9yC6OjoDt+/N/zM99Z/60+cOAEAePLJJ3HXXXdh1apVGD9+PKZMmSL3qpSXl7cp4anVaiQnJwf0b7249vj4eBQUFODpp59GaWkpbDYb3n33XWzZsqVrP/MB514iBO2kzXbs2CGNHTtWAiCpVCqpsLBQmjFjhjR9+nRJkiTp888/lwYPHiw1NDR0+DovvPCCpNfrJZVKJcXExEiLFi2SAEjvv/++JEmSFBsbK3+IlOaQIUOkZ555xuN1/ve//0kApObmZp/OP5TX3h6Rxi8qKpIkSZLuuusuadq0aR7HNDU1SQCkFStWSKdOnfK49j/96U+SJEmSRqORli1b5vF1ixcvltLT07t0fuG+fnf79u2TUlNTpaefflp+rCvX39rx48clANJXX33lyyXLQnXtDzzwgHTTTTfJz3/zzTceJZ5gXHuwSzySFPy/d+Hxxx+X+vfvL0lS1649WD/34bj2zZs3SwCkHTt2yI/15p95Seqd/9Z/9913EgBpwYIFHl83evRo6Xe/+50kSZL0pz/9SRo6dGib90tLS5NeffXVLl17UVGRNHnyZPn8LrjgAmnu3LnS8OHDA/oeSJIkqQMPbbqPCRMmYM+ePaivr4fZbEZaWhomTZokp7G+/vprHD9+HImJiR5fN3v2bFx22WVYv349AODBBx/EAw88gLKyMiQlJeHkyZNYsGABBg0aBAAeqxDENtWZmZmoqKjweN2Kigro9fpOfxsJhs6uvT2TJk0CABQVFeG8885DZmYmtm3b5nGMuKbMzExkZ2d7XLsofXi7dvcSWiDn549gXL9w8OBBTJkyBXfffTcee+wx+fGuXH9rgwYNQmpqKoqKijBlyhS/rrW1YFz7119/jX379uHjjz8GALm8kZqaikcffRSPP/540K49mIL59976mKeffhomk6nb/twH+9rfeustjBs3DhMmTJAf680/80Dv/Lc+KysLAJCfn+/xdSNGjEBJSYl8Da1XaVmtVtTU1Mh/h4Fe+3nnnYcNGzagqakJBoMBWVlZuOmmm+TvaUACDm0iBD5EpUePHpWUSqW0evVqSZIkqaysTNq3b5/HBwDpb3/7m3TixAmvr/P4449LOTk5ktVq9XrMI488Io0aNcrjsTlz5oSlcao9ra+9PZs2bZIASHv37pUkydUkW1FRIR/zxhtvSHq9XjIajV5f58Ybb5SuueYaj8cKCgrabZT05/y8CdX1S5Ik7d+/X0pPT5cefvhhn88nkOs/ffq0pFAopM8//9zn95Gk0F17UVGRx/8X//znPyUA0ubNmz1+Hlrz59pDkUFpLdC/99b++Mc/SklJSR2+Vzh/7kN97Q0NDVJcXJz0yiuv+HQ+veFnvj294d96u90uZWdnt2mSHTdunJxVEU2y7tmy1atX+9Qk68+1S5Ik1dTUSAkJCdIbb7zR4XV0pEcEKA0NDdLu3bul3bt3SwCkF198Udq9e7d06tQpSZIk6cMPP5S++eYb6fjx49Jnn30m5eXlSTfccEOHr9neD8Bzzz0n/fDDD9L+/fulhQsXShqNptMfkhMnTkgxMTHSww8/LB06dEhavHixpFKppFWrVvl8/qG89qKiImnhwoXSjh07pOLiYunzzz+XBg0aJE2ePFk+xmq1SqNGjZKmTZsm7dmzR1q1apWUlpbWJlXY2nfffSep1Wrp+eeflw4dOiT94Q9/kDQajbRv3z75mED+bsJ9/fv27ZPS0tKkW2+9VSorK5M/Kisru3T9DQ0N0m9/+1tpy5YtUnFxsfTVV19J48ePl4YMGdJh4BfOa2+tdYkn0GuXJEmqrq6Wdu/eLaeC33//fWn37t1SWVlZt7j2L774QnrzzTelffv2SceOHZNeffVVKSYmRnriiSe6fO1d+bkP59/7W2+9JUVFRXX69+3rtfeUn/ne+G+9JEnSX//6V0mv10sfffSRdOzYMemxxx6ToqKiPMpb06dPl84//3xp69at0qZNm6QhQ4ZIc+bM6fK1r1q1Slq5cqV04sQJac2aNdLYsWOlSZMmSWazudNr96ZHBCjiH83WH3fccYckSZL0t7/9Terfv7+k0Wik3Nxc6bHHHutweawktR+gXHnllVJCQoIUFRUlTZo0SVqxYoXP5zdu3DhJq9VKgwYNkpYsWeLX+Yfy2ktKSqTJkydLycnJkk6nkwYPHiw9/PDDUn19vcf7nDx5UpoxY4YUHR0tpaamSg899JBksVg6Pb8PP/xQGjp0qKTVaqWRI0dK//vf/zyeD+TvJtzX/4c//KHd98jLy+vS9Tc3N0vTpk2T0tLSJI1GI+Xl5Ul33XWXx3LuSF+7t/f05YbV2d/9kiVL2j3/P/zhD93i2leuXCmNGzdOiouLk2JjY6WxY8dKr7/+umSz2bp87V35uQ/n33tBQYF0yy23+HRevlx7T/mZ743/1guLFi2S+vfvL8XExEgFBQXSt99+6/F8dXW1NGfOHCkuLk7S6/XSz372M4/+zECv/YMPPpAGDRokabVaKTMzU5o/f75UV1fX6et2RCFJnaypIyIiIgqzHr/MmIiIiHofBihERETU7TBAISIiom6HAQoRERF1OwxQiIiIqNthgEJERETdDgMUIiIi6nYYoBAREVG3wwCFiHoNhUKBzz77LNKnQURBwACFiLrszjvvhEKhwD333NPmufnz50OhUODOO+8M2vs9+eSTGDduXNBej4i6HwYoRBQUOTk5eP/999HS0iI/ZjQasWzZMuTm5kbwzIioJ2KAQkRBMX78eOTk5OCTTz6RH/vkk0+Qm5uL888/X37MZDLh17/+NdLT0xEVFYVLL70U27dvl59fv349FAoF1q1bh4kTJyImJgYXX3wxjhw5AgBYunQpnnrqKezduxcKhQIKhQJLly6Vv76qqgrXX389YmJiMGTIEHzxxRehv3giCjoGKEQUND//+c+xZMkS+fN//vOf+NnPfuZxzCOPPIL//ve/+Ne//oVdu3Zh8ODBKCwsRE1Njcdxjz76KF544QXs2LEDarUaP//5zwEAN910Ex566CGMHDkSZWVlKCsrw0033SR/3VNPPYUbb7wRP/zwA66++mrMnTu3zWsTUffHAIWIgubWW2/Fpk2bcOrUKZw6dQrfffcdbr31Vvn5pqYmvPbaa/jLX/6CGTNmID8/H2+++Saio6Px9ttve7zWn/70J1x++eXIz8/H7373O2zevBlGoxHR0dGIi4uDWq1GZmYmMjMzER0dLX/dnXfeiTlz5mDw4MF45pln0NjYiG3btoXte0BEwaGO9AkQUe+RlpaGmTNnYunSpZAkCTNnzkRqaqr8/PHjx2GxWHDJJZfIj2k0Glx44YU4dOiQx2uNGTNG/nNWVhYAoLKystN+Fvevi42NhV6vR2VlZZeui4jCjwEKEQXVz3/+c9x3330AgMWLFwf8OhqNRv6zQqEAANjtdr++TnytL19HRN0LSzxEFFTTp0+H2WyGxWJBYWGhx3PnnXcetFotvvvuO/kxi8WC7du3Iz8/3+f30Gq1sNlsQTtnIup+mEEhoqBSqVRyuUalUnk8Fxsbi3vvvRcPP/wwkpOTkZubi+eeew7Nzc2YN2+ez+8xYMAAFBcXY8+ePejfvz/i4+Oh0+mCeh1EFFkMUIgo6PR6vdfn/vznP8Nut+O2225DQ0MDJk6ciNWrVyMpKcnn1589ezY++eQTXHnllairq8OSJUuCOgiOiCJPIUmSFOmTICIiInLHHhQiIiLqdhigEBERUbfDAIWIiIi6HQYoRERE1O0wQCEiIqJuhwEKERERdTsMUIiIiKjbYYBCRERE3Q4DFCIiIup2GKAQERFRt8MAhYiIiLqd/w8bwH73ohxTvQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ap1['ds']=ap1.index\n",
        "ap1.rename(columns={'Passengers': 'y'}, inplace=True)\n",
        "ap1['unique_id']=1"
      ],
      "metadata": {
        "id": "64JhuqEMasS0"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train=ap1[:-12]\n",
        "test=ap1[-12:]\n",
        "train"
      ],
      "metadata": {
        "id": "bkU0YWwia4FY",
        "outputId": "1ce8e1ed-c998-49cd-a15f-70e41e89a7bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           y       ds  unique_id\n",
              "Month                           \n",
              "1949-01  112  1949-01          1\n",
              "1949-02  118  1949-02          1\n",
              "1949-03  132  1949-03          1\n",
              "1949-04  129  1949-04          1\n",
              "1949-05  121  1949-05          1\n",
              "...      ...      ...        ...\n",
              "1959-08  559  1959-08          1\n",
              "1959-09  463  1959-09          1\n",
              "1959-10  407  1959-10          1\n",
              "1959-11  362  1959-11          1\n",
              "1959-12  405  1959-12          1\n",
              "\n",
              "[132 rows x 3 columns]"
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              "      <th>y</th>\n",
              "      <th>ds</th>\n",
              "      <th>unique_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1949-01</th>\n",
              "      <td>112</td>\n",
              "      <td>1949-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-02</th>\n",
              "      <td>118</td>\n",
              "      <td>1949-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-03</th>\n",
              "      <td>132</td>\n",
              "      <td>1949-03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-04</th>\n",
              "      <td>129</td>\n",
              "      <td>1949-04</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1949-05</th>\n",
              "      <td>121</td>\n",
              "      <td>1949-05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-08</th>\n",
              "      <td>559</td>\n",
              "      <td>1959-08</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-09</th>\n",
              "      <td>463</td>\n",
              "      <td>1959-09</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-10</th>\n",
              "      <td>407</td>\n",
              "      <td>1959-10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-11</th>\n",
              "      <td>362</td>\n",
              "      <td>1959-11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959-12</th>\n",
              "      <td>405</td>\n",
              "      <td>1959-12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>132 rows × 3 columns</p>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 132,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 132,\n        \"samples\": [\n          \"1953-09\",\n          \"1955-12\",\n          \"1950-08\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106,\n        \"min\": 104,\n        \"max\": 559,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          355,\n          115,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 132,\n        \"samples\": [\n          \"1953-09\",\n          \"1955-12\",\n          \"1950-08\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install  StatsForecast"
      ],
      "metadata": {
        "id": "KUMWFoWrbEJX",
        "outputId": "20c10cf1-71a4-45fd-8817-4f246fd98fd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting StatsForecast\n",
            "  Downloading statsforecast-1.7.4-py3-none-any.whl (121 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.8/121.8 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: cloudpickle in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (2.2.1)\n",
            "Collecting coreforecast>=0.0.7 (from StatsForecast)\n",
            "  Downloading coreforecast-0.0.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (193 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.5/193.5 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numba>=0.55.0 in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (0.58.1)\n",
            "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (1.25.2)\n",
            "Requirement already satisfied: pandas>=1.3.5 in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (2.0.3)\n",
            "Requirement already satisfied: scipy>=1.7.3 in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (0.14.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (4.66.2)\n",
            "Collecting fugue>=0.8.1 (from StatsForecast)\n",
            "  Downloading fugue-0.9.0-py3-none-any.whl (278 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m278.2/278.2 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting utilsforecast>=0.0.24 (from StatsForecast)\n",
            "  Downloading utilsforecast-0.1.7-py3-none-any.whl (40 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.3/40.3 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: threadpoolctl in /usr/local/lib/python3.10/dist-packages (from StatsForecast) (3.5.0)\n",
            "Collecting triad>=0.9.6 (from fugue>=0.8.1->StatsForecast)\n",
            "  Downloading triad-0.9.6-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting adagio>=0.2.4 (from fugue>=0.8.1->StatsForecast)\n",
            "  Downloading adagio-0.2.4-py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.55.0->StatsForecast) (0.41.1)\n",
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            "Requirement already satisfied: fsspec>=2022.5.0 in /usr/local/lib/python3.10/dist-packages (from triad>=0.9.6->fugue>=0.8.1->StatsForecast) (2023.6.0)\n",
            "Collecting fs (from triad>=0.9.6->fugue>=0.8.1->StatsForecast)\n",
            "  Downloading fs-2.4.16-py2.py3-none-any.whl (135 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.3/135.3 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: appdirs~=1.4.3 in /usr/local/lib/python3.10/dist-packages (from fs->triad>=0.9.6->fugue>=0.8.1->StatsForecast) (1.4.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from fs->triad>=0.9.6->fugue>=0.8.1->StatsForecast) (67.7.2)\n",
            "Installing collected packages: fs, coreforecast, utilsforecast, triad, adagio, fugue, StatsForecast\n",
            "Successfully installed StatsForecast-1.7.4 adagio-0.2.4 coreforecast-0.0.8 fs-2.4.16 fugue-0.9.0 triad-0.9.6 utilsforecast-0.1.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsforecast import StatsForecast\n",
        "from statsforecast.models import AutoARIMA,AutoETS,HoltWinters,AutoCES,AutoTheta\n",
        "\n",
        "sf = StatsForecast(\n",
        "    models = [AutoARIMA(season_length = 12),AutoETS(season_length = 12),HoltWinters(season_length = 12),AutoCES(season_length = 12),AutoTheta(season_length = 12)],\n",
        "    freq = 'M'\n",
        ")\n",
        "\n",
        "sf.fit(train)\n",
        "pred=sf.predict(h=12)"
      ],
      "metadata": {
        "id": "a72EnMtkbJqO",
        "outputId": "c71a6797-43ff-430e-8c9b-a846960f0d5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsforecast/core.py:417: FutureWarning: In a future version the predictions will have the id as a column. You can set the `NIXTLA_ID_AS_COL` environment variable to adopt the new behavior and to suppress this warning.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sf.plot(test, pred)"
      ],
      "metadata": {
        "id": "_YH6YTwwgfLD",
        "outputId": "0b226e28-b0c2-4a21-c673-8f6796ddfc6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsforecast/core.py:1329: FutureWarning: Passing the ids as the index is deprecated. Please provide them as a column instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 2400x350 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.reset_index(inplace=True)\n",
        "test"
      ],
      "metadata": {
        "id": "jYOGrZt_il0b",
        "outputId": "91c1b154-0871-491f-9be2-6ca89a2dbc77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Month    y       ds  unique_id\n",
              "0   1960-01  417  1960-01          1\n",
              "1   1960-02  391  1960-02          1\n",
              "2   1960-03  419  1960-03          1\n",
              "3   1960-04  461  1960-04          1\n",
              "4   1960-05  472  1960-05          1\n",
              "5   1960-06  535  1960-06          1\n",
              "6   1960-07  622  1960-07          1\n",
              "7   1960-08  606  1960-08          1\n",
              "8   1960-09  508  1960-09          1\n",
              "9   1960-10  461  1960-10          1\n",
              "10  1960-11  390  1960-11          1\n",
              "11  1960-12  432  1960-12          1"
            ],
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1960-08</td>\n",
              "      <td>606</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1960-09</td>\n",
              "      <td>508</td>\n",
              "      <td>1960-09</td>\n",
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              "    <tr>\n",
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              "      <td>1960-10</td>\n",
              "      <td>461</td>\n",
              "      <td>1960-10</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1960-11</td>\n",
              "      <td>390</td>\n",
              "      <td>1960-11</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1960-12</td>\n",
              "      <td>432</td>\n",
              "      <td>1960-12</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"1960-11\",\n          \"1960-10\",\n          \"1960-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 390,\n        \"max\": 622,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          535,\n          417,\n          390\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"1960-11\",\n          \"1960-10\",\n          \"1960-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred.reset_index(inplace=True)\n",
        "pred"
      ],
      "metadata": {
        "id": "lMLw8Rjqit8x",
        "outputId": "be5bb4d8-17e3-4d24-e6f8-0277dbed9f98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    unique_id         ds   AutoARIMA     AutoETS  HoltWinters         CES  \\\n",
              "0           1 1959-12-31  424.160156  406.651276   409.680145  410.827393   \n",
              "1           1 1960-01-31  407.081696  401.732910   390.044067  391.996124   \n",
              "2           1 1960-02-29  470.860535  456.289642   448.929810  456.414459   \n",
              "3           1 1960-03-31  460.913605  440.870514   435.445007  447.666779   \n",
              "4           1 1960-04-30  484.900879  440.333923   454.699951  472.150970   \n",
              "5           1 1960-05-31  536.903931  496.866058   507.904968  549.434143   \n",
              "6           1 1960-06-30  612.903198  545.839111   577.622375  621.770508   \n",
              "7           1 1960-07-31  623.903381  544.672485   581.996765  630.332764   \n",
              "8           1 1960-08-31  527.903320  477.034485   479.946136  513.530518   \n",
              "9           1 1960-09-30  471.903320  412.423096   421.522797  454.807617   \n",
              "10          1 1960-10-31  426.903320  357.949158   373.590912  398.501953   \n",
              "11          1 1960-11-30  469.903320  402.032745   410.594971  436.452240   \n",
              "\n",
              "     AutoTheta  \n",
              "0   412.711884  \n",
              "1   404.640472  \n",
              "2   466.810883  \n",
              "3   449.600372  \n",
              "4   454.047760  \n",
              "5   517.857727  \n",
              "6   572.380127  \n",
              "7   571.371643  \n",
              "8   502.108032  \n",
              "9   438.527100  \n",
              "10  382.673859  \n",
              "11  432.116119  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>unique_id</th>\n",
              "      <th>ds</th>\n",
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              "      <th>AutoTheta</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1959-12-31</td>\n",
              "      <td>424.160156</td>\n",
              "      <td>406.651276</td>\n",
              "      <td>409.680145</td>\n",
              "      <td>410.827393</td>\n",
              "      <td>412.711884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-01-31</td>\n",
              "      <td>407.081696</td>\n",
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              "      <td>404.640472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-02-29</td>\n",
              "      <td>470.860535</td>\n",
              "      <td>456.289642</td>\n",
              "      <td>448.929810</td>\n",
              "      <td>456.414459</td>\n",
              "      <td>466.810883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-03-31</td>\n",
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              "      <td>447.666779</td>\n",
              "      <td>449.600372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-04-30</td>\n",
              "      <td>484.900879</td>\n",
              "      <td>440.333923</td>\n",
              "      <td>454.699951</td>\n",
              "      <td>472.150970</td>\n",
              "      <td>454.047760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-05-31</td>\n",
              "      <td>536.903931</td>\n",
              "      <td>496.866058</td>\n",
              "      <td>507.904968</td>\n",
              "      <td>549.434143</td>\n",
              "      <td>517.857727</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-06-30</td>\n",
              "      <td>612.903198</td>\n",
              "      <td>545.839111</td>\n",
              "      <td>577.622375</td>\n",
              "      <td>621.770508</td>\n",
              "      <td>572.380127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-07-31</td>\n",
              "      <td>623.903381</td>\n",
              "      <td>544.672485</td>\n",
              "      <td>581.996765</td>\n",
              "      <td>630.332764</td>\n",
              "      <td>571.371643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-08-31</td>\n",
              "      <td>527.903320</td>\n",
              "      <td>477.034485</td>\n",
              "      <td>479.946136</td>\n",
              "      <td>513.530518</td>\n",
              "      <td>502.108032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-09-30</td>\n",
              "      <td>471.903320</td>\n",
              "      <td>412.423096</td>\n",
              "      <td>421.522797</td>\n",
              "      <td>454.807617</td>\n",
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              "      <td>426.903320</td>\n",
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              "      <td>1960-11-30</td>\n",
              "      <td>469.903320</td>\n",
              "      <td>402.032745</td>\n",
              "      <td>410.594971</td>\n",
              "      <td>436.452240</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pred",
              "summary": "{\n  \"name\": \"pred\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1959-12-31 00:00:00\",\n        \"max\": \"1960-11-30 00:00:00\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"1960-10-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoARIMA\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          426.9033203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoETS\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          357.94915771484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoltWinters\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          373.5909118652344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CES\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          398.501953125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoTheta\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          382.6738586425781\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred['y']=test['y'].values\n",
        "pred"
      ],
      "metadata": {
        "id": "okJ6l86Ei4cU",
        "outputId": "d1800393-becb-40b0-d25a-06a07639b4d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    unique_id         ds   AutoARIMA     AutoETS  HoltWinters         CES  \\\n",
              "0           1 1959-12-31  424.160156  406.651276   409.680145  410.827393   \n",
              "1           1 1960-01-31  407.081696  401.732910   390.044067  391.996124   \n",
              "2           1 1960-02-29  470.860535  456.289642   448.929810  456.414459   \n",
              "3           1 1960-03-31  460.913605  440.870514   435.445007  447.666779   \n",
              "4           1 1960-04-30  484.900879  440.333923   454.699951  472.150970   \n",
              "5           1 1960-05-31  536.903931  496.866058   507.904968  549.434143   \n",
              "6           1 1960-06-30  612.903198  545.839111   577.622375  621.770508   \n",
              "7           1 1960-07-31  623.903381  544.672485   581.996765  630.332764   \n",
              "8           1 1960-08-31  527.903320  477.034485   479.946136  513.530518   \n",
              "9           1 1960-09-30  471.903320  412.423096   421.522797  454.807617   \n",
              "10          1 1960-10-31  426.903320  357.949158   373.590912  398.501953   \n",
              "11          1 1960-11-30  469.903320  402.032745   410.594971  436.452240   \n",
              "\n",
              "     AutoTheta    y  \n",
              "0   412.711884  417  \n",
              "1   404.640472  391  \n",
              "2   466.810883  419  \n",
              "3   449.600372  461  \n",
              "4   454.047760  472  \n",
              "5   517.857727  535  \n",
              "6   572.380127  622  \n",
              "7   571.371643  606  \n",
              "8   502.108032  508  \n",
              "9   438.527100  461  \n",
              "10  382.673859  390  \n",
              "11  432.116119  432  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>unique_id</th>\n",
              "      <th>ds</th>\n",
              "      <th>AutoARIMA</th>\n",
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              "      <th>HoltWinters</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1959-12-31</td>\n",
              "      <td>424.160156</td>\n",
              "      <td>406.651276</td>\n",
              "      <td>409.680145</td>\n",
              "      <td>410.827393</td>\n",
              "      <td>412.711884</td>\n",
              "      <td>417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-01-31</td>\n",
              "      <td>407.081696</td>\n",
              "      <td>401.732910</td>\n",
              "      <td>390.044067</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-02-29</td>\n",
              "      <td>470.860535</td>\n",
              "      <td>456.289642</td>\n",
              "      <td>448.929810</td>\n",
              "      <td>456.414459</td>\n",
              "      <td>466.810883</td>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-03-31</td>\n",
              "      <td>460.913605</td>\n",
              "      <td>440.870514</td>\n",
              "      <td>435.445007</td>\n",
              "      <td>447.666779</td>\n",
              "      <td>449.600372</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-04-30</td>\n",
              "      <td>484.900879</td>\n",
              "      <td>440.333923</td>\n",
              "      <td>454.699951</td>\n",
              "      <td>472.150970</td>\n",
              "      <td>454.047760</td>\n",
              "      <td>472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-05-31</td>\n",
              "      <td>536.903931</td>\n",
              "      <td>496.866058</td>\n",
              "      <td>507.904968</td>\n",
              "      <td>549.434143</td>\n",
              "      <td>517.857727</td>\n",
              "      <td>535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-06-30</td>\n",
              "      <td>612.903198</td>\n",
              "      <td>545.839111</td>\n",
              "      <td>577.622375</td>\n",
              "      <td>621.770508</td>\n",
              "      <td>572.380127</td>\n",
              "      <td>622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-07-31</td>\n",
              "      <td>623.903381</td>\n",
              "      <td>544.672485</td>\n",
              "      <td>581.996765</td>\n",
              "      <td>630.332764</td>\n",
              "      <td>571.371643</td>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-08-31</td>\n",
              "      <td>527.903320</td>\n",
              "      <td>477.034485</td>\n",
              "      <td>479.946136</td>\n",
              "      <td>513.530518</td>\n",
              "      <td>502.108032</td>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-09-30</td>\n",
              "      <td>471.903320</td>\n",
              "      <td>412.423096</td>\n",
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              "      <td>454.807617</td>\n",
              "      <td>438.527100</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>1960-10-31</td>\n",
              "      <td>426.903320</td>\n",
              "      <td>357.949158</td>\n",
              "      <td>373.590912</td>\n",
              "      <td>398.501953</td>\n",
              "      <td>382.673859</td>\n",
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              "      <td>402.032745</td>\n",
              "      <td>410.594971</td>\n",
              "      <td>436.452240</td>\n",
              "      <td>432.116119</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b6f5219-9d32-43a1-bbde-702df04be306 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7b6f5219-9d32-43a1-bbde-702df04be306');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8b930fe6-07a7-470d-8122-103024e452af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b930fe6-07a7-470d-8122-103024e452af')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b930fe6-07a7-470d-8122-103024e452af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0b2ef719-54ba-4315-a8a0-89c327ed06e2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pred')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0b2ef719-54ba-4315-a8a0-89c327ed06e2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pred');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pred",
              "summary": "{\n  \"name\": \"pred\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"unique_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1959-12-31 00:00:00\",\n        \"max\": \"1960-11-30 00:00:00\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"1960-10-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoARIMA\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          426.9033203125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoETS\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          357.94915771484375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoltWinters\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          373.5909118652344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CES\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          398.501953125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AutoTheta\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          382.6738586425781\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 390,\n        \"max\": 622,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          535\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def mae(y_hat, y_true):\n",
        "    return np.mean(np.abs(y_hat-y_true))\n",
        "\n",
        "y_true = pred['y'].values\n",
        "ets_preds = pred['AutoETS'].values\n",
        "arima_preds = pred['AutoARIMA'].values\n",
        "hw_preds = pred['HoltWinters'].values\n",
        "CES = pred['CES'].values\n",
        "AutoTheta = pred['AutoTheta'].values\n",
        "\n",
        "print('AutoETS   MAE: %0.3f' % mae(ets_preds, y_true))\n",
        "print('ARIMA MAE: %0.3f' % mae(arima_preds, y_true))\n",
        "print('HW MAE: %0.3f' % mae(hw_preds, y_true))\n",
        "print('CES MAE: %0.3f' % mae(CES, y_true))\n",
        "print('AutoTheta MAE: %0.3f' % mae(AutoTheta, y_true))"
      ],
      "metadata": {
        "id": "EV4D4Kvgjz5Q",
        "outputId": "2408a582-4450-4d55-8b58-2d17285c0579",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AutoETS   MAE: 35.612\n",
            "ARIMA MAE: 18.551\n",
            "HW MAE: 23.490\n",
            "CES MAE: 10.145\n",
            "AutoTheta MAE: 19.357\n"
          ]
        }
      ]
    }
  ]
}