alyferryhalo/untitled5.ipynb

## untitled5.ipynb
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  "metadata": {
    "colab": {
      "name": "Untitled5.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMcw6QbJ3QiwtyOQ4aHkZSN",
      "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/gist/alyferryhalo/575d5edb83985a36bb95d82bb90f84d8/untitled5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4F_1d5v5Vx_2"
      },
      "source": [
        "# Введение\n",
        "В качестве тренировки я нашла датасет от Procter & Gamble в рамках виртуальной стажировки Shift+Enter от Challenge >>.\n",
        "\n",
        "## Датасет\n",
        "Для выполнения задания я пользовалась открытым датасетом [ shampoo_sales.csv,](https://www.kaggle.com/redwankarimsony/shampoo-saled-dataset) содержащим данные о ежемесячном объёме продаж шампуня за трехлетний период (всего 36 наблюдений).\n",
        "\n",
        "## Задача\n",
        "Дополнить код и получить еще два графика, которые нужны для анализа\n",
        "трендов и сезонности.\n",
        "\n",
        "## Дополнительная информация\n",
        "\n",
        "Существует несколько способов определения **трендов** во временных рядах, один из которых связан с использованием **скользящего среднего** (a rolling average). Это когда мы берем для каждой точки временного ряда среднее значение точек по обе стороны от нее. Такой подход позволяет сглаживать шум и влияние сезонности, выделять определенный тренд в\n",
        "данных, если он есть.\n",
        "\n",
        "Что касается **поиска сезонных закономерностей** в данных, то, наоборот, нужно **удалить тренд** временного ряда, чтобы было легче поймать сезонность. Для графического представления сезонности можно находить разность между последовательными точками данных, называемую **разностью первого порядка** (first-order difference).\n",
        "\n",
        "Hints. В Pandas есть функции rolling и diff, которые ты должен использовать для построения графиков. В коде комментарием укажи, такой тренд наблюдается для данных по продаже шампуня, если выбрать размер окна равным шести, а также сделай вывод про сезонность.\n",
        "\n",
        "## Полезная информация\n",
        "[Анализ временных рядов - тренд,\n",
        "сезонность, шум - Электронный учебник K-tree](https://k-tree.ru/articles/statistika/prognozirovanie/analiz_vremennih_ryadov)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5DfHKC5VwEG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cefb8078-3acd-44a8-f7d9-66964a21268c"
      },
      "source": [
        "import io\n",
        "from io import BytesIO\n",
        "from google.colab import files\n",
        "\n",
        "import csv\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "KdSgnMI5s9rS",
        "outputId": "7328127b-ff6c-4afb-c022-5c16ca8a0cb8"
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ef46ce27-a8b4-4d51-817c-c097f15634d6\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ef46ce27-a8b4-4d51-817c-c097f15634d6\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving shampoo_sales.csv to shampoo_sales.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3Dy2YQbv0zj"
      },
      "source": [
        "data = pd.read_csv(io.BytesIO(uploaded['shampoo_sales.csv']))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-Jc7r0Eyv59C",
        "outputId": "d25d934d-e2ac-4e04-f419-eb0d4b00973b"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1-01</td>\n",
              "      <td>266.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1-02</td>\n",
              "      <td>145.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1-03</td>\n",
              "      <td>183.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1-04</td>\n",
              "      <td>119.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1-05</td>\n",
              "      <td>180.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Month  Sales\n",
              "0  1-01  266.0\n",
              "1  1-02  145.9\n",
              "2  1-03  183.1\n",
              "3  1-04  119.3\n",
              "4  1-05  180.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "HGcezAzEwC0p",
        "outputId": "d21db215-c12a-4117-b2d1-0273867d4285"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>312.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>148.937164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>119.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>192.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>280.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>411.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>682.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Sales\n",
              "count   36.000000\n",
              "mean   312.600000\n",
              "std    148.937164\n",
              "min    119.300000\n",
              "25%    192.450000\n",
              "50%    280.150000\n",
              "75%    411.100000\n",
              "max    682.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "tnXDmYGiw488",
        "outputId": "d6262769-ed5b-470a-e0b6-8063b279e55e"
      },
      "source": [
        "sales = data[['Sales']]\n",
        "sales.plot(figsize=(12,10),linewidth=5, fontsize=20)\n",
        "plt.xlabel('Month', fontsize=20)\n",
        "plt.ylabel('Sales per month',fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "imAhkHUQSlr9",
        "outputId": "c9af57b0-87eb-4f6b-b6e5-68dc09792662"
      },
      "source": [
        "result = seasonal_decompose(sales, model='additive', freq=1)\n",
        "result.plot()\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hxdZ5W3/gJE//uJ24tFzuHtKWJ0d3rJIh6iLCc1d0JiPXwpzIOF67rhvX9Cx/ZQdVtVyeoERkPLDIGLNbRJ4H+ojIqzVhoq6qHRIycvlq7SGu7dWCV8Z2Iz3HwluL99GrVRCD2mt3TEX8tuM4L/yyi7Qc+/8VPQSaBvrRPMif5kH+tGhg/9kyyJ9FuxL5MzqZ167rVq4Whr+PJx/9rQ/XfLSGx+dsY8Yd/c+0enMKCnlzUTRfrztMaKM61bL+kIjw+vU9ePjS8HJ3E6rq4Y4uvheMMXMda0BdCryNfTn2ASWdICKtgJnYC8YaYKox5gMReQm4Bzi9kNNzxpgFjnOeBe4CrMDDxpjFbvguqhq8tzQGY+DxyzrYf6nc0IO9CZk8NGsrCx4eUuwkygvVr9vieWdJDNf2as7dw9qVe/2fk6fy+eevu/l9ZwI9W9bnP9eHkZ5TQHx6LvHpuRxPz2XrsTQW7Eyg0Pb/cyLvH96eWwaUvyBpx6b1ePHqrjz3884zrd7Nh1N5au52Dp/M4fbBoTw9pmOZy024i4eHaHKqRdzxr+T0iL0rgWnGmN9F5NUyzikEnnB0D9YDokRkqWPfe8aYt4seLCJdgAlAV+zD2ZeJSAdjjNtGCx5KycZitVW4xL1yTkxSFvOi4rjjorZnnnvU9fXi01v7MvajtTw4ayvf3z2gxO6pmmDbsXQ+WBZDp2aBPDoyHF+v8ndh2WyGd5ZG8/GfB2gR5M9//4hl5oYjPDA8jImD2jjVLbZwZwLP/7KLzDwLT43uyL3D2pX452a1GVJO5ROfnkuh1dAvtEG5Yz7t5v6tWBubwttLotl/Iouft8bTIsifWfcM1BawKhd3JKh4EfkcuAx4Q0R8KWO+lWMdpwTH+ywR2Uvpy7ePBX4wxuRjX7U3Fvvihutd8QWKiY+HZ20lISOPOfcOpF011R77K3hz0T4CfL148JKzy7l0CKnHf67vzqOzt/HW4mievaJzNUVYsiMns3lzcTS/70ignp8Xf0YnszI6mf/e3IuwJs7/x+ZUfiGPzd7G0j1JTOjXipfHdiM6MYs3F+/jtQV7mb7mEI+MDGd835bFJpy07AJenL+b+duP061FIN+PH0jHpqXf39NDCAn0I8QFrVMR4T83dGd7XDo/bYnnlgGtee6Kzk5XA1fqNHfU4qsDjAF2GmP2O1bB7W6MWeLk+aHAKqAb9hGBtwOZQCT2VlaaiHwEbDDGfOs4Zzqw0Bgzr7RrV6bUUeyJLG76fAM+Xh7MuXeQ0zPOlfM2HUrlxs/X8/SYjiXWG3v+l532QRMT+zK6BlTSBvuEz/8u3893G4/g5eHBPcPaMXlYO9YfOMnff9xBdn4hz1/ZmVsHtimzovWx1BzumRlJTFIWL1zVhdsHh551zvoDJ3lz8T62Hk2nbXAAj1/WgSu7NzvzrGfJ7kSe+3kXGbkFPDwinCnD25drXpIrxafnkpKVT89WOsdIla5Ka/GJSB9gCPbnSWudHSAhInWxLw//mjHmJxEJAVIc13kFaGaMubM8CUpEJgOTAVq3bt33yJEjFf5ee45ncvO0DQT6ezHn3kE0q+98peSazGYzLN6dyLG0HAL9vAn093b89DrzuZ6fl1t/0RljuP7TdSSk5/Hnk8Px9ym+Cyu/0Mr4z9ZzKCWb3x4aQptGAW6LqSy5BVa+XHuIz1YcILugkJv6teaxkeFnPSM7kZXHU3N3sDImmRGdmvDGDT1KXMV006FUpnwbhcVq4+O/9WFYh8bFHmeMYdneE7y9OJropCy6Ng/koRHhLN6dyM9b4+nSLJB3buxJ52aBbvneSrlaVVYz/ycwHvjJselaYK4xptTnUCLiDfwGLDbGvFvM/lDgN2NMtyJVKv7j2LcYeMkYU2oXnyuKxe6IS+eWaRtpXM+XH+4dSJN6tfuB/boDKfxnwT52xpc9l7qOjydNA/0Y1L4RQ8MbMzisUbkf2pdk0a4Epny7hTdu6M5N/VqXeuyx1Byu+nANLYL8+amaKmn/uCWOd5fEkJiZx2VdQvj7mI4lduMZY5ix7jD/XriPer5evDW+x3mj42ZvPsrzv+yiVYM6TJsU4dQSFlabYf72eN5dGsOx1Fy8PIQHR4TxwCVh1dZqUqoiqjJBRQM9jTF5js/+wDZjTMdSzhFgBpBqjHm0yPZmjudTiMhjwABjzAQR6Qp8j/25U3NgORBe1iAJV1Uzjzycym1fbqJVgzrMmjyQhgGuWfOlKkUnZvH6wr38GZ1MiyB/nhzdgZGdQziVX0hmbiGZeRYycy1k5Np/ZuYVkplr4fDJbNYfOEl2gRVPD6F3qyCGhjdmaIdgerSoX6HBC4VWG6PeW4WHh7DokaFOXePPfSe44+vN3BTRijfG9ajIH0G5GWNYvvcEbzlaLr1aBfHcFZ3p39a54dLRiVk88sNW9iVmMXFgG567ojPensJrC/by1drDDA0P5qOb+5S7akZBoY2le5Jo3ySATk211aRqn6peD+o6Y0y643MQ8FNp60E5hqSvBnYCpys6PgfcDPTC3sV3GLi3SML6B3An9hGAjxpjFpYVmyuX21gXm8IdX28mrEldvr9nIPX9a0cpnsSMPN5dGs28qDjq+nrx4IgwbhsUWq5WiMVqY+vRdFbvT2bV/hR2xKVjDAT6eXFRWDDDOjRmbK/mTg8l/m7jEf7x8y6m3RbBZV2cn3fz9uJoPvozljfH9eDGiFaAvVWRkWshPaeAtBz7z/QcC+m5FsKa1GVIWHCFqk+vO5DCW4ujzzz7eWp0Ry7v1rTcq6TmWay8vTiaL9Ycon3jAJrW92Nt7EnuvKgtz13RqUaPTlTKXdyeoETkQ+yJpDXQDzg9THwksMkYc71LblQJrl4PakX0Ce6ZGUm3FvX55q4BZZZhOb3g2foDJxkS3phelXx4nJZdQNSRNBrW9SEk0I/GdX3x8Sr+F1xWnoXPVh5g+ppD2Gxw26A2PDgizCUrfqZlF7D2QAqrY1JYtT+ZhIw8WgT588JVXRjdNaTUX+I5BYVc/NaKM5M3y/ML32ozTJy+kcjDaTQL8iM9x0JmnoXS/kk3r+/HuL4tGR/RyqmBLtuOpfP24mjWxKbQrL4fj1wazg19W1a6C231/mSemLOdtJwCXhnbjQn9S+/WVOpCVhUJapLjrT/2Jd8N9tZNLoAxZoZLblQJ7liwcPHuRO7/bgsRbRrw9R39z3u4b7Ha2HQolYW7EliyO4kTWfln9l3dszlPj+5Y7hGB2fmFTF9ziGmrDpKVX3jWvkYBPo7hwr6EBPrRJNAPTxFmrD9ManYBY3s158lR5b+ns4wxbDqUyovzd7MvMYvhHRvz0tVdCQ0ufjDDh8v3887SGH68bxB925S/skDKqXxeX7gPi9VGkL83QXV8CKrjTQPHz6A6PjSo401dXy82HExlduQxVu+3z/u+qH0wN/ZrxaguIee1IKMTs3hnSTRL9iTRMMCHBy4J45YBrV36vCszz0JadkG1DvRQqiaoigTlDbyGvdvtCPY6fK2Br7BXgKj2enzuWlF3/vbjPPrDVi4KC2babfY/47WxKSzclciyvUmk51jw9/bkkk6NGd21KQPaNuL7jUeYuvpguVoz+YVWvttwlI//jOVkdgGjuoRw+0Wh5FtsJGXmkZSZT2JmHicy80jKsn9OOZWPMTCoXSOeu6Kz08sUVFah1caM9Ud4b2kMBVYbUy5uz/3D25/1C/7kqXwufmsFF4U14vOJ5/3bdJv49FzmRh5jbmQc8em51Pf35rreLbipXysCfLx4b1kMv2yLp66PF/cMa8edQ9pWuEipUqpsVZGg3gPqAo8bY7Ic2wKxlzrKKTr4obq4c8n3uZHHeGreDsKb1OV4ei7ZBVbq+XlxWecQRndrysUdGp/3v+/Tz4PmRsUR6OfNg5eEcdvgNudVHrDaDD9tieP9ZfuJT89lULtGPD2mI71blz3bv9BqIzOvkAZ1vMv9vMQVkjLzeO33vczffpxWDf156equXNrZ/pzppfm7+WbDERY/OoywJlU/+dlmM6w9kMLszcdYsjuJAqsNDwFvTw9uvyiUKcPa06AWDoBRqrapigS1H+hgzrmgiHgC+4wx4S65USW4M0EBfL/xKJ+siGVoeDBjujVjULtGJT4TKmpvQib/WbiPVTHJtGroz1OjO3F1j2aAvQvx7SUxxJ44ZV+5dHQnLgprVC3JpjLWHUjhn7/uJvbEKUZ2DuHOi0KZ9NUmxvVtxX+u717d4ZGWXcAv2+JJOZXPbYNCXVJRQSnlnKpIUDHGmA7l3VeV3J2gKmv1/mT+vWAfexMy7bPvjWF7XAZhTery5KgOjO5a/lFjNUlBoY0v1x7ig2X7ybVY8ff2ZOVTw/9SxV+VUucrKUG5smN9j4jcZoyZec6NbwX2ufA+F6yh4Y357aFgft4azztLovEQ4c1xPbi+d4sLYvixj5cHUy5uzzU9m/Pe0hj6tGmgyUkpVSJXtqBaYK8ekQtEOTZHYB/Vd50xJt4lN6qEmt6CUkqpvyK3t6AcCWiAiIzAvgwGwAJjzHJX3UMppdRfh1uKxdZUIpKMfQh8RQVjL15bG9XW2Gtr3FB7Y6+tcYPGXh1cEXcbY8x51ZH/UgmqskQksrhmaG1QW2OvrXFD7Y29tsYNGnt1cGfctf/Ju1JKqQuSJiillFI1kiao8pla3QFUQm2NvbbGDbU39toaN2js1cFtceszKKWUUjWStqCUUkrVSJqglFJK1UiaoJRSStVImqCUUkrVSJqglFJK1UiaoJRSStVImqCUUkrVSJqglFJK1UiaoJRSStVImqCUUkrVSK5c8r3GCw4ONqGhodUdhlJKqSKioqJSilsP6i+VoEJDQ9El35VSF6rs/EK2x6UzuH1wdYdSLiJS7EKy2sWnlFIXiPeWxvC3aRtZF1sbF+Y9nyYopZS6AOQUFDIn8hgAry3Yi81W+1eq0ASllFIXgF+3HSczr5BbB7Zm9/FMftoa7/Z7Rh1J46X5u3HXsk2aoJRSqpYzxjBj3WE6Nwvk5Wu60bNlfd5avI+cgkK33C8uLYeHZm3lhk/XsWBnAomZeW65jyYopZSq5TYfTmNfYhaTBrXBw0N4/qouJGXmM23VIZfeJzu/kLcXR3PpOytZsjuRh0eE8eeTw2lW39+l9zntLzWKTymlLkQz1x8m0M+Lsb1aANAvtCFjujbl81UHuLl/K5oE+lXq+lab4ceoON5aEk1yVj7X9mrO02M60TzIPYnpNG1BKaVULZaUmceiXYnc1K8V/j6eZ7Y/c3knLFYb7yyJqdT11x84ydUfruHpH3fQqoE/P98/mPcn9HZ7cgJtQSmlVK32/cajWI3h1oFtztoeGhzAbYNC+XLtISYNDqVL88ByXffoyRxe/X0PS/Yk0SLIn//e3JurezRDRFwZfqm0BaWUUtUoLbuAB7/fwqZDqeU+t6DQxvebjjK8Q2PaNAo4b/9DI8II9PPm3wv2lmukXeThVK76cDVrYlN4anRHlj9xMdf0bF6lyQk0QSmlVLUpKLRx33dR/LYjgYdmbSE9p6Bc5y/anUhyVj63DQ4tdn9QHR8evjScNbEprIhOduqaf+xL4tbpGwmu68viR4fxwCVh+Hl7ln2iG2iCUkqpamCM4cX5u9hwMJUpF7fn5KkC/vnr7nJdY+a6w7RpVIeLw88rY3fGxIFtCG1Uh9cW7KXQaiv1ej9tieOemVGEN6nH3CmDaNWwTrnicTWXPYMSketL22+M+clV91JKqdruq7WHmbXpGPcPb8/TYzpRx8eTd5fGcFmXEK7u2bzM83cfzyDySBrPX9kZD4+Su958vDx45vLOTPk2ih82HzvvWdVpX6w+yKu/72Vw+0ZMvS2Cur7VP0TBlS2oqx2vu4DpwC2O1xfAnc5cQEQOi8hOEdkmIpGObQ1FZKmI7Hf8bODYLiLyXxGJFZEdItLHhd9FKaXc5s/oE7z6+x5Gdw3hyVEdAbh/eHt6tgri+V92keTExNdv1h/B39uT8X1blXns6K4h9A9tyHtLY8jKs5y1zxjDm4v28erve7mie1O+uqNfjUhO4MIEZYy5wxhzB+ANdDHG3GCMuQHo6tjmrEuMMb2MMRGOz88Ay40x4cByx2eAy4IdWwkAACAASURBVIFwx2sy8KkrvodSSrlTTFIWD32/lU5NA3nvpl5nWj9enh68e2NP8gutPD1vR6mDGtJzCvhlWzzX9m5B/Tpl/3oVEZ6/qjMnswv4ZMWBM9sLrTae/Wknn6w4wN8GtObDm/vg61U9z5uK445nUK2MMQlFPicBrStxvbHADMf7GcC1RbbPNHYbgCARaVaJ+yillFudPJXPXTM24+/jyReTIqjjc3ZLpX3jujx7eWdWxiTz/aajJV5nbmQceRYbtw0qvruuOD1aBnFd7xZMX3OIuLQc8ixWHvh+Cz9sPsZDI8J47dpueJbSVVgd3NGOWy4ii4FZjs83AcucPNcAS0TEAJ8bY6YCIUUSXiIQ4njfAjhW5Nw4x7aiyRERmYy9hUXr1pXJk0opVXH5hVamfBvFicx8Zt87qMSJrhMHtmHZ3iRe/W0vF7UPJjT47OHjVpvhmw1H6N+2IZ2blW9u01OjO7JgZwKv/LaHzNxC1h88yYtXd+GOi9pW+Hu5k8tbUMaYB4HPgZ6O11RjzENOnj7EGNMHe/fdAyIy7JxrG+xJrDzxTDXGRBhjIho3Lnmki1JKuYsxhn/8vIvNh9N4e3xPerUKKvFYDw/hzXE98PIUHp+zDes5y2asjDnB0dQcJg0KLXcczYP8uXtoWxbvTmLz4VQ+mNCrxiYncFMlCceIvXKP2jPGxDt+nhCRn4H+QJKINDPGJDi68E44Do8Hij4dbOnYppRSNcrUVQeZFxXHI5eGOzVCr1l9f14Z241HZ2/j81UHuH942Jl9M9YdISTQl1FdQ0q5QsnuGx5GXFou1/dpycUdavZ/2l3eghKR6x0j7jJEJFNEskQk04nzAkSk3un3wChgFzAfmOQ4bBLwq+P9fOA2x2i+gUDGOc++lFKq2i3dk8Tri/ZxVY9mPDoy3OnzxvZqzhXdm/Le0hh2H88A4FBKNitjkvlb/zZ4e1bs13ddXy8+mNC7xicncE8L6k3gamPM3nKeFwL87Cil4QV8b4xZJCKbgTkichdwBLjRcfwC4AogFsgB7nBF8Eqp2ifPYuXFX3eTlW/h3Rt7VaryQWp2AU/N3U6TQD+u6N6Uge0aVSgZHEvNYcmeJN5ZEk2PFvV5e3zPcpUKEhFevbY7mw+n8fjs7cx/6CK+WX8Eb0/h5gFlDy2/ELgjQSVVIDlhjDmI/ZnVudtPApcWs90AD1QoQqVUpUQdSaVdcF0aBPhUdyikZRcw+ZtINh9OA6DQupVPbumDVwWSSlaehUlfbiI6KQsvD2HWpqM0qOPNqC5Nubx7Uwa3D8bHq/jrGmPYm5DFkj2JLNmdxJ4Ee8dR9xb1mXpbRIWSZsMAH964oTt3fh3Ja7/v5eet8VzerRlN6lVu+Yzawh0JKlJEZgO/APmnN2olCaUuDL9ui+eRH7bRs2V95k4ZXOIv7Kpw9GQOt3+1ibi0XD68uTcnT+Xz0v/28MxPO3nzhh6lVlg4V57Fyl0zItmbkMnU2/oyuH0wK2OSWbgzgd93JjA78hiBfl5c1qUpV3RvypDwYDxF2Hw4jSV7Elm6J4m4tFxEIKJNA/5xRWcu6xJy3ii88hrRKYSb+7di5vojAEwa7PzQ8trOHQkqEHuX26gi2wwVGDShlKpZ1h84yZNzt9MuOIDtcRm8tXgf/7iyS7XEsvVoGnfPiMRqDN/dM4B+oQ0BSM+18P6y/dT39+b5Kzs71a1WUGjjvm+jHCPbejOik30AwuiuTRndtSn5hVbW7E9hwc5Elu5J5MctcdTz9cLLU0jLseDj5cHQsGAeGhHGiE4hNK7n69Lv+vyVXVh34CRBdXzo07qBS69dk7k8QTmqSSilLjAxSVlM/iaS0EYBzJsymLeXRDNt9SEGtw/mkk5NqjSWRbsSeXT2VprU8+OrO/rRvnHdM/seuTSc9BwL09ccokEdbx4cUfrABKvN8PicbfwZncy/r+vONcWMsvP18uTSziFc2jmEgsLurDuQwqJdiRRYbVzWOYRhHRoT4MbyQAG+Xix4eCg2Y6p8yYvq5PI/URHpgL3sUIgxppuI9ACuMca86up7KaWqRlJmHrd/uQk/b0++uqMf9et4848rO7P5cCpPzN3OgoeH0rR+1TwXmb7mEK/+voeeLYP4YlIEwXXPbq2ICP+8qguZuRbeXhJD/To+TCyhQKoxhud/2cVvOxJ49vJO/G1A2ZP5fbw8GN6xCcM7Vm1SdmcCrKnc0Xk8DXgWsAAYY3YAE9xwH6VUFTiVX8gdX20mPdfCV7f3o2UD+xIMft6efPS3PuQWWHl09tbzJpS6mtVmeGn+bl75bQ+juoQw656B5yWn0zw8hDfG9WBk5yb889dd/Lqt+CmSry/ax6xNR3ngkvbce3F7d4avKsAdCaqOMWbTOdsK3XAfpZSbWaw27v9uC9FJWXxySx+6tah/1v6wJnV5eWxXNhxM5eM/Y90WR26BvUzQ1+sOc+dFbfnklr74+5Q+Ks7b04OP/taH/qENeWLOdv7cd+Ks/Z+siOXzlQeZOLDNmYriqmZxR5sxRUTa4yhJJCLjOKc+nlLOiEvLIepIWrUsNa1Ol+fZyaqYZN64oXuJXVrj+rZkbWwK7y+LYUDbhgxo18ip6+dZrLy/bD9frT2EzRi8PT3OvHw8BS9PD7w9BW9PDzJzLSRk5pW7bpyft70o683TNjDl2yi+uWsA/ds25JsNR3hzUTTX9W7Bv67pqv++aigpzzr1Tl1QpB0wFRgMpAGHgFuMMUdceqMKiIiIMJGRkdUdhnKCMYZxn60n6kgar1/fnQn9tdBvVftg2X7eWxbDwyPCeLyMFsap/EKu+u9q8iw2Fj4ytMz5UTvi0nliznb2nzjF1T2b0yLIn0KrDYvVRoHVnHlvsRoKrDaMMdzcvzWXdq5YeZ+Tp/IZ//l6kjPzuWtoWz5Yvp9LO4Xw6a19KlyRQbmOiEQVWWLp/7e7MkGJiCfwhjHmSUe5Ig9jTJbLblBJmqBqj8W7E7n3myhCAn1Jy7Hw032Dz+teUu4zN/IYT83bwQ19WvL2+B5OtTB2xWdw/SfrGBoezBeTIoo9p6DQxod/7OeTFQdoXNeX10tpmbna8fRcxn26juMZeQxq14iv7uhXqYoTynXcnqBExMsYUygiG4wxA11yURfTBFU7FFptjH5/FQCzJg9k7Edr8fIUfntwqFOLs6nzZeVZeGPRPjYeTKVpfT9aBPnTIsif5o5Xywb+hAT64ePlwer9ydzx1WYGtmvEl7f3K9dE3K/WHuJf/9vDP6/qwp1Dzu6K2xWfwZNzt7MvMYtxfVvywlVdqO9ftX+fh1KymRd1jPuGh9WYVWNVyQnKlX9Dm4A+wFYRmQ/MBbJP79RKEspZc6PiOJCczecT+9Kknh8f/a0PN32+nifmbmfqxL7lqg5Q26VmFzAn8hhXdm9Gq4Z1KnSN1fuT+fu8HSRm5jE0vDHpOQXsTcgk5VTBWceJQEg9P9JzCwhrUpdPbu1T7ioRtw8OZW3sSf6zcC/9QhvSvWV9LFYbH/8Zy0d/xNIgwIfpkyIq3FVXWW2DA3hqdKdqubcqP1e2oLYYY/qIyFdFNhtAsJfOu7OUc1sBM7EXjDXY15D6QEReAu4Bkh2HPmeMWeA451ngLsAKPGyMWVxWjJVpQVlt5syDXOU+OQWFDH9rBa0a1mHelEFnuolO/8/872M6cd/wmj0c2GYzLN93grbBAYQ1qVv2CSWITszi7pmbOZaaay8Q2r81D44Ic7oO26n8Qv69YC/fbzxKu8YBvDO+J72LVCHIs1hJyMgjPi2X4+m5xKfbfxbaDE+P6Uiz+sUvqFeWtOwCrvjvany9PHjnxp7889fd7D6eybW9mvPSNV0JqlP99ftUzVIVLagmIvI49iUyTiem08rKgoXAE8aYLY4lN6JEZKlj33vGmLeLHiwiXbDPreoKNAeWiUgHY4zVFV/kXMYYnpq7nQKrjQ8m9K5xyyJfSL5ae5gTWfl8ckufs55h3D44lMgjaby1eB+9WgUxqL1zI8Wq2rrYFP69cC+74jPx8fLg2cs7cfvg0HKPElu2J4lHfthKgK8X0ydFsHzfCb7beJS5kXHcOSSUycPal9o9ti42hafm7eB4Ri6Th7Xj8cs6nPe8xc/bk7bBAbStZK24czUI8OGDCb2ZMHU9N3y6nuC6Pnx2a1/GdGvq0vuoC58rE5QnUJezE9NppSYoxzpOCY73WSKyF/vy7SUZC/xgjMkHDolILPbFDddXJPCyiAgdmtbj9YX78PXy5K1x5StCqZyTml3AZysOcFmXECIcddVOExHeuKEHexMyeWjWVhY8PIQmgTWnovO+xExeX7iPFdHJtAjy540burNkdxL/+t8e/oxO5u1xPZyK1xjDZysP8ubiffYq2BMjaFrfj0s7hzB5aDveXRrDx38e4NsNR5lycXtuHxx61nyg7PxCXl+4j282HKFtcADzpgyib5uGpdzRPfq3bcgr13ZjV3wGT43uRMMaUPVc1T4u7+JzwXVCgVVAN+Bx4HYgE4jE3spKE5GPgA3GmG8d50wHFhpj5hVzvcnAZIDWrVv3PXKk4qPd318Ww/vL9nPLgNa8em23C2ruhMVqI89iJcDHq9qS78v/28PX6w6x5LFhhDWpV+wx0YlZXPvxWrq3rM/3dw+o0JIKrpSQkcs7S2LOFA99aEQ4Ewe1wc/bE2MM3248ymu/76GOjxevX9+dUV1LbkXkWaw8+9NOft4az9U9m/PWuB7FjjLbczyTt5dE88e+EzSu58vDl4ZzU0QrthxN46l524lLy+XOi9ry5KiOZU5mVaomqIpRfFuNMb0reY26wErgNWPMTyISAqRgb4G9AjQzxtxZngRVVGVH8RljeH3RPj5feZC7hrR1ulJyTVZotTE78hjvLd1Pyql8PATq+XkT6O9FoJ839f29CSzyOSTQj0HtG9GlWaBLE9mx1BxGvLOCG/q05PUbepR67M9b43hs9nbuvbgdz17e2WUxlEdmnoVPVxzgyzWHMAZuvyiU+4e3L/b5SuyJLB75YRu7j2dyc//WvHBVZ+r4nN15cSIzj8nfRLHtWDpPjurAA5eElflva/PhVN5aFM2mw6k0qefLiax82jSqw1vjetK/bdW3mpSqqKp4BnXeooLlISLewI/Ad6dH/Bljkorsnwb85vgYDxRdUrKlY5tbiQjPjOlEvsXG9DWH8Pf25MnRtbNEijHmzFLUB5Oz6RfagHuGtuVUfiGZuRYy8+w/M3ItHEw5RWZuIZl5FnIK7I/5guv6MCQsmKHhjRkaHlzp7rZ3lkTj6SE8OrJDmcde17slkYfT+HzlQfq2blBqq8TV8gutfLfhKB/+sZ+0HAvX9W7B45d1KHWEXViTevx8/0W8szSaqasOsvHgSd6f0IseLYMA2BmXwT0zI8nMs5TrWU2/0IbMvncgK2KSmb76EFf1aM6Tozucl/yUqq1cXkmiQkHY/6s4A0g1xjxaZHszx/MpROQxYIAxZoKIdAW+x/7cqTmwHAgva5CEq+ZB2WyG537eyQ+bj/HkqA5llvOvabYcTeM/C/ay+XAa7RoH8MyYTlzWJcSp1uCJzDxW709h9f5kVu9P4WS2fahyp6b1GBpuT1j92zYs1wTIXfEZXPXhGu4f3p6nxzg3BDjPYmX8Z+s5fDKb3x4aQptGJT/oN8aQZ7Hh5+1R4RZvodXGT1vj+WDZfuLTc7korBHPXt653JOH1x1I4Yk520nOyucxR2J7et52GgX4Mu22CLo0D6xQfErVZlVSSaKiRGQIsBrYCdgcm58DbgZ6Ye/iOwzcWyRh/QO4E/sIwEeNMQvLuo8rJ+pabYYn527n563xPH9lZ+4e2s7pc0+eyqdBHZ8qf9ZzKCWbtxbvY8HORILr+vLYZfZnFxV9jmOzGfYmZrIqxp6wIg+nUWC1J4I7L2rLgyPCnPrf/MTpG9kVn8HKpy8h0M/5iZvHUnO46sM1NKvvx7W9W5CeYyE9p4C0nALHe4v9fa6FgkIbLRv4M75vK8ZHtKR5kHNDqG02w8JdibyzNJqDydn0aFmfJ0d1ZGh4cIWTXUaOhed+2cnvO+wlKvuFNuDTW/uWWJlbqQtdjU5QVcXVlSQKrTYe/mErC3Ym8sq13Updc2ZPQiaLdyWyaHciMUmn6N6iPs9e0YnB7YMrdO/49Fw+WBbDiuhkGgb4EBLoR0igLyGBfjQJ9COknq9jmx8eHvDJnwf4dsMRfLw8mDysHfcMbefy9WVyCgrZeCiVX7bG8+u247QI8uefV3dhVCmts9X7k5k4fRMvXNWFu4Y4XwT0tD/2JTHlmy0UWG34eHoQVMfb8fKhQR1vgvx9CArwpp6vFxsOprImNgURGBbemJv6tWJk55BiJ6MaY1gZk8zbS6LZFZ9JeJO6PDGqI6O7OtfSLIsxhvnbjxN74hQPjgjD10sHM6i/Lk1QuKfUUUGhjfu/i2LZ3hO8Na4H4yPsj8ZsNsPWY+ks3p3Iol2JHE3NwUPsw2/7hzbkxy3xxKfnMqJTE569vBPhIcWPWjvXyVP5jmHG9tGIo7s1Jc9i5URmHkmZ+SSfyi92XR5PD2FCv1Y8MjLc6YmelbHpUCov/LKL6KQsLunYmJeu6XpeN5zNZrj6ozVk5FpY/sTFFf4lfSq/EA8Bf2/PMpPHsdQc5kbFMS/yGMcz8mgY4MN1vVtwU79WdHD8HRQdfNCqoT+PjezA2F4tdP6bUm6iCQr31eLLs1i5Z2Yka2NTeGp0JxIyclm8O5GkzHy8PYWLwoIZ07UpI7uEnOnGybNY+WrtYT75M5bsgkJu6teaxy4rOXlk5VmYtvoQ01cfJNdiZXzfVjw8MpwW53RVWW2Gk6fyScrMJykzj6SsPNJzLIzu2rRSVQ0qwmK1MWPdYd5bGoPFZrjv4vbcN7z9medTv26L55EftvH+Tb24tndp095cz2ozrN6fzJzIYyzdk4TFaujVKohAf29WxSTTpJ4vDzmGb5e33I9Sqnw0QeHeYrG5BVYmfbWJTYdS8fP2YHiHJozp1pRLOjUpdcZ/anYB/12+/6zut8nD2p15dpNnsfLN+iN8siKWtBwLV3ZvxuOjOtC+cdUmm8pIyszjtd/3Mn/7cVo3rMO/runK4LBGjHx3JfV8vfntoSHVOvH55Kl8ft4az5zIY6ScKmDysHZMGhSqc4iUqiKaoHB/NfOcgkKijqTRt02Dcg/1PZySzZuOAQyN6/ny+GX24dYfLNtPYmYewzo05qlRHenesvYuObEuNoUXft3FgeRswpvUZf+JU8y8sz/DOjSu7tCUUtVIExS1Y7mNqCNp/HvBXqKOpAHQp3UQT4/pxEAnVymt6QoK7XPI/rt8P/3aNmTmnf2rOySlVDXTBEXtSFBgH+G1IiYZDxGGVWI4c02WkWvB18tDF4xTSlVJJQnlIiLCJVW0ymh1qeqF6pRStc9fqgUlIslAxavFQjD22oC1UW2NvbbGDbU39toaN2js1cEVcbcxxpz3MPovlaAqS0Qii2uG1ga1NfbaGjfU3thra9ygsVcHd8atEzyUUkrVSJqglFJK1UiaoMpnanUHUAm1NfbaGjfU3thra9ygsVcHt8Wtz6CUUkrVSNqCUkopVSNpglJKKVUjaYJSSilVI2mCUkopVSNpglJKKVUjaYJSSilVI2mCUkopVSNpglJKKVUjaYJSSilVI1VrghKRMSISLSKxIvJMMft9RWS2Y/9GEQl1bA8VkVwR2eZ4fVbVsSullHKvaluwUEQ8gY+By4A4YLOIzDfG7Cly2F1AmjEmTEQmAG8ANzn2HTDG9KrSoJVSSlWZ6lxRtz8Qa4w5CCAiPwBjgaIJaizwkuP9POAjqcT658HBwSY0NLSipyullHKDqKiolOIWLKzOBNUCOFbkcxwwoKRjjDGFIpIBNHLsaysiW4FM4HljzOqybhgaGkpkZGSlA1dKKeU6IlLsSufVmaAqIwFobYw5KSJ9gV9EpKsxJvPcA0VkMjAZoHXr1lUcplJKqYqqzkES8UCrIp9bOrYVe4yIeAH1gZPGmHxjzEkAY0wUcADoUNxNjDFTjTERxpiIxo3Pa0EqpZSqoaozQW0GwkWkrYj4ABOA+eccMx+Y5Hg/DvjDGGNEpLFjkAUi0g4IBw5WUdxKKaWqQKldfCLSp7T9xpgtFb2x45nSg8BiwBP40hizW0ReBiKNMfOB6cA3IhILpGJPYgDDgJdFxALYgCnGmNSKxqKUUqrmKXVFXRH5s5RzjTFmhOtDcp+IiAijgySUUqpmEZEoY0zEudtLbUEZYy5xX0hKKaVUyZwexSci3YAugN/pbcaYme4ISimllHIqQYnIi8Bw7AlqAXA5sAbQBKWUUsotnB3FNw64FEg0xtwB9MQ+5FsppZRyC2cTVK4xxgYUikggcIKz5zAppZRSLuXsM6hIEQkCpgFRwClgvduiUkop9ZfnVIIyxtzvePuZiCwCAo0xO9wXllJKqb+68oziawG0OX2OiAwzxqxyV2BKKaX+2pwdxXd6HaY9gNWx2QCaoJRSSrmFsy2oa4GOxph8dwajlFJKnebsKL6DgLc7A1FKKaWKcrYFlQNsE5HlwJlWlDHmYbdEpZRS6i/P2QQ1n/OXwlBKKaXcxtlh5jMcazadXhQw2hhjcV9YSiml/uqcegYlIsOB/cDHwCdAjIgMq+zNRWSMiESLSKyIPFPMfl8Rme3Yv1FEQovse9axPVpERlc2FqWUUjWLs1187wCjjDHRACLSAZgF9K3ojR0r4n4MXAbEAZtFZL4xZk+Rw+4C0owxYSIyAXgDuElEumBfvLAr0BxYJiIdjDFWlFJKXRCcTVDep5MTgDEmRkQqO6qvPxBrjDkIICI/AGOxz7U6bSzwkuP9POAjERHH9h8cw94POVbc7Y8byy/963+72XM8012XV0qpWqlL80BevLqrW67t7DDzSBH5QkSGO15fAJVdmrYFcKzI5zjHtmKPMcYUAhlAIyfPBUBEJotIpIhEJicnVzJkpZRSVcXZFtR9wAPA6WHlq7E/i6rxjDFTgalgX/K9otdx1/8QlFJKFc/ZUXz5wLvAuyLSEGjpgqoS8Zy9ZEdLx7bijokTES/sa1CddPJcpZRStZizo/hWiEigIzlFAdNE5L1K3nszEC4ibR1D2Cdw/lyr+cAkx/txwB/GGOPYPsExyq8tEA5sqmQ8SimlahBnu/jqG2MyReRuYKYx5kURqdRyG8aYQhF5EFgMeAJfGmN2i8jLQKQxZj4wHfjGMQgiFXsSw3HcHOwDKgqBB3QEn1JKXVicTVBeItIMuBH4h6tuboxZACw4Z9s/i7zPA8aXcO5rwGuuikUppVTN4uwovpext3RijTGbRaQd9om7SimllFs4O0hiLjC3yOeDwA3uCkoppZRydsFCP+xVHboCfqe3G2PudFNcSiml/uKc7eL7BmgKjAZWYh/WneWuoJRSSilnE1SYMeYFINsYMwO4EhjgvrCUUkr91TmboE4vrZEuIt2wT5ht4p6QlFJKKeeHmU8VkQbAC9gnydYF/ln6KUoppVTFOTuK7wvH25VAO/eFo5RSStk5W+ooRESmi8hCx+cuInKXe0NTSin1V+bsM6ivsU/Ube74HAM86o6AlFJKKXA+QQUbY+YANjizNpPWvlNKKeU2ziaobBFpBBgAERmIffFApZRSyi2cHcX3OPbRe+1FZC3QGPvyF0oppZRblNqCEpF+ItLUGLMFuBh4DsgHlmBfZl0ppZRyi7K6+D4HChzvB2NfauNjIA3HMuoVISINRWSpiOx3/GxQwnGTHMfsF5FJRbavEJFoEdnmeOmkYaWUusCUlaA8jTGpjvc3AVONMT86yh6FVeK+zwDLjTHhwHLH57M4Vu99EXtJpf7Ai+cksluMMb0crxOViEUppVQNVGaCEpHTz6kuBf4oss/Z51fFGQvMcLyfAVxbzDGjgaXGmFRjTBqwFBhTiXsqpZSqRcpKMrOAlSKSAuQCqwFEJIzKjeILMcYkON4nAiHFHNMCOFbkc5xj22lfiYgV+BF41RhjiruRiEwGJgO0bt26EiErpZSqSqUmKGPMayKyHGgGLCmSBDyAh0o7V0SWYV+i41xnLRlvjDEiUmxyKcUtxph4EamHPUFNBGaW8B2m4nheFhERUd77KKWUqiZldtMZYzYUsy3GifNGlrRPRJJEpJkxJkFEmgHFPUOKB4YX+dwSWOG4drzjZ5aIfI/9GVWxCaqoqKioFBE5UtZxpQgGUipxfnWqrbHX1rih9sZeW+MGjb06uCLuNsVtlBJ6xtxKRN4CThpjXheRZ4CGxpinzzmmIRAF9HFs2gL0BTKBIGNMioh4Y++GXGaM+awK4o40xkS4+z7uUFtjr61xQ+2NvbbGDRp7dXBn3M5WknC114HLRGQ/MNLxGRGJEJEvAByjB18BNjteLzu2+QKLRWQHsA17S2ta1X8FpZRS7lSZkXgVZow5iX1U4LnbI4G7i3z+EvjynGOysbeklFJKXcCqqwVVW1V4cnINUFtjr61xQ+2NvbbGDRp7dXBb3NXyDEoppZQqi7aglFJK1UiaoJRSStVImqCUUkrVSJqglFJK1UiaoJRSStVImqCUUkrVSJqglFJK1UiaoJRSStVImqCUUkrVSJqglFJK1UjVmqBEZIyIRItIrGPZjXP3+4rIbMf+jSIS6tgeKiK5IrLN8XL7UhtKKaWqVrVUMwcQEU/gY+Ay7Mu5bxaR+caYPUUOuwtIM8aEicgE4A3gJse+A8aYXuW5Z3BwsAkNDa188EoppVwmKioqxRjT+Nzt1ZagsK+CG2uMOQggIj8AY4GiCWos8JLj/TzgIxGRit4wNDSUyMjIip6ulFLKDUpa6bw6u/haAMeKfI5zbCv2GGNMIZABNHLsaysiW0VkpYgMLekmIvJ/7d17jB1VAcfx7y8FRNEK1aI8Wm21aAQVlCzvOAAACs9JREFU9YqPGEJEBJ/4QCxBU+MDSSDB8IeCL6SRxAegf2gwRYpQxYoPdDUaBRHUP8TuIgoUkVIwtiAUKpZiVB4//5izenPdvTvt3umd2/4+yWZnzj33zq8n2Xs6M2fOOUnSuKTxTZs2DS59REQ0alQHSdwNLLT9IuB04DJJc6eqaHuF7Y7tzvz5/3cGGRERLTXMDmojsKBr/8BSNmUdSbsBTwbut/2vsiovtieA24GDGk8cERE7zDA7qDXAEkmLJO0BLAXGeuqMAcvK9nHA1bYtaX4ZZIGkxcASYP0Oyh0RETvA0AZJ2H5E0qnAT4E5wErbN0taDozbHgMuAlZJWgdspurEAA4Hlkt6GHgMONn25h3/r4iIiKb0XfJd0un93mz7/IEnalCn03FG8UVEtIukCdud3vKZzqCe1FCeiIiIvvp2ULbP3lFBIiIiutW6ByVpT6pZHQ4G9pwst/3ehnJFRMQuru4ovlXA04GjgWuphoQ/2FSoiIiIuh3Us21/AnjI9iXAG4CXNRcrIiJ2dXU7qIfL7wckHUL1wOy+zUSKiIio/xzUCkn7AJ+genj2icAnG0sVERG7vFodlO2vls1rgcXNxYmIiKjUHcU35dmS7eWDjRMREVGpe4nvoa7tPYE3ArcMPk5ERESl7iW+87r3JZ1LNYdeREREI7Z3NvMnUD0LFRER0Yi696BuBCZnlZ0DzAdy/ykiIhpT9wzqjcCbys9rgf1tf2m2B5d0jKRbJa2TdMYUrz9O0rfK69dJembXa2eW8lslHT3bLBER0S59z6AkzSubvdMazZXEbNZgKgsOfhk4CtgArJE0ZnttV7X3AX+z/WxJS4HPAu+U9DyqtaEOBvYHrpJ0kO1HtzdPRES0y0xnUBPAePm9CfgTcFvZnpjlsQ8D1tleb/vfwGrg2J46xwKXlO3vAEdKUilfXZZ+vwNYVz4vIiJ2EjMtt7EIQNKFwBW2f1z2Xwe8ZZbHPgD4S9f+Bv5/fr//1ikr8P4deEop/03Pew+YZZ6+zv7hzay9a0uTh4iIGDnP238uZ73p4EY+u+49qJdPdk4Atn8CvLKRRAMm6SRJ45LGN23aNOw4ERFRU90Hde+S9HHg62X/ROCuWR57I7Cga//AUjZVnQ2SdqOapPb+mu8FwPYKYAVUS75vb9im/ocQERFTq3sGdQLV0PIrys++pWw21gBLJC2StAfVoIexnjpjwLKyfRxwtW2X8qVllN8iYAnw21nmiYiIFqk7k8Rm4LRBHrjcUzqVakaKOcBK2zdLWg6M2x4DLgJWSVoHbKbqxCj1LgfWAo8Ap2QEX0TEzkXVCck0L0pftP0hST/kfw/q/pftNzcZbtA6nY7Hx8eHHSMiIrpImrDd6S2f6QxqVfl97uAjRURETG+mYeYT5fe1k2Vl4cIFtv/QcLaIiNiF1RokIekaSXPLzBLXAxdKOr/ZaBERsSurO4rvyba3AG8DLrX9MuA1zcWKiIhdXd0OajdJ+wHHAz9qME9ERARQv4NaTjUc/HbbayQtppqTLyIiohF1n4P6NvDtrv31wNubChUREVF3kMRBkn4u6aay/4Iy9VFEREQj6l7iuxA4E3gYoAwxX9pUqIiIiLod1BNs985198igw0REREyq20HdJ+lZlOmOJB0H3N1YqoiI2OXVXW7jFKolK54raSNwB9WSGxEREY2oO4pvPfAaSXtRnXX9g+oe1J8bzBYREbuwvpf4yvRGZ0r6kqSjqDqmZcA6qod2IyIiGjHTPahVwHOAG4EPAL8A3gG81fax23tQSfMkXSnptvJ7n2nqLSt1bpO0rKv8Gkm3Srqh/Oy7vVkiIqKdZrrEt9j28wEkfZVqYMRC2/+c5XHPAH5u+zOSzij7H+muUCamPQvoUA3OmJA0ZvtvpcqJtrO4U0TETmqmM6iHJzfKirUbBtA5ARwLXFK2LwHeMkWdo4ErbW8undKVwDEDOHZERIyAmc6gXihpS9kW8PiyL8C2527ncZ9me3KY+l+Bp01R5wDgL137G0rZpIslPQp8F/i0+y0NHBERI2emBQvnbO8HS7oKePoUL32s5xiWtK2dy4m2N0p6ElUH9W7g0mlynAScBLBw4cJtPExERAxL3eegtpntadeLknSPpP1s312W8bh3imobgSO69g8ErimfvbH8flDSZcBhTNNB2V5B9QwXnU4nZ1kRESNCw7gyJunzwP1dgyTm2f5wT515wATw4lJ0PfASYAuwt+37JO0OfBO4yvZXahx3E7N7duupwH2zeP8wjWr2Uc0No5t9VHNDsg/DIHI/w/b83sJhdVBPAS4HFlJ1GMfb3iypA5xs+/2l3nuBj5a3nWP74vKw8C+B3YE5wFXA6WUQR9O5x213mj5OE0Y1+6jmhtHNPqq5IdmHocncjV3i68f2/cCRU5SPA+/v2l8JrOyp8xDVmVREROzE6k4WGxERsUOlg9o2K4YdYBZGNfuo5obRzT6quSHZh6Gx3EO5BxURETGTnEFFREQrpYOqSdIxZYLadWVo/EiQdKekG8ukuq2eu1DSSkn3Srqpq6zWxMLDNk32T0na2DWp8euHmXEqkhZI+oWktZJulnRaKW91u/fJPQptvqek30r6fcl+dilfJOm68h3zLUl7DDtrrz7Zvybpjq52P3Qgx8slvplJmgP8CTiKasqlNcAJttcONVgNku4EOrZb/3yFpMOBrcCltg8pZZ8DNnc9M7eP7Y/0+5xhmCb7p4Ctts8dZrZ+yoPy+9m+vszMMkE1N+Z7aHG798l9PO1vcwF72d5anuX8NXAacDrwPdurJX0F+L3tC4aZtVef7CcDP7L9nUEeL2dQ9RwGrLO93va/gdVUE97GANn+JbC5p7jOxMJDN0321rN9t+3ry/aDwC1Uc162ut375G49V7aW3d3Lj4FXA5Nf8K1rc+ibvRHpoOqZaeLaNjPwM0kTZV7CUVNnYuE2O1XSH8olwFZdJusl6ZnAi4DrGKF278kNI9DmkuZIuoFqmrcrgduBB2w/Uqq09jumN7vtyXY/p7T7FyQ9bhDHSge183uV7RcDrwNOKZeiRlKZsX6UrklfADwLOJRqLbXzhhtnepKeSDXx8odsb+l+rc3tPkXukWhz24/aPpRqjtHDgOcOOVJtvdklHQKcSfVveCkwj571/bZXOqh6NgILuvYPLGWt1zWx7r3AFVR/DKPknnK/YfK+w1QTC7eS7XvKH/NjwIW0tO3LvYTvAt+w/b1S3Pp2nyr3qLT5JNsPUK1U/gpgb0mTs/u0/jumK/sx5ZKrbf8LuJgBtXs6qHrWAEvKKJs9gKXA2JAzzUjSXuUGMmUOw9cCN/V/V+uMAcvK9jLgB0PMsk0mv+CLt9LCti83vS8CbrF9ftdLrW736XKPSJvPl7R32X481eCrW6i+7I8r1VrX5jBt9j92/WdGVPfOBtLuGcVXUxmu+kWqCWpX2j5nyJFmJGkx1VkTVPMuXtbm3JK+SbXEylOBe4CzgO8zxcTCw8o4nWmyH0F1qcnAncAHu+7rtIKkVwG/Am4EHivFH6W6n9Padu+T+wTa3+YvoBoEMYfqJOFy28vL3+tqqktkvwPeVc5IWqNP9quB+VSL2d5ANen31uk/qebx0kFFREQb5RJfRES0UjqoiIhopXRQERHRSumgIiKildJBRUREK6WDioiIVkoHFRERrZQOKiIiWuk/XGEmQPFFN2YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KM3xNyhfs7qu",
        "outputId": "e31d755a-4df7-4bb7-acaa-19d5093f17b4"
      },
      "source": [
        "print(result.trend)\n",
        "print(result.seasonal)\n",
        "print(result.resid)\n",
        "print(result.observed)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Sales\n",
            "0   266.0\n",
            "1   145.9\n",
            "2   183.1\n",
            "3   119.3\n",
            "4   180.3\n",
            "5   168.5\n",
            "6   231.8\n",
            "7   224.5\n",
            "8   192.8\n",
            "9   122.9\n",
            "10  336.5\n",
            "11  185.9\n",
            "12  194.3\n",
            "13  149.5\n",
            "14  210.1\n",
            "15  273.3\n",
            "16  191.4\n",
            "17  287.0\n",
            "18  226.0\n",
            "19  303.6\n",
            "20  289.9\n",
            "21  421.6\n",
            "22  264.5\n",
            "23  342.3\n",
            "24  339.7\n",
            "25  440.4\n",
            "26  315.9\n",
            "27  439.3\n",
            "28  401.3\n",
            "29  437.4\n",
            "30  575.5\n",
            "31  407.6\n",
            "32  682.0\n",
            "33  475.3\n",
            "34  581.3\n",
            "35  646.9\n",
            "    Sales\n",
            "0     0.0\n",
            "1     0.0\n",
            "2     0.0\n",
            "3     0.0\n",
            "4     0.0\n",
            "5     0.0\n",
            "6     0.0\n",
            "7     0.0\n",
            "8     0.0\n",
            "9     0.0\n",
            "10    0.0\n",
            "11    0.0\n",
            "12    0.0\n",
            "13    0.0\n",
            "14    0.0\n",
            "15    0.0\n",
            "16    0.0\n",
            "17    0.0\n",
            "18    0.0\n",
            "19    0.0\n",
            "20    0.0\n",
            "21    0.0\n",
            "22    0.0\n",
            "23    0.0\n",
            "24    0.0\n",
            "25    0.0\n",
            "26    0.0\n",
            "27    0.0\n",
            "28    0.0\n",
            "29    0.0\n",
            "30    0.0\n",
            "31    0.0\n",
            "32    0.0\n",
            "33    0.0\n",
            "34    0.0\n",
            "35    0.0\n",
            "    Sales\n",
            "0     0.0\n",
            "1     0.0\n",
            "2     0.0\n",
            "3     0.0\n",
            "4     0.0\n",
            "5     0.0\n",
            "6     0.0\n",
            "7     0.0\n",
            "8     0.0\n",
            "9     0.0\n",
            "10    0.0\n",
            "11    0.0\n",
            "12    0.0\n",
            "13    0.0\n",
            "14    0.0\n",
            "15    0.0\n",
            "16    0.0\n",
            "17    0.0\n",
            "18    0.0\n",
            "19    0.0\n",
            "20    0.0\n",
            "21    0.0\n",
            "22    0.0\n",
            "23    0.0\n",
            "24    0.0\n",
            "25    0.0\n",
            "26    0.0\n",
            "27    0.0\n",
            "28    0.0\n",
            "29    0.0\n",
            "30    0.0\n",
            "31    0.0\n",
            "32    0.0\n",
            "33    0.0\n",
            "34    0.0\n",
            "35    0.0\n",
            "    Sales\n",
            "0   266.0\n",
            "1   145.9\n",
            "2   183.1\n",
            "3   119.3\n",
            "4   180.3\n",
            "5   168.5\n",
            "6   231.8\n",
            "7   224.5\n",
            "8   192.8\n",
            "9   122.9\n",
            "10  336.5\n",
            "11  185.9\n",
            "12  194.3\n",
            "13  149.5\n",
            "14  210.1\n",
            "15  273.3\n",
            "16  191.4\n",
            "17  287.0\n",
            "18  226.0\n",
            "19  303.6\n",
            "20  289.9\n",
            "21  421.6\n",
            "22  264.5\n",
            "23  342.3\n",
            "24  339.7\n",
            "25  440.4\n",
            "26  315.9\n",
            "27  439.3\n",
            "28  401.3\n",
            "29  437.4\n",
            "30  575.5\n",
            "31  407.6\n",
            "32  682.0\n",
            "33  475.3\n",
            "34  581.3\n",
            "35  646.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "IO0pKB8EtEtd",
        "outputId": "4f774f0d-5600-4903-b114-cc2eca3f760f"
      },
      "source": [
        "result = seasonal_decompose(sales, model='multiplicative', freq=1)\n",
        "result.plot()\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "uGbIBXkztTN-",
        "outputId": "311dd216-c82a-4afb-c334-5d71e457d242"
      },
      "source": [
        "from pandas import read_csv\n",
        "from pandas import datetime\n",
        "from matplotlib import pyplot\n",
        "\n",
        "def parser(x):\n",
        "\treturn datetime.strptime('190'+x, '%Y-%m')\n",
        "\n",
        "series = data\n",
        "print(series.head())\n",
        "series.plot()\n",
        "pyplot.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Month  Sales\n",
            "0  1-01  266.0\n",
            "1  1-02  145.9\n",
            "2  1-03  183.1\n",
            "3  1-04  119.3\n",
            "4  1-05  180.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "FpdtwYH7t30q",
        "outputId": "f5ea924f-8684-4ecd-86f0-84adf3c7a78d"
      },
      "source": [
        "from pandas.plotting import autocorrelation_plot\n",
        "autocorrelation_plot(sales)\n",
        "pyplot.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZXfZwydNuChI",
        "outputId": "c1b3cc3c-5a5b-4410-b0cb-9f49a62aeb56"
      },
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from pandas import DataFrame\n",
        "\n",
        "model = ARIMA(sales, order=(5,1,0))\n",
        "model_fit = model.fit(disp=0)\n",
        "print(model_fit.summary())\n",
        "# plot residual errors\n",
        "residuals = DataFrame(model_fit.resid)\n",
        "residuals.plot()\n",
        "pyplot.show()\n",
        "residuals.plot(kind='kde')\n",
        "pyplot.show()\n",
        "print(residuals.describe())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             ARIMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                D.Sales   No. Observations:                   35\n",
            "Model:                 ARIMA(5, 1, 0)   Log Likelihood                -196.170\n",
            "Method:                       css-mle   S.D. of innovations             64.241\n",
            "Date:                Thu, 24 Jun 2021   AIC                            406.340\n",
            "Time:                        15:52:24   BIC                            417.227\n",
            "Sample:                             1   HQIC                           410.098\n",
            "                                                                              \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const            12.0649      3.652      3.304      0.003       4.908      19.222\n",
            "ar.L1.D.Sales    -1.1082      0.183     -6.063      0.000      -1.466      -0.750\n",
            "ar.L2.D.Sales    -0.6203      0.282     -2.203      0.036      -1.172      -0.068\n",
            "ar.L3.D.Sales    -0.3606      0.295     -1.222      0.231      -0.939       0.218\n",
            "ar.L4.D.Sales    -0.1252      0.280     -0.447      0.658      -0.674       0.424\n",
            "ar.L5.D.Sales     0.1289      0.191      0.673      0.506      -0.246       0.504\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1           -1.0617           -0.5064j            1.1763           -0.4292\n",
            "AR.2           -1.0617           +0.5064j            1.1763            0.4292\n",
            "AR.3            0.0816           -1.3804j            1.3828           -0.2406\n",
            "AR.4            0.0816           +1.3804j            1.3828            0.2406\n",
            "AR.5            2.9315           -0.0000j            2.9315           -0.0000\n",
            "-----------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                0\n",
            "count   35.000000\n",
            "mean    -5.495223\n",
            "std     68.132882\n",
            "min   -133.296638\n",
            "25%    -42.477902\n",
            "50%     -7.186558\n",
            "75%     24.748322\n",
            "max    133.237947\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "z-Wnfls_uWtY",
        "outputId": "b64a5b5b-008e-4fc2-c982-66b5a30f6660"
      },
      "source": [
        "from pandas import read_csv\n",
        "from pandas import datetime\n",
        "from matplotlib import pyplot\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "X = sales\n",
        "size = int(len(X) * 0.66)\n",
        "train, test = X[0:size], X[size:len(X)]\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for t in range(len(test)):\n",
        "\tmodel = ARIMA(history, order=(5,1,0))\n",
        "\tmodel_fit = model.fit(disp=0)\n",
        "\toutput = model_fit.forecast()\n",
        "\tyhat = output[0]\n",
        "\tpredictions.append(yhat)\n",
        "\tobs = test[t]\n",
        "\thistory.append(obs)\n",
        "\tprint('predicted=%f, expected=%f' % (yhat, obs))\n",
        "error = mean_squared_error(test, predictions)\n",
        "print('Test MSE: %.3f' % error)\n",
        "# plot\n",
        "pyplot.plot(test)\n",
        "pyplot.plot(predictions, color='red')\n",
        "pyplot.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-700b3060250e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0mmodel_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_fit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforecast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, endog, order, exog, dates, freq, missing)\u001b[0m\n\u001b[1;32m    986\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mARIMA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 988\u001b[0;31m             \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    989\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, order, exog, dates, freq, missing)\u001b[0m\n\u001b[1;32m   1005\u001b[0m             \u001b[0;31m# in the predict method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"d > 2 is not supported\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mARIMA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_diff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_unintegrate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munintegrate_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, order, exog, dates, freq, missing)\u001b[0m\n\u001b[1;32m    426\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'endog must be 1-d or 2-d with 1 column'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m         \u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m  \u001b[0;31m# get it after it's gone through processing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0m_check_estimable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_ar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_ar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mk_ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m_check_estimable\u001b[0;34m(nobs, n_params)\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_estimable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnobs\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mn_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Insufficient degrees of freedom to estimate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Insufficient degrees of freedom to estimate"
          ]
        }
      ]
    }
  ]
}