21.1: kats_basics.ipynb

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      "source": [
        "Github repo : https://github.com/facebookresearch/Kats/tree/master/tutorials"
      ]
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    {
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      "source": [
        "# Kats 101 - Basics\n",
        "\n",
        "Kats (**K**its to **A**nalyze **T**ime **S**eries) is a light-weight, easy-to-use, extenable, and generalizable framework to perform time series analysis in Python.  Time series analysis is an essential component of data science and engineering work.  Kats aims to provide a one-stop shop for techniques for univariate and multivariate time series including:\n",
        "\n",
        "1. Forecasting  \n",
        "2. Anomaly and Change Point Detection  \n",
        "3. Feature Extraction \n",
        "\n",
        "\n",
        "and after introducing the basic Kats data structure, this Kats 101 notebook provides a basic introduction to each of these time series techniques in Kats.  The complete table of contents for Kats 101 is as follows: \n",
        "\n",
        "1. Kats Basics          \n",
        "    1.1 Initiate `TimeSeriesData` Object         \n",
        "    1.2 `TimeSeriesData` built-in operations         \n",
        "2. Forecasting with Kats         \n",
        "    2.1 An example with Prophet model         \n",
        "    2.2 An example with Theta model         \n",
        "3. Detection with Kats         \n",
        "    3.1 What are the algorithms?         \n",
        "    3.2 An example with outlier detection method         \n",
        "    3.3 An example with CUSUM algorithm         \n",
        "4. Feature extraction with Kats         \n",
        "5. Summary         "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDSHbGXDzF87"
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      "source": [
        "**Note:** We provide two types of tutorial notebooks\n",
        "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
        "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# 1. Kats Basics\n",
        "`TimeSeriesData` is the basic data structure in Kats to represented univariate and multivariate time series.  There are two ways to initiate it, henceforth referred to as \"Method 1\" and \"Method 2\":\n",
        "\n",
        "1) `TimeSeriesData(df)`, where `df` is a `pd.DataFrame` object with a \"time\" column and any number of value columns.\n",
        "\n",
        "2) `TimeSeriesData(time, value)`, where `time` is either a `pd.Series` or `pd.DatetimeIndex` object and `value` is either a `pd.Series` (for univariate) or a `pd.DataFrame` (for multivariate)\n",
        "\n",
        "## 1.1 Initiate `TimeSeriesData` object\n",
        "We will use the `air_passenger` and `multi_ts` datasets to demonstrate how to create a `TimeSeriesData` object for univariate and multivariate time series, respectively."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvCr6vwh2H4o"
      },
      "source": [
        "!pip install kats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip94ilcMzF8-"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime, timedelta\n",
        "import matplotlib.pyplot as plt\n",
        "import sys\n",
        "sys.path.append(\"../\")\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "from kats.consts import TimeSeriesData"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "air_passengers_df = pd.read_csv(\"https://raw.githubusercontent.com/gulabpatel/TimeSeries/master/Dataset/air_passengers.csv\")\n",
        "\n",
        "\"\"\"\n",
        "Note: If the column holding the time values is not called \"time\", you will want to specify \n",
        "the name of this column using the time_col_name parameter in the TimeSeriesData constructor.\n",
        "\"\"\"\n",
        "air_passengers_df.columns = [\"time\", \"value\"]\n",
        "air_passengers_df.head()"
      ],
      "execution_count": null,
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              "      <td>1949-01-01</td>\n",
              "      <td>112</td>\n",
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              "      <td>118</td>\n",
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              "      <td>1949-03-01</td>\n",
              "      <td>132</td>\n",
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              "      <th>3</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>129</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05-01</td>\n",
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              "         time  value\n",
              "0  1949-01-01    112\n",
              "1  1949-02-01    118\n",
              "2  1949-03-01    132\n",
              "3  1949-04-01    129\n",
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      "source": [
        "multi_ts_df = pd.read_csv(\"https://raw.githubusercontent.com/gulabpatel/TimeSeries/master/Dataset/multi_ts.csv\", index_col=0)\n",
        "multi_ts_df.columns = [\"time\", \"v1\", \"v2\"]\n",
        "multi_ts_df.head()"
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>v1</th>\n",
              "      <th>v2</th>\n",
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              "      <th>2</th>\n",
              "      <td>2017-03-14</td>\n",
              "      <td>0.178</td>\n",
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              "      <th>3</th>\n",
              "      <td>2017-03-15</td>\n",
              "      <td>0.339</td>\n",
              "      <td>53.5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-03-16</td>\n",
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              "         time     v1    v2\n",
              "0  2017-03-12 -0.109  53.8\n",
              "1  2017-03-13  0.000  53.6\n",
              "2  2017-03-14  0.178  53.5\n",
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      "source": [
        "Here we construct `TimeSeriesData` objects for each time series using Method 1."
      ]
    },
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        "id": "ItsDSozRzF9W"
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      "source": [
        "air_passengers_ts = TimeSeriesData(air_passengers_df)\n",
        "multi_ts = TimeSeriesData(multi_ts_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "3de7a2d7-1feb-4ff4-e6bb-99aca85f2e40"
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      "source": [
        "# check that the type of the data is a \"TimeSeriesData\" object for both cases\n",
        "print(type(air_passengers_ts))\n",
        "print(type(multi_ts))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'kats.consts.TimeSeriesData'>\n",
            "<class 'kats.consts.TimeSeriesData'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "source": [
        "# For the air_passengers TimeSeriesData, check that both time and value are pd.Series\n",
        "print(type(air_passengers_ts.time))\n",
        "print(type(air_passengers_ts.value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "277d42ce-00d9-463b-ba05-b539f41b7a01"
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      "source": [
        "# For the multi_ts TimeSeriesData, time is a pd.Series and value is a pd.DataFrame\n",
        "print(type(multi_ts.time))\n",
        "print(type(multi_ts.value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        }
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      "source": [
        "Now, we show how to construct the same `TimeSeriesData` objects as before using Method 2."
      ]
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      "source": [
        "air_passengers_ts_from_series = TimeSeriesData(time=air_passengers_df.time, value=air_passengers_df.value)\n",
        "multi_ts_from_series = TimeSeriesData(time=multi_ts_df.time, value=multi_ts_df[['v1', 'v2']])"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
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      "source": [
        "`TimeSeriesData` can accomodate time expressed as a variety of different types, including \n",
        "- standard `datetime`, \n",
        "- `pandas.Timestamp`,\n",
        "- a `str` (if in a non-standard format or if efficiency is important, use the optional `date_format` argument),\n",
        "- `int` (i.e. unix time).\n",
        "\n",
        "Here is an example of how to construct a `TimeSeriesData` object when time is provided in unix time format.\n",
        "\n",
        "\n",
        "Here's an example where the time is auto-interpreted from a unix time format. Using this format just requires a couple optional parameters in the `TimeSeriesData` constructor:\n",
        "- `use_unix_time = True`\n",
        "- `unix_time_units=\"s\"` (the default is `\"ns\"`, indicating nanoseconds)"
      ]
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      "source": [
        "from dateutil import parser\n",
        "from datetime import datetime\n",
        "\n",
        "# Convert time from air_passengers data to unix time\n",
        "air_passengers_ts_unixtime = air_passengers_df.time.apply(\n",
        "    lambda x: datetime.timestamp(parser.parse(x))\n",
        ")\n",
        "\n",
        "air_passengers_ts_unixtime"
      ],
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              "0     -662688000.0\n",
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              "4     -652320000.0\n",
              "          ...     \n",
              "139   -297216000.0\n",
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      "source": [
        "# Create the TimeSeriesData object with the extra arguments to accomodate unix time \n",
        "ts_from_unixtime = TimeSeriesData(\n",
        "        time=air_passengers_ts_unixtime, \n",
        "        value=air_passengers_df.value, \n",
        "        use_unix_time=True, \n",
        "        unix_time_units=\"s\"\n",
        ")\n",
        "\n",
        "ts_from_unixtime"
      ],
      "execution_count": null,
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              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-01-01</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-02-01</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-03-01</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05-01</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>1960-08-01</td>\n",
              "      <td>606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>1960-09-01</td>\n",
              "      <td>508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1960-10-01</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>1960-11-01</td>\n",
              "      <td>390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>1960-12-01</td>\n",
              "      <td>432</td>\n",
              "    </tr>\n",
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              "<p>144 rows × 2 columns</p>\n",
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              "          time  value\n",
              "0   1949-01-01    112\n",
              "1   1949-02-01    118\n",
              "2   1949-03-01    132\n",
              "3   1949-04-01    129\n",
              "4   1949-05-01    121\n",
              "..         ...    ...\n",
              "139 1960-08-01    606\n",
              "140 1960-09-01    508\n",
              "141 1960-10-01    461\n",
              "142 1960-11-01    390\n",
              "143 1960-12-01    432\n",
              "\n",
              "[144 rows x 2 columns]"
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    {
      "cell_type": "markdown",
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      "source": [
        "## 1.2 `TimeSeriesData` built-in operations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_THfcj0zF9r"
      },
      "source": [
        "The `TimeSeriesData` object supports many of the same operations of the standard `pandas.DataFrame`, including:\n",
        "- Slicing\n",
        "- Math Operations\n",
        "- Extend \n",
        "- Plotting\n",
        "- Utility Functions (`to_dataframe`, `to_array`, `is_empty`, `is_univariate`)\n",
        "\n",
        "We give examples of each as follows."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caAEUqiMzF9r"
      },
      "source": [
        "## Slicing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvdEkfHJzF9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2fc6342a-5495-47e4-94a3-808211c3f1da"
      },
      "source": [
        "air_passengers_ts[1:5]"
      ],
      "execution_count": null,
      "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>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-02-01</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-03-01</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-05-01</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        time  value\n",
              "0 1949-02-01    118\n",
              "1 1949-03-01    132\n",
              "2 1949-04-01    129\n",
              "3 1949-05-01    121"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hccwyGW0zF9t"
      },
      "source": [
        "## Math operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xKP3My9zF9t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2418c5ea-1721-44fd-9422-72a4bf318138"
      },
      "source": [
        "air_passengers_ts[1:5] + air_passengers_ts[1:5]"
      ],
      "execution_count": null,
      "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>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-02-01</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-03-01</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-05-01</td>\n",
              "      <td>242</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        time  value\n",
              "0 1949-02-01    236\n",
              "1 1949-03-01    264\n",
              "2 1949-04-01    258\n",
              "3 1949-05-01    242"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVNkvmTCzF9u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "485d0560-f075-4598-b73e-8f4cbafcfb6f"
      },
      "source": [
        "# Equality and Inequality are also supported:\n",
        "\n",
        "air_passengers_ts == air_passengers_ts_from_series"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZmWcKRQzF9v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a88579f-2250-4c89-9806-34aaf0db7475"
      },
      "source": [
        "multi_ts == multi_ts_from_series"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FKTuCr0zF-S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dee9bacd-4355-4c15-c84e-b24b15a48f3d"
      },
      "source": [
        "# length\n",
        "\n",
        "len(air_passengers_ts)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "144"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7JNQZtfzF-T"
      },
      "source": [
        "## Extend"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgh-zzlpzF-U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "a1b85546-83dc-4beb-a6d4-00fbcf321ad9"
      },
      "source": [
        "# Creating two slices\n",
        "ts_1 = air_passengers_ts[0:3]\n",
        "ts_2 = air_passengers_ts[3:7]\n",
        "\n",
        "ts_1.extend(ts_2)\n",
        "ts_1"
      ],
      "execution_count": null,
      "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>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-01-01</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-02-01</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-03-01</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05-01</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1949-06-01</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1949-07-01</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        time  value\n",
              "0 1949-01-01    112\n",
              "1 1949-02-01    118\n",
              "2 1949-03-01    132\n",
              "3 1949-04-01    129\n",
              "4 1949-05-01    121\n",
              "5 1949-06-01    135\n",
              "6 1949-07-01    148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZ2oH3B-zF-2"
      },
      "source": [
        "## Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4NaJaojzGLV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "47624252-1c24-498d-a4eb-9f3ca6018d90"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# Must pass the name of the value columns to plot\n",
        "air_passengers_ts.plot(cols=['value'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLbcvAIBzGLW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "59d3ab70-2a74-48c7-cd42-9b1b815dbbf2"
      },
      "source": [
        "# We can plot multiple time series from multi_ts by passing in the name of each value column we want to plot\n",
        "multi_ts.plot(cols=['v1','v2'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "En2EsuIrzGLW"
      },
      "source": [
        "## Utility functions\n",
        "\n",
        "Here we provide examples of a few useful Kats utility functions for `TimeSeriesData`.  They can be helpful for working with external libraries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rr7YqZ9VzGLi"
      },
      "source": [
        "### Convert to `pandas.DataFrame`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PFmRioazGLi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5cfb1437-fdd4-4676-b789-80a2e6b906b8"
      },
      "source": [
        "air_passengers_ts.to_dataframe().head()"
      ],
      "execution_count": null,
      "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>time</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949-01-01</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949-02-01</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949-03-01</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949-04-01</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05-01</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        time  value\n",
              "0 1949-01-01    112\n",
              "1 1949-02-01    118\n",
              "2 1949-03-01    132\n",
              "3 1949-04-01    129\n",
              "4 1949-05-01    121"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBf74oOuzGLj"
      },
      "source": [
        "### Convert to `numpy.ndarray`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-WnbxX2zGLj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bf7cb2c-69c4-4d9c-9914-ca2645e22bf1"
      },
      "source": [
        "air_passengers_ts.to_array()[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[Timestamp('1949-01-01 00:00:00'), 112],\n",
              "       [Timestamp('1949-02-01 00:00:00'), 118],\n",
              "       [Timestamp('1949-03-01 00:00:00'), 132],\n",
              "       [Timestamp('1949-04-01 00:00:00'), 129],\n",
              "       [Timestamp('1949-05-01 00:00:00'), 121]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tW89_ZztzGLk"
      },
      "source": [
        "### Check basic characteristics of the time series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "goOBJfQczGLk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "511467a9-4f94-4b78-f922-a10cd3d219d2"
      },
      "source": [
        "air_passengers_ts.is_empty()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPBv4zWZzGLl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3dd19b9-564d-41c1-bb66-46fbde921d3a"
      },
      "source": [
        "air_passengers_ts.is_univariate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCfI4gC4zGLl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9780fec2-8993-404b-fe89-1027ead39e42"
      },
      "source": [
        "multi_ts.is_univariate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRQC3_mCzGLm"
      },
      "source": [
        "# 2. Forecasting with Kats\n",
        "\n",
        "We currently support the following 10 base forecasting models: \n",
        "\n",
        "1. Linear  \n",
        "2. Quadratic   \n",
        "3. ARIMA   \n",
        "4. SARIMA   \n",
        "5. Holt-Winters   \n",
        "6. Prophet   \n",
        "7. AR-Net   \n",
        "8. LTSM   \n",
        "9. Theta   \n",
        "10. VAR   \n",
        "\n",
        "\n",
        "Each models follows the `sklearn` model API pattern:  we create an instance of the model class and then call its `fit` and `predict` methods.  In this section, we provide examples for the Prophet and Theta models.  A more in-depth introduction to forecasting in Kats is provided in the Kats 201 tutorial.\n",
        "\n",
        "\n",
        "\n",
        "## 2.1 An example with Prophet model\n",
        "\n",
        "We will demonstrate how to use Prophet model to forecast with the `air_passengers` data set.      "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpdE2Hy_zGLn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4631f5d-d93a-4f91-c15c-9b1b43264420"
      },
      "source": [
        "# import the param and model classes for Prophet model\n",
        "from kats.models.prophet import ProphetModel, ProphetParams\n",
        "\n",
        "# create a model param instance\n",
        "params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
        "\n",
        "# create a prophet model instance\n",
        "m = ProphetModel(air_passengers_ts, params)\n",
        "\n",
        "# fit model simply by calling m.fit()\n",
        "m.fit()\n",
        "\n",
        "# make prediction for next 30 month\n",
        "fcst = m.predict(steps=30, freq=\"MS\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulY6Pa6NzGLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f793276e-b707-40ae-f81e-e69d0fb98824"
      },
      "source": [
        "# the predict method returns a dataframe as follows\n",
        "fcst.head()"
      ],
      "execution_count": null,
      "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>time</th>\n",
              "      <th>fcst</th>\n",
              "      <th>fcst_lower</th>\n",
              "      <th>fcst_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1961-01-01</td>\n",
              "      <td>451.765190</td>\n",
              "      <td>438.010922</td>\n",
              "      <td>465.004999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1961-02-01</td>\n",
              "      <td>433.212698</td>\n",
              "      <td>420.821520</td>\n",
              "      <td>446.746621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1961-03-01</td>\n",
              "      <td>492.254622</td>\n",
              "      <td>478.752069</td>\n",
              "      <td>504.607162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1961-04-01</td>\n",
              "      <td>495.496541</td>\n",
              "      <td>481.344749</td>\n",
              "      <td>508.438582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1961-05-01</td>\n",
              "      <td>504.186375</td>\n",
              "      <td>490.823101</td>\n",
              "      <td>517.731891</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        time        fcst  fcst_lower  fcst_upper\n",
              "0 1961-01-01  451.765190  438.010922  465.004999\n",
              "1 1961-02-01  433.212698  420.821520  446.746621\n",
              "2 1961-03-01  492.254622  478.752069  504.607162\n",
              "3 1961-04-01  495.496541  481.344749  508.438582\n",
              "4 1961-05-01  504.186375  490.823101  517.731891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OErxhQB-zGLs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "021da4ce-2b13-4e78-ffde-f06756ca76fc"
      },
      "source": [
        "# visualize the results with uncertainty intervals\n",
        "m.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3l_1omLfzGLt"
      },
      "source": [
        "## 2.2 An example with Theta model\n",
        "\n",
        "\n",
        "We will now use the Theta model to forecast with the `air_passengers` data set.  \n",
        "\n",
        "The Theta Method (Assimakopoulos and Nikolopoulos, 2000) is a univariate forecasting method that fits two Theta lines: 1) a linear interpolation (called the `Theta=0`) and 2) a second-order difference (called the `Theta=2` line), and then combines them to build a forecast.  Prior to running this forecast, we test the time series for seasonality, deseaonalize if seasonality is detected, and then reseasonalize the calculated forecasts.  \n",
        "\n",
        "Hyndman and Billah (2003) showed that the Theta Method is equivalent to simple exponential smoothing with drift.  In Kats we use this underlying model to calculate prediction intervals for `ThetaModel`.\n",
        "\n",
        "Our implementation of `ThetaModel` in Kats is similar to the [thetaf function in R](https://pkg.robjhyndman.com/forecast/reference/thetaf.html).  Because each of our time series models follow the `sklearn` model API pattern, the code using `ThetaModel` is quite similar to the example above using \n",
        "`ProphetModel`: we initialize the model with its parameters and then call the `fit` and `predict` methods.  We can then use the `plot` method to visualize our forecast."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w19cZsuGzGLu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "d51a6cad-6eb5-4120-9399-18ca7defe90c"
      },
      "source": [
        "# import param and model from `kats.models.theta`\n",
        "from kats.models.theta import ThetaModel, ThetaParams\n",
        "\n",
        "# create ThetaParam with specifying seasonality param value\n",
        "params = ThetaParams(m=12)\n",
        "\n",
        "# create ThetaModel with given data and parameter class\n",
        "m = ThetaModel(data=air_passengers_ts, params=params)\n",
        "\n",
        "# call fit method to fit model\n",
        "m.fit()\n",
        "\n",
        "# call predict method to predict the next 30 steps\n",
        "res = m.predict(steps=30, alpha=0.2)\n",
        "\n",
        "# visualize the results\n",
        "m.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1g5tcQNzGLv"
      },
      "source": [
        "# 3. Detection with Kats\n",
        "\n",
        "Kats provides a set of models and algorithms to detect outliers, change points, and trend changes in time series data.\n",
        "\n",
        "\n",
        "## 3.1 What are the algorithms?\n",
        "\n",
        "To detect a specific pattern, we provided different algorithms, which is summarized as follows.\n",
        "- **Outlier Detection**. This usually refers to a abnormal spike in a time series data, which can be detected with `OutlierDetector`\n",
        "- **Change Point Detection**. This refers to a sudden change that the time series have different statistical properties before and after the change. We provided three major algorithms to detect such patterns:\n",
        "    - CUSUM Detection\n",
        "    - Bayesian Online Change Point Detection (BOCPD)\n",
        "    - Stat Sig Detection\n",
        "- **Trend Change Detection**. This refers to a slow trend change on the time series data, which can be detected with Mann-Kendall detection algorithm, `MKDetector`\n",
        "\n",
        "In this tutorial, we will demonstrate the usage of two detectors: `OutlierDetector` and `CUSUM`.  A more in-depth introduction to detection in Kats is provided in the Kats 202 tutorial.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjI0zaTGzGLx"
      },
      "source": [
        "## 3.2 An example with outlier detection method\n",
        "\n",
        "We provide the `OutlierDetector` module to detect outliers in time series.  Since outliers can cause so many problems in downstream processing, it is important to be able to detect them.  `OutlierDetector` also provides functionality to handle or remove outliers once they are found.\n",
        "\n",
        "Our outlier detection algorithm works as follows:\n",
        "\n",
        "- We do a [seasonal decomposition](https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) of the input time series with additive or multiplicative decomposition as specified (default is additive)\n",
        "- We generate a residual time series by either removing only trend or both trend and seasonality if the seasonality is strong.\n",
        "- We detect points in the residual which are outside 3 times the inter quartile range.  This multiplier can be tuned using the `iqr_mult` parameter in `OutlierDetector`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PfIX3tunzGLy"
      },
      "source": [
        "Our example below copies the `air_passengers` data set and manually inserts outliers into it. We then use `OutlierDetector` to find them. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-oe3YYDzGLy"
      },
      "source": [
        "# deep copy the air_passenger_df \n",
        "air_passengers_outlier_df = air_passengers_df.copy(deep=True)\n",
        "\n",
        "# manually add outlier on the date of '1950-12-01'\n",
        "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1950-12-01','value']*=5\n",
        "# manually add outlier on the date of '1959-12-01'\n",
        "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1959-12-01', 'value']*=4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03txvB5czGLz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "6e3ede44-ead4-4f7a-eace-28ef60576306"
      },
      "source": [
        "# visualize the raw data\n",
        "air_passengers_outlier_df.plot(x='time', y='value', figsize=(15,8))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHf00MSMzGL0"
      },
      "source": [
        "# transform the outlier data into `TimeSeriesData` Object\n",
        "air_passengers_outlier_ts = TimeSeriesData(air_passengers_outlier_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CjR27PnzGL1"
      },
      "source": [
        "from kats.detectors.outlier import OutlierDetector\n",
        "\n",
        "ts_outlierDetection = OutlierDetector(air_passengers_outlier_ts, 'additive') # call OutlierDetector\n",
        "ts_outlierDetection.detector() # apply OutlierDetector"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CtZp0rbzGL3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32602f71-f2ef-4c89-d189-6cb9dfcc45e2"
      },
      "source": [
        "ts_outlierDetection.outliers[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Timestamp('1950-12-01 00:00:00'),\n",
              " Timestamp('1959-11-01 00:00:00'),\n",
              " Timestamp('1959-12-01 00:00:00')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLKakr-uzGL3"
      },
      "source": [
        "After detecting the outlier, we can now easily removal them from the data. Here we will explore two options: \n",
        "- **No Interpolation**: outlier data points will be replaced with **NaN** values\n",
        "- **With Interpolation**: outlier data points will be replaced with **linear interploation** values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ftZjSYOzGL4"
      },
      "source": [
        "air_passengers_ts_outliers_removed = ts_outlierDetection.remover(interpolate = False) # No interpolation\n",
        "air_passengers_ts_outliers_interpolated = ts_outlierDetection.remover(interpolate = True) # With interpolation"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fF1NgcezGL4"
      },
      "source": [
        "Here we visualize the difference between these two approaches to removing outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-Ll4DSxzGL5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "c019e06e-38fe-48a6-80c0-7eb76390e9b9"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20,8), nrows=1, ncols=2)\n",
        "\n",
        "air_passengers_ts_outliers_removed.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[0])\n",
        "ax[0].set_title(\"Outliers Removed : No interpolation\")\n",
        "air_passengers_ts_outliers_interpolated.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[1])\n",
        "ax[1].set_title(\"Outliers Removed : With interpolation\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5BSfTIQzGL6"
      },
      "source": [
        "## 3.3 An example with CUSUM algorithm\n",
        "\n",
        "Cusum is a method to detect an up/down shift of means in a time series. Our implementation has two main steps:\n",
        "\n",
        "1.  **Locate the change point:** This is an iterative process where we initialize a change point (in the middle of the time series) and CUSUM time series based on this change point.  The next changepoint is the location where the previous CUSUM time series is maximized (or minimized).  This iteration continues until either 1) a stable changepoint is found or 2) we exceed the limit number of iterations.\n",
        "\n",
        "2.  **Test the change point for statistical significance:** Conduct log likelihood ratio test to test if the mean of the time series changes at the changepoint calculated in Step 1.  The null hypothesis is that there is no change in mean.\n",
        "\n",
        "By default, we report a detected changepoint if and only if we reject the null hypothesis in Step 2.  \n",
        "\n",
        "Here are a few additional points worth mentioning:\n",
        "- We assume there is at most one increase change point and at most one decrease change point.  You can use the `change_directions` argument in the detector to specify whether you are looking an increase, a decrease, or both (default is both).\n",
        "- We use Gaussian distribution as the underlying model to calculate the CUSUM time series value and conduct the hypothesis test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYOUQEzfzGL7"
      },
      "source": [
        "# import packages\n",
        "from kats.consts import TimeSeriesData, TimeSeriesIterator\n",
        "from kats.detectors.cusum_detection import CUSUMDetector\n",
        "\n",
        "# synthesize data with simulation\n",
        "np.random.seed(10)\n",
        "df = pd.DataFrame(\n",
        "    {\n",
        "        'time': pd.date_range('2019-01-01', '2019-03-01'),\n",
        "        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),\n",
        "        'decrease':np.concatenate([np.random.normal(1,0.3,50), np.random.normal(0.5,0.3,10)]),\n",
        "    }\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-eo8p0MzGL8"
      },
      "source": [
        "It is straightforward to use `CUSUMDetector` to detect an increase or a decrease when there is one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6uHHiLxzGL8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "af4e9e1d-8335-4a70-ee16-5e1f7a4f3125"
      },
      "source": [
        "# detect increase\n",
        "timeseries = TimeSeriesData(\n",
        "    df.loc[:,['time','increase']]\n",
        ")\n",
        "detector = CUSUMDetector(timeseries)\n",
        "\n",
        "# run detector\n",
        "change_points = detector.detector(change_directions=[\"increase\"])\n",
        "\n",
        "# plot the results\n",
        "plt.xticks(rotation=45)\n",
        "detector.plot(change_points)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eubGC3mE3u0P46w809rKsMMP/GU/HXZcsJdkhrLE/u3DZsrwAl9sbly2tVRkoyhzyl4/s5QevnQ66r75riOqCyfECH3dsqeapT17BF25eT3JARayI8PbzyvnDBFdRbfsgyQ6ZpGCq8q3HE2sNjrb0k+QQ/wCWzUvzSXJIzFxFxhge3dVAzdL8KSe93byxgvquoWk7u/5ibyPff/U0f3zZcv75XRtIdgjPH2kLuvZAUy/rw3AR+bhgSR4H/uH6sNxKgVy0zGpaF4/1BtMqAxFZIiIviMghETkoIp8IskZE5OsickJE3hSRzQH7PiQix+3bh6J9AIoSr5xo6+fx3Y18/XfHGR0b7xboc7npGhyd0jJIcghryoK7LW7yuYoOnXUVnWwfoLogY1Irhdz0FLLTkie5iY609LO8KJO05CTAcn9sqMiJmTLY19DL8bYB3nthcKvAx/XrykhJmtpVdKy1n88+tp+apfl87qY1ZDtT2LK8gOePtE5a2zvk5kzXMBsqIruwO1OSIloPVluL5UWZvHEqPrK2AgnHMhgDPmWMWQdcAtwnIusmrLkRWGXf7gW+BSAiBcAXgIuBLcAXRCQ/SrIrSlzz5B7rYtUxMMqzh8ZfhOondCuNlPOrcqnMS2dbgKuotn3QX3k8kcr89EmFZ0db+/wuIh9blhew90zPlJk3c8Wju87gTHFw0/nlU67LzUjhrecW85v9zUG7sfa73Hz0R7vITEvmG3dt9ivHq9eUcKx1gDNd4z+HA3b67nkR/sqfKRcty2dnXVfcdZKdVhkYY5qNMbvt+/3AYaBywrJbgB8Yi+1AnoiUA9cDzxpjuowx3cCzwA1RPQJFiUOMMTy5t5HLzimkMi+dn7xRN26/L610pkPSRYSbzivjDyc66B1y4/EaTnUOhnSvVOWPrzUYGBnjTNcwa0onKoNCRj3eWQ3XmQkut4df7m3ihvVlYfntb95YQXOvi10T+jd1D47yFz/bR13nEPffuYnSgGyfq9eUAFZn0kB8/vv1YQSPo8FFywroGXJzYo6K52ZKRDEDEVkGbAJen7CrEgic/NBgbwu1Pdhr3ysiO0VkZ3t7eyRiKUrcsauum4buYd6zuYrbL1rCH050crpj0L+/rmty6+pIuem8ctwew7OHW2nqGWZ0zMvKUJZBXvq4mMExOzXz3AmWwUXLLMN9vusNnjvcSp9rjPdeuCSs9desLcWZ4vC7iroGR/n3p45w+b89z3OHW/nbm9ZyyYrCcc9ZUZzF8qLMSXGDA019VOalB81emgviNW4QtjIQkSzgMeCTxpi+aAtijHnAGFNjjKkpLi6O9ssryrzyxJ5G0lOSuH59GbddtIQkh/DwG/X+/fWdQxRmpvoLqmbCBUvy/K6iEyEyiXxU5WfQPzJG77AbgGN2JtHEbJi8jFTWlGXz+jzHDR7d1UBFrpNLVxZOvxjITEvmbWtK2ba/mX+zlcC3XjzJ1jUlPP3JK/njy5cHfd5Vq0t49WTnuBnTBxt7QzanmwuWFmZQnJ3G74+2x9Vc5LCUgYikYCmCHxtjHg+ypBEIVOlV9rZQ2xUlYRkd8/Kb/c1ct76UzLRkSnOcXLO2hJ/vavD3FJourTQcRIQbN5Tx8vF29tlN7SbWGPg4m15quaeOtPSTkZrEkvzJMmxZXsCuuu6QOfnRpqXXxUvH2nn35iqSAlp5T8fNG8vpGBjl2y+e5GpbCXzjzs2T4iCBvG1tCaNjXl49YVk+/S43tR2DEQePZ4OI8L4Lq3j2UCv/9dzxeXvf6Qgnm0iA7wKHjTFfC7Hsl8AH7ayiS4BeY0wz8DRwnYjk24Hj6+xtipKwvHisnZ4hN7decNYjeufFS+kaHOVpu1AsWLfSmXDT+Zar6Aev1ZGbnkJBCFdH1YRW1kdb+llVmj1ujoKPLcsLGBr1+AuxosW2/c08suPMpIIrf23BNFlEE7lmbSn/eMt6nvnkldx/52bOLZ0+5/+iZQVkpSXzO9tVdMg+xkhTRGfLX123mttqqvjv3x3n/ufjQyGEY6NeBtwN7BeRvfa2vwGqAYwx3wa2ATcBJ4Ah4MP2vi4R+RKww37ePxpj4stRpigR4vUafr2/mWvWlgTtcPnknkYKM1O5fFWRf9sV5xSxpCCdn7xex/XrS2nqHQ7aoC5SNi3JoyLXSVOvi83Vef4xjRPxtbxosHsUHW3t59q1pUHXbll2doD7RnvQzmzpHXLzl4/sxeX2cv8LJ/jE21Zx66ZKHGJlEdUszWd5CKsmFMlJDj546bKInpOa7OCKVUW8cKTNmmxmK4P18+gmAmsU5r+8+3zGPIavPnOM5CQHH33rynmVYZJM0y0wxrxijBFjzPnGmAvs2zZjzLdtRYCdRXSfMWalMeY8Y8zOgOc/ZIw5x759by4PRlHmg4NNffz5w3v4+E/2THKl9LncPHu4lZs3VozL93c4hNsvqmZ7bRcvHevAGGbtJgLbVWS3RpiqUKsgM5X0lCQae4ZpHxiha3A0pDulJMfJ8qLMqMYNHtvdgMvt5e/fsY5sZzKf+vk+rvvPF/mf509wsn1w2tqCaHL1mhJa+lwcau7jYGMvpTlplGSH32MoWiQ5hK+8byM3b6zgX397hAdfrp13GQLRCmRFiZBuu+r3+SNtfPFXB8cFAZ/a38LomJdbN01OmntfTRXJDuErTx8BoqMMwMoqgrMDbYIhIv5ag6MhgseBbFlWwI7TXXiikAtvjOFH2+vYVJ3HRy5fzq8+fjnfumszDhG+9uwxnCkO3j5NbUE02braSjF9/nAbB5p65zVeMJEkh/Cft23kxg1l/NNvDvN/r56OmSyqDBQlQvrtMZHXrC3hR9vrefDlU/59T+5tZHlRJhurJl9gSrKdXLe+lGOtVubPVK0oImFzdR7/+u7zeF/N1L+ufUNujk7oSRSMrauL6R128+Kx4O0bIuHVk53Udgxy9yVLActKuvG8cp765JV8/Y5N/Nf7N5EdZk+gaFCcncbGJXn8Zn8zJ9oGImpDMRckJzn4+h2buHZdKf/74smYNQqceV6boixS+l1WeuYX37metOQk/nnbYSrz09lUncdrtZ188m3nhvTd37llKdv2t5CRmkRRVnTy2kWE27dUT7uuMi+dvWd6ONLST1FWGoVZaSHXXrOulOLsNH74Wh1XrwkeWwiXH75WR35Git+C8ZHkEN65sWJWrz1Trl5dwn8+dwxg2oE280FKkoP779xE58DorNKNZ4NaBooSIT7LICc9hf+4bSMXLs3nL362ly9vO4IxcMsFoS9wb1lZyNLCDJYVZoZUGHNFVX4GPUNudtd3T9ttMyXJwR1bqvn9sXZ/64xg9Lvc/NXP97E/RMVyS6+LZw+3cttFS2bUy2eueNvaEv/9+c4kCkVachIVEbbEjiaqDBQlQvpdbkQgKzUZZ0oS3/lgDeW5Tn61r4lN1XksmyIrxuEQvv2BC/m395w/jxJb+GoNatsHp3QR+bhzSzUOEX78el3INd9+8SSP7mrg3h/upHNgZNL+h9+ox2sMd21ZOnPB54D1FTmUZKdRkJka0ejKREaVgaJESJ9rjKy0ZH+OfkFmKt/78BZWFGdyz+Urpn3+2vIczgsSU5hrfLUGMHW8wEdZrpPr1pXys51ngjaua+l18d1XTnHRsnw6B0f5xE/3jgs4uz1eHn6jnq3nFs+4B9NcISL82dtWcc8Vy+fdQotXVBkoSoT0udyTmqktL8rk+U9tndesmEipCnBBhDuU5e5LltIz5ObXb04eQv9fzx3D4zV87bYL+NIt63nlRAf/+ewx//5nD7XS1j/CBy6JL6vAx92XLOVPt54TazHiBlUGihIh/a4xsp0LL/eiKCuN1GQHIrCqJDxlcOnKQlYWZ/LD7eNdRcdb+3lk5xk+cMlSlhRk8P6Lqrmtpor7XzjBc3a77h9tr6MyL92fyqnEN6oMFCVC+l3uBakMHA6hMi+dZYWZpKeGF8wVEe6+ZCn7zvTwZsPZkZH/9tRRMlOT+bOrV/m3/eMtG1hfkcNfPLKX54+08urJTu66pDqifkNK7FBloCgRYlkG85cXH03ee6HVUjsS3n1hFRmpSfzwNcs62HG6i+cOt/LRrSvH9UJypiTx7Q9ciAD3/N9OUpMc3FYT2XspsUOVgaJEiBUzWHiWAcB9V53Dn0TYAyfHmcKtmyr55b4mugdH+fK2w5TmpPHHl01uE72kIIP/uv0CvAZuOq+MoilqGZT4QpWBokTIQrYMZsoHLl7KyJiX+36ymz31PfzlteeGdDVdvaaUJ++7jC/dumGepVRmw8L8eaMoMcIYs2ADyLNhXUUONUvzefVkJ6tKsnjP5qlbX1wQpW6nyvyhloGiRMCw24PHaxadZQDwYdst9Nkb15CcpJeORGNx/bxRlFnia0Wx2CwDsGIAL/31VXFXQKZEB1XvihIBffYM4Zz0xWcZiIgqggRGlYGiREDfIrYMlMRm2jNaRB4C3gG0GWMmpQeIyF8DdwW83lqg2B55eRroBzzAmDGmJlqCK0os8LWvXqippYoSinAsg+8DN4TaaYz5im8cJvA54MUJc46vsverIlAWPGdjBovPTaQkNuHMQH4JCHcY6h3Aw7OSSFHiGP8sA1UGSoIRtZiBiGRgWRCPBWw2wDMisktE7p3m+feKyE4R2dne3h4tsRQlqvTZbiKNGSiJRjQDyDcDf5jgIrrcGLMZuBG4T0SuDPVkY8wDxpgaY0xNcXFxFMVSlOjR73KT5BAywmz0pigLhWgqg9uZ4CIyxjTaf9uAJ4AtUXw/RZl3+u3BNjoQRUk0oqIMRCQXeCvwi4BtmSKS7bsPXAcciMb7KUqsWIytKJTFQTippQ8DW4EiEWkAvgCkABhjvm0vexfwjDFmMOCppcAT9i+oZOAnxpinoie6osw//UGmnClKIjCtMjDG3BHGmu9jpaAGbqsFNs5UMEWJR/qG1TJQEhOtQFaUCOhzubXGQElIVBkoSgT0u8a0+lhJSFQZKEoELNT5x4oyHaoMFCVMvF7DwMjYouxYqiQ+qgwUJUwGR8fwGq0+VhITVQaKEibapE5JZFQZKEqYLOYpZ0rio8pAUcKk39+kTi0DJfFQZaAoYXK2fbVaBkriocpAUcKkTy0DJYFRZaAoYdKnloGSwKgyUJQw0ZiBksioMlCUMOl3jZHsEJwp+rVREg89qxUlTPpdbnLSU3SwjZKQqDJQlDDR9tVKIqPKQFHCRJvUKYmMKgNFCZN+1xjZaRo8VhKTaZWBiDwkIm0iEnR+sYhsFZFeEdlr3z4fsO8GETkqIidE5LPRFFxR5hudf6wkMuFYBt8HbphmzcvGmAvs2z8CiEgS8A3gRmAdcIeIrJuNsIoSS3wBZEVJRKZVBsaYl4CuGbz2FuCEMabWGDMK/BS4ZQavoyhxgVoGSiITrZjBpSKyT0R+KyLr7W2VwJmANQ32tqCIyL0islNEdra3t0dJLEWJDh6voX9kTAvOlIQlGspgN7DUGLMR+B/gyZm8iDHmAWNMjTGmpri4OApiKUr0GBjRVhRKYjNrZWCM6TPGDNj3twEpIlIENAJLApZW2dsUJSJOtg9w03+/THv/SMxkONuKQpWBkpjMWhmISJnYJZkissV+zU5gB7BKRJaLSCpwO/DL2b6fsvjYcaqLQ819bK/tjJkMZ9tXq5tISUym/ZkjIg8DW4EiEWkAvgCkABhjvg28F/iYiIwBw8DtxhgDjInIx4GngSTgIWPMwTk5CiWhaeoZBuBgUx83b6yIiQw68lJJdKZVBsaYO6bZfz9wf4h924BtMxNNUSyael0AHGzqjZkMfcPqJlISG61AVuIen2VwoLEXy+icf/pHVBkoiY0qAyXuaeoZRgS6h9w021bCfKNuIiXRUWWgxDXGGJp6XdQszQcs6yAWnFUGahkoiYkqAyWu6RwcZXTMy9VrSnEIHGjqi4kcfS43qckOnClJMXl/RZlrVBkocU1zj+UWWlGcycriLA7FKIjcNzymBWdKQqPKQIlrGu3gcWVeOusrcjjQGBvLwJploPECJXFRZaDENc29ljIoz3WyoTKXlj4XHQPzX4msTeqUREeVgRLXNPUMk5bsoCAzlfUVuYBVfDbf9LvcWn2sJDSqDI6RydIAACAASURBVJS4pqnHRUVeOiLCuoocIDYZRWoZKImOKgMlrmnqHaYizwlAbnoK1QUZMalE7tP5x0qCo8pAiWuaeoYpz033P95QmRMjN5HOMlASG1UGStzi9nhp6x+hIu+sMlhfkUtd5xB9dkvpQEbHvHzgwdd56kBLVOUwBoZGPWoZKAmNKgMlbmnpdWEMVOQ6/dvW23GDQ0Gsg237m3nlRAe/O9waVTk8dj8kDSAriYwqAyVu8fUhmmgZwOQgsjGGB1+pBaxhONFkzGspA7UMlERGlYESt/i6lQYqg+LsNEpz0ibFDV4/1cWBxj4KMlM52T4Y1e6mHo8X0CZ1SmKjykCZMYMjY9xy/yvsPN01J6/f1OtTBs5x2zdU5E7KKHrw5VMUZKZyzxXL6R120zk4GjU5fJaBtqNQEhlVBsqMOdDYy76GXt6YK2XQM0xeRgoZqeMvwusrcznRNsDwqAeA2vYBfneklQ9cstTvRqptH5zytT1ew5j9i386PH43kVoGSuKiykCZMUda+gFo65ub9hBNPa5xaaU+1lfk4DVwuMVyFX3vD6dJcTi4+5KlrCzOBKaPG3z+Fwf44ENvhCWHTxnkpKtloCQu0yoDEXlIRNpE5ECI/XeJyJsisl9EXhWRjQH7Ttvb94rIzmgKPpGBkTE+/pPdPLarYS7fRgngcLN1MW6fo15BTT3DVE5wEQFsqDzblqJnaJSf7zrDrZsqKM5OoyI3HWeKg5NtUyuDV092sqe+B693+tiCWgbKYiAcy+D7wA1T7D8FvNUYcx7wJeCBCfuvMsZcYIypmZmI4ZGZmsTRln5+sL1uLt9GCeCwbRm0z5llMBzUMqjIdZKfkcLBxl5+/Ho9LreXj1y+AgCHQ1hRlDWlZTAwMsbpzkGG3R5a+6efnKbZRMpiYFplYIx5CQjpFDbGvGqM6bYfbgeqoiRbRIgId2ypZt+ZnqA56Ep08XgNx3zKYA4sg4GRMfpcY+MyiXyICOsrctlT38P/vXqaK1YVsbos279/RXEmJ6eIGRxt6cOXbHSqY+rYAljH6kxxkJKkXlUlcYn22f0R4LcBjw3wjIjsEpF7p3qiiNwrIjtFZGd7e/uM3vzdmytJTXbw0x31M3p+vHO6Y5BL/+V3HGvtj7Uo1Nm/rHOcybT3R18ZNPcEzyTysb4yh6Ot/bT1j3DPFSvG7VtZnEVD9xAutyfocwPTUsNVBuoiUhKdqCkDEbkKSxl8JmDz5caYzcCNwH0icmWo5xtjHjDG1BhjaoqLi2ckQ15GKjdtKOOJPY3+TJNE4hd7m2judbH3TE+sRfEHjy9fVcTAyBhDo2NRff2mIAVngWyws4ZWlWRx5aqicftWlmThNVDXORT0uYea+sjLSCEt2cHpMJTBmNdoWqmS8ERFGYjI+cCDwC3GmE7fdmNMo/23DXgC2BKN95uK27dU0+8a4zf7m+f6readpw9aPXd8oyBjyeHmPhwCb1lpXYijbR0EKzgLZFN1HskO4WNbVyIi4/ZNl1F0qLmP9RU5LCvMDNMy8KploCQ8s1YGIlINPA7cbYw5FrA9U0SyffeB64CgGUnR5OLlBawoyuThNxLLVXSma4hDdvaOb/rXVJzuGKR3aHIzt2hxuLmfFcVZLCnIAKAtysqguWcYh0BpdlrQ/VX5Gez422t49+bJIaoVRVkAQTOKxjxejrT0s648h2VFGWEpgzGv0eCxkvCEk1r6MPAasFpEGkTkIyLyURH5qL3k80Ah8M0JKaSlwCsisg94A/iNMeapOTiGifJyx5ZqdtV1x4VvPVr4rIKirDS/C2Uq7nrwdf7+F3One4+09LG2PIfiLOtiHW3LoLHHRWmOk+Qpgrb5malBt6enJlGZlx7UMjjZPsjomJd1FTksL8qivmto2uIzj9dokzol4Zn2544x5o5p9t8D3BNkey2wcfIz5p53b67k358+wsNv1POFm9fHQoSo8/TBFtaUZVNdMP2vWZfbQ2PPML3DbkbHvKQmRzdPoM/lpqF7mDu2VFOSMzfKwEorDR48DocVxZnUBvmcDjVbbSzWV+QyOubF7TE09bioLswI+Voer9GCMyXhSchcucKsNK5fX8bjuxtDZpQsJNr7R9hZ180NG8qoyEv3d/MMRUO35UYaGBlje23nlGtnwlE7eLyuPIf8jFSSHEJbGPn6kdDcOxwyXhAOK4uzONk2MKlh3aGmPlKTHawoymS57U6q7Zi6QE2ziZTFQEIqA4A7tlTTO+yO+qCTWPDc4VaMgevXl1Ge62RgZIz+IMNdfDT2DI97brTxVR6vKc8mySEUZqZG1TIwxtDU65qdMijJYnDUQ+uEgrhDzX2sKcsmOcnB8iIr0DyVpeU14DWG7DS1DJTEJmGVwaUrCllamJEQgeSnDrRQXZDBmrJsyu0L5FTWQaNtGZxXmctzh1qj2s4ZrOBxbnoKZTmWG6ckJy2qAeTOwVFGx7zjhtpESrCMImMMh5r6WFduDcgpykolKy15yvRSj9fXvlqVgZLYJKwycDiE2y+q5vVTXVEfdjKf9LncvHqygxs2lCEifj96U0/ojKKG7iGSHcJdF1fT1OvyZyFFCyt4nO1P6SzOSouqZeA7tvJZuolgvDJo7nXRPeRmnT0tTURYXhQ8tuBD+xIpi4WEVQYA772wimSH8LMdZ2Ityox54Ugbbo/h+vWlAH5lMKVl0DNMeZ6Ta9aVIgLPHoqeq8jrNRxt6WdNWY5/W0m2M6qWQZNdR1E5C2VQkp1GVlryuFbWvjYlvtGZAMuKMjndGVoZ+GcZpKsyUBKbhFYGxdlpbKrOY2997Ct2Z8ozB1ut41iSD0BpjhORs+0agtHQPUxlXjpFWWlsrs6PatygvmuIoVGP39UC1ufcOTDi/xU9W/yWwSzcRCLCyuLMcZbBoeY+RGB1gCJbXpRJY/cwI2PBEw082qROWSQktDIAq4K1pW9mmS6NPcP81c/3Rb3VQri43B5eONrGdetKcTgsl0xKkoOS7KlrDRq7h6nKt1Ilr1lbyoHGvrAK1cLhSMvZ4LGPkpw0vAa6ojRdrKlnmLRkBwUh6gjCxZdR5ONQUx/LCjPJCggGLy/KwGusor5gqDJQFgsJrwzKcpy09LpmFET9zku1PLqrgR2nu6dfPAe8cryDoVEP168vG7e9PDc95MV9dMxLa7/L72K5dp3lXnouSq6iQ839OATOLT2rDHyFZ9FKL23uteSf2GYiUlaWZNHU62JwxFLmh5r7xlk0wNn00hBdToft1GRfsFxREpXEVwa5TkY93oh/tbrcHp7c2wjAkSgHYMPl6YMtZDuTuWRF4bjtFXnOkP2JmnuHMQaq8i1lsLI4k+VFmTx7uC0qMh1p7mN5USbOlCT/tuLs6Bae+WIes2VFQOpon8tNfdeQP3jsY3mhtSZU3KB32E1GajKFWcHbYihKopDwyiCcgGswnjnUSs+QmySH+PPq55Mxj5fnDrdyzdrSSRXElmUQ3NrxpZVW2spARLhmbQmvneyYsjYhXA639LFmwq/rkmzrM45WELm5d5iKIENtImVlydmMosN28HiiMsjNSKEgMzVorcHgyBj9rjHyMjR4rCQ+Ca8MyuyLSmuEcYNHdpyhMi+dK1cVcbh5/nscvXGqi+4htz+LKJDyXCfDbg+9w5Mv7r7q46q8s+0Vrl1XhttjePl4x7i1LreHT/50D3c8sD0smfpdbs50DU9ytUTTMhgd89LWPzKrtFIfSwszcIjVj8iXXrt+guwAywqDt/h47WQnxhhyNZNIWQQkvjLIidwyONM1xCsnOritZgnrKnI42T4QMttkrvj5rgay05J567klk/b5KnObgriKGuxun2UBmTibq/PIz0gZl2LaMzTK3d99nSf3NvFabWdYVoOv8d+agKliYDWGy06LzpCb1j4XxhB09nGkpCUnUV2Qwcn2AQ419VGUlepXXIEsL8oKqgxeOt6OQ0SDx8qiIOGVQXF2GkkOoSUCZfDznWcQgffVVLG2PIcxr+HENAPWo0n34Ci/2d/MuzZXkp6aNGl/md/1NTmI3Ng9TGmOc5xrKTnJwVVrSnj+SBtjHi8N3UO899uvse9ML++xW0CH08r5kG0hTXQTgfU5R0MZnE0rnb1lAGczig41W11WgwWllxdl0No34g80+3jpWDs56Sk4ZhnIVpSFQMIrgySHUJKdFnZ6qcdr+PmuBq5cVUxFXjpr7QvfdK6itn5X1KyHx3Y3MDrm5c6Lq4Pu9/nTg6WXNnQP+YPHgVy7tpTeYTf/91od7/rmq7T1ufjhR7bwsa3WyMhwqrSPNPeR40wO2iYiWsqgeZoJZ5GysiSL2o5BjrcOTIoX+PBlFAUGkes7hzjdOUSeuoiURULCKwOwCrXCtQxeOt5Oc6+L2y9aAsCywkycKY4pg8iDI2Nc/dUXuf4/X+K1k7PrEmqM4Sdv1LO5Om9clW8gxdlpJDskaOFZY89w0MrdK88tJjXJwZd+fYgUh/Dox97CxSsKqS7IJMkhIVMrAzncbAWPg/26Ls5Oi0pqaeM0s48jZUVRJqNjXkY9XtbbozInsqzIiq+c7jhba/DScWsOd64Gj5VFwqJQBuW5zrCLrh7ZcYbCzFTettYK3CY5hNWl2f5iq2DsqutmYGSM7iE3d3xnO5997M2gwd1weP1UF7Xtg9x58dKQa5IcQmmOc1IcZMzjpbnX5S84CyQzLZl3bCxn45I8Hv/Ty/x1AqnJDr9ffSp8bSgmBo99RMsyaOgeJj8jhYzU6PjpfRlFQEjZlxX6UlDPfgYvHWunMi99XAqtoiQyi0IZlOU6J7UyDkbHwAjPHmrl3Zsrx/nc15bncLi5P2Th2vbaTpIcwu8+9Vb+5MoVPLLzDNd87UWeOhD5HOafvF5PjjOZd5xfPuW68lznpGZ1rf1WS4jKIG4igP9430Z+cd9l44LLYP16Ptk2tWXQ0D3M4KhnUvDYR0m2k8FRzyS/e6Q09gyHlH8m+BrWOVPOtqyeSGZaMmU5Tk7ZloHb4+XVk51ceW4xGi1QFgthKQMReUhE2kQk6BxFsfi6iJwQkTdFZHPAvg+JyHH79qFoCR4JZTnTzwAAeGJ3I2New/ttF5GPNWXZdA2Ohsyjf/1UF+dV5lKUlcbnblrLL+67nOKsND76o93c+Z3tPHuoNay+PV2Dozx1oIV3b66a9hdpeZAhNw12S4VQDd5CVfSuLMniVOfglDL6JoQFCx5D9NJLG7uHxqXFzpaCzFTyM1JYU5ZDkiP0pd2ah2xZBnvP9DAwMsZbzy2KmhyKEu+Eaxl8H7hhiv03Aqvs273AtwBEpAD4AnAxsAX4gojkz1TYmeL7JTxV3MAYw0931HPh0nzOKRn/6/dsEHmyq2hodIx9Z3rGVQmfV5XLLz5+GX/39rWc6hjk//1gJ1u/+gIPvHRyyiH1j+46w6jHy10hAseBVORObrPh87cHCyBPxcpiy6/uK1gLxsGmPpIcMoVlYCuDgZkrA2NM1C0DgP935Qr+6C3LplyzvCiL052WMn3pWDtJDuHSlaoMlMVDWMrAGPMS0DXFkluAHxiL7UCeiJQD1wPPGmO6jDHdwLNMrVTmBF+a4lS1BrvruznZPsj7a5ZM2ucL5AbLKNpd18OY13DxioJx21OSHNxzxQpe/vRVfPOuzZTnpvPlbUe4+F+e459+fWjSOE6v1/DwG2e4aFk+q0qDX3DHH5PVZqMzoM2G72IeaSbOiiC9/ydysKmPc4qzQlosPsugLQx3XCi6Bkdxub2zal0djD/deg63bqqccs3yogy6BkfpGRrlpWPtXLAkT4vNlEVFtGIGlUDg0IAGe1uo7ZMQkXtFZKeI7Gxvb4+SWBa+wrOp0kt/d7iNZIfw9iC++tyMFCrz0oNaBr54Qc3S4AZPcpKDm84r55E/uZRtf34F7zi/ggdfOcWt3/gDJ9rOKpfXajs51TEYMp10Iv6JZwGFZw3dwxRnp0Uc9Aw2CGYiBxp7x80BmIjfMphFRpHPsom2ZRAOvvTSPfU9vNnYy5WriuddBkWJJXETQDbGPGCMqTHG1BQXR/eLWJJjXaimchMdbxtgWVEmmSFm3a4tzw6qDF4/1cmGytywJmGtq8jhq+/byPc+fBHt/SPc/D9/4Gc76q100tfryctI4cYNUweOffgnngVkSYVKK52OgsxU8jJSOBkivbS9f4S2/pGQefoA+RmpJDlkVv2J/H2VomwZhMNyO730h9vrMAau1HiBssiIljJoBAL9K1X2tlDb5xVnShKFmalTuolOtg9wTnFWyP1rynKo7Rgc594ZHvWw90wPlywvCPm8YFy1uoTffuIKNlXn8ZnH9vOxH+3mmUMtvCeMwLEPv+srIKMoVMFZOKwszqI2hGVwsMkKHm+oDJ6nD9aY0aKs1FkFkP19lWJgGSwpsPoYvXC0jdz0FM6vypt3GRQllkRLGfwS+KCdVXQJ0GuMaQaeBq4TkXw7cHydvW3eKc1xhmxWNzrmpa5ziJUlwVMPwQoieya0pdhT343bYya1mA6HkhwnP/zIxfz19at59nArbo/hji3huYgACjNTSU1y+BWc12to6nHN2MViTQULbhkcDNHxcyIl2c5ZBZAbe4bJSkuOia8+LTmJyvx0jIHLzymaMvNIURKRsCp7RORhYCtQJCINWBlCKQDGmG8D24CbgBPAEPBhe1+XiHwJ2GG/1D8aY6YKRM8Z5bnOkNPB6uy0ynNKQlsGa+3JXoea+/y/kLfXduIQqFk2swSpJIdw31XncNk5RZzqGJjy/SficAhlAcfUMTDCqMdL1QxdLCuKs3hkZwO9w+5JF+ODTb1UF2SQM40rrDg7LaIeUBPxjeuc7VCbmbK8KIszXcPqIlIWJWEpA2PMHdPsN8B9IfY9BDwUuWjRpSzXye764BPLfIHTc4pDZ/EstdtSHAnIKNp+qivseMFUXLAkjwuWRO6WKM91+t1EZ/wulpnl6PuCyLXtA2yqHq/cDjb1TRk89lGSncb+xt4ZvT9Ev+AsUlYUZfLSsXau0OCxsgiJmwDyXFOW46R7yD0ppRPwu35WFId2EyU5hNVlOf4gssvtYW99DxdHGC+IJhUBhWezzcTxHftEV1Gfy01d51BYyqA4O43OgZGwCuyC0dg9FJPgsY+7L13Kl991XtSa5CnKQmLxKAM7+yZY3OBE2wAVuc6QmUQ+1pVnc7ilD2MMe+p7GPV4ZxQviBbluVYcxOM1NHRPXX08HdUFGSQ7ZFIQ2TchbP0UwWMfJdlpeA10DkYeN+hzuelzjcXUMlhZnBV2aq+iJBqLRhlMVXh2on1gXEOzUKwpy6FnyE1r3wjbazsRgZplsbMMyvPSGfMaOgZGaLQbvE2n0EKRkuSgunBywzpf8DhcywBmVngWy7RSRVEWkTIoyw1ea+D1Gk62DYYVvA1sS/H6qU7WV+TEtErVN1egqWc4Kv72lcVZk9xEB5p6Kc5O8885noriWbSkaIxhWqmiKItKGVgXmYlVyM19LobdnrCUwRo7o2jvmR521/dw8fLYuYggcOKZi4bu4Vk3eFtZnEVd5yBjHq9/26Ewg8eAX2HMpNYgltXHiqIsImWQlZZMdlryJMvAFzxeOUXBmY8cp9WW4uc7zzA6Ftt4AQRMPOsZprF79pbBiuJM3B7jz0xyuT0cbxsIWxnMpnNpY88wqckOijInzyhWFGXuWTTKAKxf0hOH3Jy0lUG4Of5ry3No6nUhAltiGC8AyMtIwZni4FBTH8Nuz6xdLIHppQDHWvvxeA0bQkwIm4gzJYlsZ/LMlIFdY+DQYi9FiQmLThm0TAhunmgfIC8jhcLM1LBeY53tKlpblhPzkYgiQkVuOm+ctur4Zht8XelPL7WUwYFGX/A4PGUAMx9/2TDDvkqKokSHxaUMcpy0TLAMTrQNsLI4K+yqV99wl4ktq2NFeZ4zoKfP7GIGeRmpFGam+qeeHWzqJduZzJKC8C/SJTMcf+mzDBRFiQ2LShmU5zpp7x8ZFyA92TZ1g7qJXLg0n2xnMtevL5sLESPGlzIL0Qm+rizOotae+HWwqY915TkRtYcoznZGrAxcbg8dAyOaSaQoMWRRKYPSXCdeczb1sXtwlM7B0Yh6ApXmONn/xetjHjz24UsvzY5Sg7cVdsM6j9dwpKVvyk6lwSjOSou4jbVmEilK7FlUyqA8IBUTAnoSRaAM4g3fkJtoXUhXFmfRNTjKrrpuXG5v2JlEPkpy0hga9TA4Mhb2c7TgTFFiz6JSBmU5dq2BrQwiSSuNV3wKLlouFl+Pol/us8ZORBI8BssyACKyDtQyUJTYs7iUQRDLIC3ZsaAvQr6YwWyDxz58inHb/hbSkh3+DKNw8U2ViyRu0Ng9TJJD/ONJFUWZfxaVMsjPSCE12eFvVneibYAVxVkLepBJVX46GalJrC4L3X470tdLTXLQNTjKmrJskpMiO0VmUnjW2DNMWY4z4vdSFCV6zKyr2QJFRKwZAD43UfsAFyyZ2WCaeCEzLZnf/9VWCsKsk5iO5CQHSwszrMrjCIPHcLYlRSS1Bg3dQwvaOlOURGDR/RQrtWsNXG4PDd3DEbtB4pGSKP+q9rmKIg0eA+Slp5DsEFoj6Fza2D084wltiqJEh7CuICJyg4gcFZETIvLZIPv/U0T22rdjItITsM8TsO+X0RR+JpTnOmnpc1HbPogxCzuTaK7wBZEjDR6DNY5zdVk2e0JMlZuI2+OlpW/ms5sVRYkO07qJRCQJ+AZwLdAA7BCRXxpjDvnWGGP+ImD9nwGbAl5i2BhzQfREnh1luU5ae0c43maNr1RlMJnr15dxrHXAP/c5Ui5fVcRDr5xiYGSMrGnmK7T0uvAaTStVlFgTjmWwBThhjKk1xowCPwVumWL9HcDD0RBuLijLcTLq8bLjdBcOgWWFC99NFG02LsnjwQ/VkJacNKPnX7mqGLfH8Hpt57RrNa1UUeKDcJRBJXAm4HGDvW0SIrIUWA48H7DZKSI7RWS7iNwa6k1E5F573c729vYwxJoZvrz8P5zoZElBBs6UmV3wlNBcuDQfZ4qDl493TLtWC84UJT6IdgD5duBRY0zg1Pmlxpga4E7gv0RkZbAnGmMeMMbUGGNqiouLoyzWWXxDbk51DEbUk0gJH2dKEluWF/Ly8emVus8y0CH0ihJbwlEGjcCSgMdV9rZg3M4EF5ExptH+Wwv8nvHxhHknsLBJ4wVzx5WrijjZPkhTz/CU6xq6hyjOTlMLTVFiTDjKYAewSkSWi0gq1gV/UlaQiKwB8oHXArbli0iafb8IuAw4NPG580lxdpq/yGwht6GId65YZVl3r0zjKmrUOQaKEhdMqwyMMWPAx4GngcPAI8aYgyLyjyLyzoCltwM/NcaYgG1rgZ0isg94AfjXwCykWJDkEErsKtmVahnMGeeWZlGSncZL07iKojGuU1GU2RNWBbIxZhuwbcK2z094/MUgz3sVOG8W8s0JpTlWFbK6ieYOEeHyVUU8f6QNj9cEbfnh9RqaelxxMxtCURYzi64CGaw0xpLstKj0/1dCc+WqYnqG3Bxs6g26v2NghFGPVy0DRYkDFlVvIh9/fd1qOgcjH82oRMZl5xQB8PLxDs6vypu0v6HHN65TlYGixJpFaRksK8rkwqXxMcM4kSnOTmNteU7IFNMGf41BdNpvK4oycxalMlDmjytXFbGrrjvo5DN/wZlaBooSc1QZKHPKFXZrijdOdY3bboxhT303uekp0/YvUhRl7lFloMwpNcvySUt2jEsx9XoNf/fkAZ451MpdF1fHUDpFUXzoTzJlTrFaUxT4+xSNebx8+rE3eXx3Ix/bupK/vn51jCVUFAXUMlDmgStXFXOibYD6ziE+8dO9PL67kU9dey6fvn41Igt35KiiJBJqGShzzhXnFsE2eP8Dr9Hc6+Lv3r6We65YEWuxFEUJQJWBMuesLs2mODuN5l4X/3TrBj5wydJYi6QoygRUGShzjojw1fdtxGsMV60uibU4iqIEQZWBMi+89dy5m1GhKMrs0QCyoiiKospAURRFUWWgKIqioMpAURRFQZWBoiiKgioDRVEUBVUGiqIoCqoMFEVRFECMMbGWYRIi0g7UzfDpRUBHFMWZLSrP1MSbPLMl3o5H5ZmaRJJnqTFmxtWdcakMZoOI7DTG1MRaDh8qz9TEmzyzJd6OR+WZGpXnLOomUhRFUVQZKIqiKImpDB6ItQATUHmmJt7kmS3xdjwqz9SoPDYJFzNQFEVRIicRLQNFURQlQlQZKIqiKKoMFgKiU+OnRD+fuUU/36lJlM9HlYGNiMTVZyEiS0RkJYAxxsT6hBORS0TkhljKEIiIFIlILvg/n7j6/0VKvMmv59/UJNr5B6oMABCRq4E7RSQ/1rIAiMhNwG+Bb4jIbyG2X0gRuR74FhMqI2Moz43ANuA7IvIogDHGGwtZooGef9PKo+ff9DJdKSKXz0opGWMW9Q24DPACzwLvB/JjLM8m4E3gUvvxw0BeDOXZivUl3GQ/zsDOQrMfyzzLcxVwBLgWyAWeAj4b6/NoFsej59/U8uj5N71Mb7XPodeASwHHTF4necZaJAEQkWQgH+tLaIBbAIeIPGWM6bbXiLE/8XnCC7xgjHlNRKqAq4GviEgxcKcxZmi+ZLJ/eRUBtUCKiGQB3weG7ft3GGNc8yGPLUsqsA74nDHmWXv7d4EL5vK95wo9/6ZGz7+w5EoFzgHeA5QDnwf+SUReMxFaK4taGRhjxkTkOSDJGDMoImnAjVhfyN8aY7rm+YsIMAKsEZH/Ad4OfA24H+tL8ARw/XzJZIwxtptgFPgXYCPwz8DjtlxPADfOhzz2e4zYZnlywAWgBbhkrt9/LtDzb2r0/AtLrlER+QXgMsYMiEgG8LfAv4jIq8YYT7ivtSiVgYi8Fbgc2A0cN8acADDG/Nj2uV0PtInIJiyz/XPzIM9lwB5gO/ARLC1fJY3sgAAACnVJREFUAHzTGDMIvE9EtolIsTGmfY7lWWqMqQOwL1K/A9KBQmPMN+017wceE5EcY0zfHMtzGdYX7iiwxxjTGLB7GMiy190DVBtjPj+X8swWPf+mlUfPv+llugQ4H3gdaDLGDAAYY75qWzGfA/5cRK7AOoe+Nt1rLroAsohcCzyE5Xu8FnhARLb69htjfgj8EPgq8GfAI/MkTybWReAx4BxjzA5gELjQXncbUIL1K2ku5XkncEpE/t63zb4YPAn8b8DSO4FCLPfGXMrzDvt9q7DcKB8REWdA8LAW2Cci7wPuBR6dS3lmi55/08qj59/0Mr0d+ClWfOdTwL+LiL/TqTHmK1jnzXPAF4FnwnrhuQxsxOMN+ATwaft+DvAB4CCwNWDNu4B+YF0M5dkIvAM4gHVx2ANsmGNZSoD/w/pVsQvLNzpxjQO425Zr/RzLswrYCWyxH78NK9BaELCmEMvPfQhYG+vzS88/Pf/m4X/2ReAu+/5q4OPAb4ALA9bcCvRGcg7NueDxdsPS3v83YdsHgKeBFfbja+fjiziFPB+05UkH1ttfzOp5kEWAt9j3V9kn+OcmrMmzv6xzfuJjuTE/ADgDtj0JXBXwOAvrV9K5sT63ZvH/1vPP6PkXgVxfBr4T8LjIVgj/a993An8UqbKcF+Hj6YaVEbAP+GrAtgKsINkV9uN5S1ebQp5v+eSZJzkmHXPAF/Jv7MfXABXMMHUtQnkcEx4n2X8fB26272+xv4zOuZZnHv7fev5N3qbnX3DZ8oCXgL8K2LYhUCn55I3ktqhiBiKSZIwZxcrYuFhEvgZgjOkCUrD9o8b+NGMsj8Mnz3ww8ZhFJNkYcxzLT/oeEXka+DqQYuahwCbIe/jO1TNAs4jcDPwbkG2Mcc21PNFAz7/Q6PkXHiLiMMb0AJ8GLhORz9jyHgA8wEX247CziPyvPU/nXdxgn2RjIlIK/BI4BrRj+UffYYw5tsjlmZSzLSL/ANyH5dc+EEt5ROTzWD71UeAeY8z++ZRnpviOI17+33EsT1ycf6HkieX5F0SWi7AU0mngOHAPcI0x5tRMXj9hU0tFpMwY0zJhm8M+8S8GyrDS6d6DFTj7zlye+AtEHt8XoAbYaIz5roicA6wB3jaXX8Qw5NlkjPkO1mezDsunfnKu5Jkt9v90LXAK2G+M6bJ/icfq/71Q5PHE6PybTp55P//stNALsKyRV4wxHQEybcAqWHwn8KdYWVW3zlQRAIkZM8CKpHuBPwqy71JgL3CDyjOlPG+zHwuQEwfyXGs/TiPOg8VYv6r3Aw9ipW1+CkiO4f97Icozn+dfOPLM6/mH5brbh/XL/2Gs6m/fvqttebdG9T3n6wSYxxOtAitX+mtYKXJ3T9h/F3CT70RTeaaUJ+Ig1BzLkxLr8yuM41lvXzx8vXTehZWBkhaj//dClmc+zr9I5JmX8w84D6vPkC+z6u+Bz2DVOmRjtS95X7T/Z3N+YPN9wyrmudq+fxVWUcjdQdbN+Ymm8iw8eaJwPIVY6ZpJAdueBWpi9PmqPAtIHvu98rFrBrBSRU8Cvwa+AfwEKLb3RVV5z8vBzdMHWAaUBdm+1b7AfNB+fCVQpPKoPHNwPOUTtvl+Xf4WuMS+fwGQq/KoPCFkKpuw7V3Ah+z7lcB3sVNbo/7+83GQ8/Ahvgd4GauvymewmmkF7r8KK1/5J1gmYZXKo/LM4/E8BKwA3m1faEpUHpVnCpk+PVGmgHUPEsRyjsZtwaeWikghVg+OPwbcWNWbq7Ha8P4sYN39WL62q80cpoOpPAtLntkSzvHY+fubsAqUPmzmNitG5VlA8kwj04vGmIcD1r0H+DvgPcaY2mjLkQippUlAH3DKGNMjIp1YlYpbRaTdGPO8iKzF0vTXzMOFReVZWPLMlqmOp9MY8xxWRe+FwGZjdyhVeVSeMGS6UkRa7e/EvcBfAO+dC0UACVJ0JiL/jdV18RPGanlbgdVfZdQY8zWxenynGXtgiMqj8kSTKY7HbYz5DxGpBrKMMYdUHpUnQpl834kyIGOuFAEs8BbWcnbe5zewNOtnRCTTGNOE1WjrFhEpNMYMzceFReVZWPLMljCO551i9f+vn48Li8qzsOQJU6ZbRKTIGNMyl4oAFqgyELF6iZuz/UNOYjWQSge+LSJFwLnAGJYPTuVReaJGhMczovKoPLOUaU5nSPhlWkhuIhEpwBrvNhSwLdVYo9+qsHx9H8IqFy8APmaM2a3yqDzRIN6OR+VZWPLEq0x+zBynTEXrhtW98FdY2vNuAopCsIZO/By75zqQC2SqPCpPoh6PyrOw5IlXmcbJN59vNosP8VysXhzrsIqQvoLVr+MKrNa/27HSrVQelSfhj0flWVjyxKtME28LJbW0CGgwdlBHRPKxOvXdhtVu9xZjTKvI5Pa3Ko/KEwXi7XhUnoUlT7zKNI6FEkA+APSJyN/Zjzdh9V13AcuMMa0wf0NBVJ4FJ89sibfjUXkWljzxKtM44jaAbAdT+o0xvSKSjNWi4D4sBWaMMbeIyB3w/9s7Y9YooiiMnqumEERtVgkqiogIIoigRRAsBBEUGxvRFLERO4NIQP+FGARTaIoUWgg22qcTRAKCKKQRUSIKYuzlWrxZXFLsujuzM/cN34Fhd3YH5nzbPN68ffdynlT6eKxB5JOXT1mi5ZFPXj5RnfpS5TOnqg5SffuPwG2KCn3F55tIxZy6/UhvAPflI58255FPXj5RnQYd4WYGZtYhNXb+DHwBvgNP3f3HhutmgevAtI+3lo58MvIpS7Q88snLJ6rT/xBxzWAdmAVukipWHgKumNku+LdZg1TP41oNP6J88vIpS7Q88snLJ6rTQMLMDCzVA/lGajfXuyHjMnAGWHX3B2Z2wmvYhCGfvHzKEi2PfPLyieo0DCFmBmZ2AXgFzANPzOxI9zt3fw4sAx0zewEsm9ke+cinKqLlkU9ePlGdhqbJBQvAgH0UzZ2B3cAdYA04uuHaJeATcEw+8mljHvnk5RPVaeQsjQuk52YLpJZu3cdWt4CvwOHifJLU+eq4fOTT5jzyycsnqtNIORq7cVpUOUlqSP0MmNvw/RywCGwtzrfJRz5tzSOfvHyiOpXK08hN4SLwjvQcbR64RJo+3e255gDwiGKklY982ppHPnn5RHUqe9Rem8jMpkhFmq66+4qZLQCngCngtZltJv1H9zSp9dxOYGyNTuSTl09ZouWRT14+UZ0qoe7Rh/SDzfScd4CXxfuDwGPgIfCWGhZa5JOXT9vyyCcvn6hOleSq/YZpsWV7z/u9wAowWXy2H9gC7JCPfNqeRz55+UR1quKofZ+Bu/9x99/FqQG/gJ/uvmZm08A9YMLd1+Ujn6qJlkc+eflEdaqCEDuQzWyR9L/cc6TpV6Pbs+WTl09ZouWRT14+ENNpWBodDIoaHRPAh+L1rLuvykc+dRAtj3zy8onqNCpRZgYzwBt3f9+0C8hnENF8yhItj3z6E80HYjoNS5TBIFT7Q/n0J5pPWaLlkU9/ovlATKdhCTEYCCGEaJYQVUuFEEI0iwYDIYQQGgyEEEJoMBBCCIEGAyGEEGgwEEIIAfwF3p2FR1mQdt4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsyJ_tktzGL9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "ad37f297-2b89-483f-be52-411b44b056c7"
      },
      "source": [
        "# detect decrease\n",
        "timeseries = TimeSeriesData(\n",
        "    df.loc[:,['time','decrease']]\n",
        ")\n",
        "detector = CUSUMDetector(timeseries)\n",
        "\n",
        "# run detector\n",
        "change_points = detector.detector(change_directions=[\"decrease\"])\n",
        "\n",
        "# plot the results\n",
        "plt.xticks(rotation=45)\n",
        "detector.plot(change_points)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "ITmdOhlyzGL-"
      },
      "source": [
        "If we try to detect a decrease in a series where there is only an increase, no change point will be found."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSXvg1pZzGL_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "ab1bd5b5-d62c-47bd-eff8-fdd28b7b9ef7"
      },
      "source": [
        "# detect increase\n",
        "timeseries = TimeSeriesData(\n",
        "    df.loc[:,['time','increase']]\n",
        ")\n",
        "detector = CUSUMDetector(timeseries)\n",
        "\n",
        "# run detector\n",
        "change_points = detector.detector(change_directions=[\"decrease\"])\n",
        "\n",
        "# plot the results\n",
        "plt.xticks(rotation=45)\n",
        "detector.plot(change_points)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:root:No change points detected!\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f8XTcffFSkh3CavuzC5fNywtwub1x2dJalYGizCF//tgefvjGqaD76ruGqC6YHC/wcefmap759OV86eZ1JAdUxIoI7zq3nN9PcBXVtg+S7JBJCqYq33o8sdbgSEs/SQ7xD2DZtDSfJIfEzFVkjOHxnQ3ULM2fctLbzRsqqO8amraz6y/2NPKD10/xh5cu5x/eu55kh/Di4baga/c39bIuDBeRj/OX5LH/764Py60UyIXLrKZ18VhvMK0yEJElIvKSiBwUkQMi8qkga0RE/lNEjovI2yKyKWDfR0TkmH37SLQPQFHileNt/Ty5q5H//N0xRsfGuwX6XG66BkentAySHMLqsuBui5t8rqKDZ1xFJ9oHqC7ImNRKITc9hey05EluosMt/SwvyiQtOQmw3B/rK3Jipgz2NvRyrG2A918Q3Crwcf3aMlKSpnYVHW3t5/NP7KNmaT5fuGk12c4UNi8v4MXDrZPW9g65Od01zPqKyC7szpSkiNaD1dZieVEmb52Mj6ytQMKxDMaAzxhj1gIXA/eLyNoJa24EVtm3+4DvAIhIAfAl4CJgM/AlEcmPkuyKEtc8vdu6WHUMjPL8wfEXofoJ3Uoj5byqXCrz0tka4CqqbR/0Vx5PpDI/fVLh2ZHWPr+LyMfm5QXsOd0zZebNXPH4ztM4UxzcdF75lOtyM1K48uxifrOvOWg31n6Xm4//eCeZacl86+5NfuV49eoSjrYOcLpr/Oew307fPTfCX/kz5cJl+eyo64q7TrLTKgNjTLMxZpd9vx84BFROWHYL8ENjsQ3IE5Fy4HrgeWNMlzGmG3geuCGqR6AocYgxhqf3NHLpWYVU5qXzyFt14/b70kpnOiRdRLjp3DJ+f7yD3iE3Hq/hZOdgSPdKVf74WoOBkTFOdw2zunSiMihk1OOd1XCdmeBye/jlniZuWFcWlt/+5g0VNPe62Dmhf1P34Ch/9rO91HUO8cBdGykNyPa5enUJYHUmDcTnv18XRvA4Gly4rICeITfH56h4bqZEFDMQkWXARuDNCbsqgcDJDw32tlDbg732fSKyQ0R2tLe3RyKWosQdO+u6aege5rZNVdxx4RJ+f7yTUx2D/v11XZNbV0fKTeeW4/YYnj/USlPPMKNjXlaGsgzy0sfFDI7aqZlnT7AMLlxmGe7zXW/wwqFW+lxjvP+CJWGtv2ZNKc4Uh99V1DU4yr88c5jL/vlFXjjUyl/ftIaLVxSOe86K4iyWF2VOihvsb+qjMi89aPbSXBCvcYOwlYGIZAFPAJ82xvRFWxBjzIPGmBpjTE1xcXG0X15R5pWndjeSnpLE9evKuP3CJSQ5hEffqvfvr+8cojAz1V9QNRPOX5LndxUdD5FJ5KMqP4P+kTF6h90AHLUziSZmw+RlpLK6LJs35zlu8PjOBipynVyysnD6xUBmWjLvXF3K1n3N/LOtBL7z8gm2rC7h2U9fwR9etjzo8646p4TXT3SOmzF9oLE3ZHO6uWBpYQbF2Wn835H2uJqLHJYyEJEULEXwE2PMk0GWNAKBKr3K3hZqu6IkLKNjXn6zr5nr1pWSmZZMaY6Ta9aU8POdDf6eQtOllYaDiHDj+jJePdbOXrup3cQaAx9n0kst99Thln4yUpNYkj9Zhs3LC9hZ1x0yJz/atPS6eOVoO+/bVEVSQCvv6bh5QzkdA6N89+UTXG0rgW/dtWlSHCSQd64pYXTMy+vHLcun3+WmtmMw4uDxbBARPnBBFc8fbOXfXzg2b+87HeFkEwnwfeCQMeabIZb9EviwnVV0MdBrjGkGngWuE5F8O3B8nb1NURKWl4+20zPk5tbzz3hE77poKV2DozxrF4oF61Y6E246z3IV/fCNOnLTUygI4eqomtDK+khLP6tKs8fNUfCxeXkBQ6MefyFWtNi6r5nHtp+eVHDlry2YJotoItesKeUrt6zjuU9fwQN3beLs0ulz/i9cVkBWWjK/s11FB+1jjDRFdLb8xXXncHtNFf/xu2M88GJ8KIRwbNRLgXuAfSKyx972V0A1gDHmu8BW4CbgODAEfNTe1yUiXwW228/7ijEmvhxlihIhXq/h1/uauWZNSdAOl0/vbqQwM5XLVhX5t11+VhFLCtJ55M06rl9XSlPvcNAGdZGycUkeFblOmnpdbKrO849pnIiv5UWD3aPoSGs/164pDbp287IzA9w32IN2ZkvvkJs/f2wPLreXB146zqfeuYpbN1biECuLqGZpPstDWDWhSE5y8OFLlkX0nNRkB5evKuKlw23WZDNbGaybRzcRWKMw//F95zHmMXzjuaMkJzn4+JUr51WGSTJNt8AY85oxRowx5xljzrdvW40x37UVAXYW0f3GmJXGmHONMTsCnv+wMeYs+/Y/c3kwijIfHGjq408f3c0nH9k9yZXS53Lz/KFWbt5QMS7f3+EQ7riwmm21XbxytANjmLWbCGxXkd0aYapCrYLMVNJTkmjsGaZ9YISuwdGQ7pSSHCfLizKjGjd4YlcDLreXv333WrKdyXzm53u57t9e5r9ePM6J9sFpawuiydWrS2jpc3GwuY8Djb2U5qRRkh1+j6FokeQQvv6BDdy8oYJ/+u1hHnq1dt5lCEQrkBUlQrrtqt8XD7fx5V8dGBcEfGZfC6NjXm7dODlp7gM1VSQ7hK8/exiIjjIAK6sIzgy0CYaI+GsNjoQIHgeyeVkB20914YlCLrwxhh9vq2NjdR4fu2w5v/rkZXzn7k04RPjm80dxpjh41zS1BdFkyzlWiumLh9rY39Q7r/GCiSQ5hH+7fQM3ri/j739ziP99/VTMZFFloCgR0m+PibxmTQk/3lbPQ6+e9O97ek8jy4sy2VA1+QJTku3kunWlHG21Mn+makURCZuq8/in953LB2qm/nXtG3JzZEJPomBsOaeY3mE3Lx8N3r4hEl4/0UltxyD3XLwUsKykG88t55lPX8F/3rmRf//gRrLD7AkUDYqz09iwJI/f7GvmeNtARG0o5oLkJAf/eedGrl1byn+/fCJmjQJnntemKIuUfpeVnvnl96wjLTmJf9h6iMr8dDZW5/FGbSeffufZIX33d21eytZ9LWSkJlGUFZ28dhHhjs3V066rzEtnz+keDrf0U5SVRmFWWsi116wtpTg7jR+9UcfVq4PHFsLlR2/UkZ+R4rdgfCQ5hPdsqJjVa8+Uq88p4d9eOAow7UCb+SAlycEDd22kc2B0VunGs0EtA0WJEJ9lkJOewr/evoELlubzZz/bw9e2HsYYuOX80Be4d6wsZGlhBssKM0MqjLmiKj+DniE3u+q7p+22mZLk4M7N1fzf0XZ/64xg9Lvc/MXP97IvRMVyS6+L5w+1cvuFS2bUy2eueOeaEv/9+c4kCkVachIVEbbEjiaqDBQlQvpdbkQgKzUZZ0oS3/twDeW5Tn61t4mN1XksmyIrxuEQvvuhC/jn286bR4ktfLUGte2DU7qIfNy1uRqHCD95sy7kmu++fILHdzZw34920DkwMmn/o2/V4zWGuzcvnbngc8C6ihxKstMoyEyNaHRlIqPKQFEipM81RlZasj9HvyAzlf/56GZWFGdy72Urpn3+mvIczg0SU5hrfLUGMHW8wEdZrpPr1pbysx2ngzaua+l18f3XTnLhsnw6B0f51E/3jAs4uz1eHn2rni1nF8+4B9NcISL8yTtXce/ly+fdQotXVBkoSoT0udyTmqktL8rkxc9smdesmEipCnBBhDuU5Z6Ll9Iz5ObXb08eQv/vLxzF4zV88/bz+eot63jteAf/9vxR//7nD7bS1j/Chy6OL6vAxz0XL+WPt5wVazHiBlUGihIh/a4xsp0LL/eiKCuN1GQHIrCqJDxlcMnKQlYWZ/KjbeNdRcda+3lsx2k+dPFSlhRk8MELq7m9pooHXjrOC3a77h9vq6MyL92fyqnEN6oMFCVC+l3uBakMHA6hMi+dZYWZpKeGF8wVEe65eCl7T/fwdsOZkZH//MwRMlOT+ZOrV/m3feWW9ayryOHPHtvDi4dbef1EJ3dfXB1RvyEldqgyUJQIsSyD+cuLjybvv8BqqR0J77ugiozUJH70hmUdbD/VxQuHWvn4lpXjeiE5U5L47ocuQIB7/3cHqUkObq+J7L2U2KHKQFEixIoZLDzLAOD+q87ijyLsgZPjTOHWjZX8cm8T3YOjfG3rIUpz0vjDSye3iV5SkMG/33E+XgM3nVtG0RS1DEp8ocpAUSJkIVsGM+VDFy1lZMzL/Y/sYnd9D39+7dkhXU1Xry7l6fsv5au3rp9nKZXZsDB/3ihKjDDGLNgA8mxYW5FDzdJ8Xj/RyaqSLG7bNHXri/Oj1O1UmT/UMlCUCBh2e/B4zaKzDAA+aruFPn/japKT9NKRaCyunzeKMkt8rSgWm2UAVgzglb+8Ku4KyJTooOpdUSKgz54hnJO++CwDEVFFkMCoMlCUCOhbxJaBkthMe0aLyMPAu4E2Y8yk9AAR+Uvg7oDXWwMU2yMvTwH9gAcYM8bUREtwRYkFvvbVCzW1VFFCEY5l8APghlA7jTFf943DBL4AvDxhzvFV9n5VBMqC50zMYPG5iZTEJpwZyK8A4Q5DvRN4dFYSKUoc459loMpASTCiFjMQkQwsC+KJgM0GeE5EdorIfdM8/z4R2SEiO9rb26MllqJElT7bTaQxAyXRiGYA+Wbg9xNcRJcZYzYBNwL3i8gVoZ5sjHnQGFNjjKkpLi6OoliKEj36XW6SHEJGmI3eFGWhEE1lcAcTXETGmEb7bxvwFLA5iu+nKPNOvz3YRgeiKIlGVJSBiOQCVwK/CNiWKSLZvvvAdcD+aLyfosSKxdiKQlkchJNa+iiwBSgSkQbgS0AKgDHmu/ay9wLPGWMGA55aCjxl/4JKBh4xxjwTPdEVZf7pDzLlTFESgWmVgTHmzjDW/AArBTVwWy2wYaaCKUo80jesloGSmGgFsqJEQJ/LrTUGSkKiykBRIqDfNabVx0pCospAUSJgoc4/VpTpUGWgKGHi9RoGRsYWZcdSJfFRZaAoYTI4OobXaPWxkpioMlCUMNEmdUoio8pAUcJkMU85UxIfVQaKEib9/iZ1ahkoiYcqA0UJkzPtq9UyUBIPVQaKEiZ9ahkoCYwqA0UJkz61DJQERpWBooSJxgyUREaVgaKESb9rjGSH4EzRr42SeOhZrShh0u9yk5OeooNtlIRElYGihIm2r1YSGVUGihIm2qROSWRUGShKmPS7xshO0+CxkphMqwxE5GERaRORoPOLRWSLiPSKyB779sWAfTeIyBEROS4in4+m4Ioy3+j8YyWRCccy+AFwwzRrXjXGnG/fvgIgIknAt4AbgbXAnSKydjbCKkos8QWQFSURmVYZGGNeAbpm8NqbgePGmFpjzCjwU+CWGbyOosQFahkoiUy0YgaXiMheEfmtiKyzt1UCpwPWNNjbgiIi94nIDhHZ0d7eHiWxFCU6eLyG/pExLThTEpZoKINdwFJjzAbgv4CnZ/IixpgHjTE1xpia4uLiKIilKNFjYERbUSiJzayVgTGmzxgzYN/fCqSISBHQCCwJWFplb1OUiDjRPsBN//Eq7f0jMZPhTCsKVQZKYjJrZSAiZWKXZIrIZvs1O4HtwCoRWS4iqcAdwC9n+37K4mP7yS4ONvexrbYzZjKcaV+tbiIlMZn2Z46IPApsAYpEpAH4EpACYIz5LvB+4BMiMgYMA3cYYwwwJiKfBJ4FkoCHjTEH5uQolISmqWcYgANNfdy8oSImMujISyXRmVYZGGPunGb/A8ADIfZtBbbOTDRFsWjqdQFwoKk3ZjL0DaubSElstAJZiXt8lsH+xl4so3P+6R9RZaAkNqoMlLinqWcYEegectNsWwnzjbqJlERHlYES1xhjaOp1UbM0H7Csg1hwRhmoZaAkJqoMlLimc3CU0TEvV68uxSGwv6kvJnL0udykJjtwpiTF5P0VZa5RZaDENc09lltoRXEmK4uzOBijIHLf8JgWnCkJjSoDJa5ptIPHlXnprKvIYX9jbCwDa5aBxguUxEWVgRLXNPdayqA818n6ylxa+lx0DMx/JbI2qVMSHVUGSlzT1DNMWrKDgsxU1lXkAlbx2XzT73Jr9bGS0KgyUOKaph4XFXnpiAhrK3KA2GQUqUiHv/EAACAASURBVGWgJDqqDJS4pql3mIo8JwC56SlUF2TEpBK5T+cfKwmOKgMlrmnqGaY8N93/eH1lTozcRDrLQElsVBkocYvb46Wtf4SKvDPKYF1FLnWdQ/TZLaUDGR3z8qGH3uSZ/S1RlWPM42Vo1KOWgZLQqDJQ4paWXhfGQEWu079tnR03OBjEOti6r5nXjnfwu0OtUZXjzGAbtQyUxEWVgRK3+PoQTbQMYHIQ2RjDQ6/VAtYwnGiirSiUxYAqAyVu8XUrDVQGxdlplOakTYobvHmyi/2NfRRkpnKifTCq3U17/e2r1TJQEhdVBsqMGRwZ45YHXmPHqa45ef2mXp8ycI7bvr4id1JG0UOvnqQgM5V7L19O77CbzsHRqMlxZsqZWgZK4qLKQJkx+xt72dvQy1tzpQx6hsnLSCEjdfxFeF1lLsfbBhge9QBQ2z7A7w638qGLl/rdSLXtg1O+tsdrGPN4w5LjzPxjtQyUxEWVgTJjDrf0A9DWNzftIZp6XOPSSn2sq8jBa+BQi+Uq+p/fnyLF4eCei5eysjgTmD5u8MVf7OfDD78Vlhx+yyBdLQMlcZlWGYjIwyLSJiL7Q+y/W0TeFpF9IvK6iGwI2HfK3r5HRHZEU/CJDIyM8clHdvHEzoa5fBslgEPN1sW4fY56BTX1DFM5wUUEsL7yTFuKnqFRfr7zNLdurKA4O42K3HScKQ5OtE2tDF4/0cnu+h683uljC2oZKIuBcCyDHwA3TLH/JHClMeZc4KvAgxP2X2WMOd8YUzMzEcMjMzWJIy39/HBb3Vy+jRLAIdsyaJ8zy2A4qGVQkeskPyOFA429/OTNelxuLx+7bAUADoewoihrSstgYGSMU52DDLs9tPZPPzmtT7OJlEXAtMrAGPMKENIpbIx53RjTbT/cBlRFSbaIEBHu3FzN3tM9QXPQleji8RqO+pTBHFgGAyNj9LnGxmUS+RAR1lXksru+h/99/RSXryrinLJs//4VxZmcmCJmcKSlD1+y0cmOqWMLYFkGzhQHKUnqVVUSl2if3R8Dfhvw2ADPichOEblvqieKyH0iskNEdrS3t8/ozd+3qZLUZAc/3V4/o+fHO6c6BrnkH3/H0db+WItCnf3LOseZTHt/9JVBc0/wTCIf6ypzONLaT1v/CPdevmLcvpXFWTR0D+Fye4I+NzAtNTxloK0olMQnaspARK7CUgafC9h8mTFmE3AjcL+IXBHq+caYB40xNcaYmuLi4hnJkJeRyk3ry3hqd6M/0ySR+MWeJpp7Xew53RNrUfzB48tWFTEwMsbQ6FhUX78pSMFZIOvtrKFVJVlcsapo3L6VJVl4DdR1DgV97sGmPvIyUkhLdnAqTGWgaaVKohMVZSAi5wEPAbcYYzp9240xjfbfNuApYHM03m8q7thcTb9rjN/sa57rt5p3nj1g9dzxjYKMJYea+3AIvGOldSGOtnUQrOAskI3VeSQ7hE9sWYmIjNs3XUbRweY+1lXksKwwMyzLoE+nnCmLgFkrAxGpBp4E7jHGHA3Yniki2b77wHVA0IykaHLR8gJWFGXy6FuJ5So63TXEQTt7xzf9aypOdQzSOzS5mVu0ONTcz4riLJYUZADQFmVl0NwzjEOgNDst6P6q/Ay2//U1vG/T5BDViqIsgKAZRWMeL4db+llbnsOyoowwlYHOMlASn3BSSx8F3gDOEZEGEfmYiHxcRD5uL/kiUAh8e0IKaSnwmojsBd4CfmOMeWYOjmGivNy5uZqddd1x4VuPFj6roCgrze9CmYq7H3qTv/3F3Onewy19rCnPoTjLulhH2zJo7HFRmuMkeYqgbX5matDt6alJVOalB7UMTrQPMjrmZW1FDsuLsqjvGpq2+EynnCmLgWl/7hhj7pxm/73AvUG21wIbJj9j7nnfpkr+5dnDPPpWPV+6eV0sRIg6zx5oYXVZNtUF0/+adbk9NPYM0zvsZnTMS2pydPME+lxuGrqHuXNzNSU5c6MMrLTS4MHjcFhRnEltkM/pYLPVxmJdRS6jY17cHkNTj4vqwoyQr9XvGtOCMyXhSchcucKsNK5fV8aTuxpDZpQsJNr7R9hR180N68uoyEv3d/MMRUO35UYaGBljW23nlGtnwhE7eLy2PIf8jFSSHEJbGPn6kdDcOxwyXhAOK4uzONE2MKlh3cGmPlKTHawoymS57U6q7Zi6QK1fYwbKIiAhlQHAnZur6R12R33QSSx44VArxsD168ooz3UyMDLmr4oNRmPP8LjnRhtf5fHq8mySHEJhZmpULQNjDE29rtkpg5IsBkc9tE4oiDvY3MfqsmySkxwsL7ICzVNZWqNjXlxuL9lpahkoiU3CKoNLVhSytDAjIQLJz+xvobogg9Vl2ZTbF8iprING2zI4tzKXFw62RrWdM1jB49z0FMpyLDdOSU5aVAPInYOjjI55xw21iZRgGUXGGA429bG23BqQU5SVSlZa8pTppWdaUagyUBKbhFUGDodwx4XVvHmyK+rDTuaTPpeb1090cMP6MkTE70dv6gmdUdTQPUSyQ7j7omqael3+LKRoYQWPs/0pncVZaVG1DHzHVj5LNxGMVwbNvS66h9ystaeliQjLi4LHFnycGWyjbiIlsUlYZQDw/guqSHYIP9t+OtaizJiXDrfh9hiuX1cK4FcGU1oGPcOU5zm5Zm0pIvD8wei5irxew5GWflaX5fi3lWQ7o2oZNNl1FJWzUAYl2WlkpSWPa2Xta1PiG50JsKwok1Od0yuDnHRVBkpik9DKoDg7jY3Veeypj33F7kx57kCrdRxL8gEozXEicqZdQzAauoepzEunKCuNTdX5UY0b1HcNMTTq8btawPqcOwdG8ITRATQc/JbBLNxEIsLK4sxxlsHB5j5E4JwARba8KJPG7mFGxoInGqibSFksJLQyAKuCtaVvZpkujT3D/MXP90a91UK4uNweXjrSxnVrS3E4LJdMSpKDkuypaw0au4epyrdSJa9ZU8r+xr6wCtXC4XDLmeCxj5KcNLwGuqI0XaypZ5i0ZAcFIeoIwsWXUeTjYFMfywozyQoIBi8vysBrrKK+YGjHUmWxkPDKoCzHSUuva0ZB1O+9UsvjOxvYfqp7+sVzwGvHOhga9XD9urJx28tz00Ne3EfHvLT2u/wulmvXWu6lF6LkKjrY3I9D4OzSM8rAV3gWrfTS5l5L/oltJiJlZUkWTb0uBkesC/rB5r5xFg1wJr00RJdTn2XhC5YrSqKS+Mog18moxxvxr1aX28PTexoBOBzlAGy4PHughWxnMhevKBy3vSLPGbI/UXPvMMZAVb6lDFYWZ7K8KJPnD7VFRabDzX0sL8rEmZLk31acHd3CM1/MY7asCEgd7XO5qe8a8gePfSwvtNaEihu8eqydteU5FGYFb4uhKIlCwiuDcAKuwXjuYCs9Q26SHOLPq59PxjxeXjjUyjVrSidVEFuWQXBrx5dWWmkrAxHhmjUlvHGiY8rahHA51NLH6gm/rkuyrc84WkHk5t5hKoIMtYmUlSVnMooO2cHjicogNyOFgszUoLUGgyNj7Kzr5oqzZ9ZFV1EWEgmvDMrsi0prhHGDx7afpjIvnStWFXGoef57HL11sovuIbc/iyiQ8lwnw24PvcOTL+6+6uOqvDPtFa5dW4bbY3j1WMe4tS63h0//dDd3PrgtLJn6XW5Odw1PcrVE0zIYHfPS1j8yq7RSH0sLM3CI1Y/Il167boLsAMsKg7f4eONEJ26PmdQiW1ESkcRXBjmRWwanu4Z47XgHt9csYW1FDifaB0Jmm8wVP9/ZQHZaMleeXTJpn68ytymIq6jB7vZZFpCJs6k6j/yMlHEppj1Do9zz/Td5ek8Tb9R2hmU1+Br/rQ6YKgZWY7jstOgMuWntc2EMQWcfR0pachLVBRmcaB/gYFMfRVmpfsUVyPKirKDK4JVj7aSnJHHBsvxZy6Io8U7CK4Pi7DSSHEJLBMrg5ztOIwIfqKliTXkOY17D8WkGrEeT7sFRfrOvmfduqiQ9NWnS/jK/62tyELmxe5jSHOc411JykoOrVpfw4uE2xjxeGrqHeP9332Dv6V5us1tAh9PK+aBtIU10E4H1OUdDGZxJK529ZQBnMooONltdVoMFpZcXZdDaN+IPNPt45Wg7l6wsJC158v9AURKNhFcGSQ6hJDst7PRSj9fw850NXLGqmIq8dNbYF77pXEVt/a6oWQ9P7GpgdMzLXRdVB93v86cHSy9t6B7yB48DuXZNKb3Dbv73jTre++3Xaetz8aOPbeYTW6yRkeFUaR9u7iPHmRy0TUS0lEHzNBPOImVlSRa1HYMcax2YFC/w4csoCgwi13cOcapzSF1EyqIh4ZUBWIVa4VoGrxxrp7nXxR0XLgFgWWEmzhTHlEHkwZExrv7Gy1z/b6/wxonZdQk1xvDIW/Vsqs4bV+UbSHF2GskOCVp41tgzHLRy94qzi0lNcvDVXx8kxSE8/ol3cNGKQqoLMklySMjUykAONVvB42C/rouz06KSWto4zezjSFlRlMnomJdRj5d19qjMiSwrsuIrpzrO1Bq8csyaw325Bo+VRcKiUAbluc6wi64e236awsxU3rnGCtwmOYRzSrP9xVbB2FnXzcDIGN1Dbu783jY+/8TbQYO74fDmyS5q2we566KlIdckOYTSHOekOMiYx0tzr8tfcBZIZloy795QzoYleTz5x5f66wRSkx1+v/pU+NpQTAwe+4iWZdDQPUx+RgoZqdEp8vJlFAEhZV9W6EtBPfMZvHK0ncq8dH96qqIkOotCGZTlOie1Mg5Gx8AIzx9s5X2bKsf53NeU53CouT9k4dq22k6SHMLvPnMlf3TFCh7bcZprvvkyz+yPfA7zI2/Wk+NM5t3nlU+5rjzXOalZXWu/1RKiMoibCOBfP7CBX9x/6bjgMli/nk+0TW0ZNHQPMzjqmRQ89lGS7WRw1DPJ7x4pjT3DIeWfCb6Gdc6UMy2rJ5KZlkxZjpOTtmXg9nh5/UQnV5xdPOvCN0VZKISlDETkYRFpE5GgcxTF4j9F5LiIvC0imwL2fUREjtm3j0RL8Egoy5l+BgDAU7saGfMaPmi7iHysLsuma3A0ZB79mye7OLcyl6KsNL5w0xp+cf9lFGel8fEf7+Ku723j+YOtYfXt6Roc5Zn9LbxvU9W4oq5glAcZctNgt1QI1eAt1IVtZUkWJzsHp5TRNyEsWPAYopde2tg9NC4tdrYUZKaSn5HC6rIckhyhL+zWPGTLMthzuoeBkTGuPFvjBcriIVzL4AfADVPsvxFYZd/uA74DICIFwJeAi4DNwJdEZN7z9Hy/hKeKGxhj+On2ei5Yms9ZJeN//Z4JIk92FQ2NjrH3dM+4KuFzq3L5xScv5W/etYaTHYP8vx/uYMs3XuLBV05MOaT+8Z2nGfV4uTtE4DiQitzJbTZ8/vZgAeSpWFls+dV9BWvBONDUR5JDprAMbGUwMHNlYIyJumUA8P+uWMEfvGPZlGuWF2VxqtNSpq8cbSfJIVyyUpWBsngISxkYY14BuqZYcgvwQ2OxDcgTkXLgeuB5Y0yXMaYbeJ6plcqc4EtTnKrWYFd9NyfaB/lgzZJJ+3yB3GAZRbvqehjzGi5aUTBue0qSg3svX8Grn72Kb9+9ifLcdL629TAX/eML/P2vD04ax+n1Gh596zQXLstnVWnwC+74Y7LabHQGtNnwXcwjzcRZEaT3/0QONPVxVnFWSIvFZxm0heGOC0XX4Cgut3dWrauD8cdbzuLWjZVTrllelEHX4Cg9Q6O8crSd85fkkattq5VFRLRiBpVA4NCABntbqO2TEJH7RGSHiOxob2+PklgWvsKzqdJLf3eojWSH8K4gvvrcjBQq89KDWga+eEHN0uAGT3KSg5vOLeexP7qErX96Oe8+r4KHXjvJrd/6PcfbziiXN2o7OdkxGDKddCL+iWcBhWcN3cMUZ6dN62KaSLBBMBPZ39g7bg7ARPyWwSwyinyWTbQtg3DwpZfuru/h7cZerlilWUTK4iJuAsjGmAeNMTXGmJri4uh+EUtyrAvVVG6iY20DLCvKJDPErNs15dlBlcGbJztZX5kb1iSstRU5fOMDG/ifj15Ie/8IN//X7/nZ9nornfTNevIyUrhx/dSBYx/+iWcBWVKh0kqnoyAzlbyMFE6ESC9t7x+hrX8kZJ4+QH5GKkkOmVV/In9fpShbBuGw3E4v/dG2OoyBKzReoCwyoqUMGoFA/0qVvS3U9nnFmZJEYWbqlG6iE+0DnFWcFXL/6rIcajsGx7l3hkc97Dndw8XLC0I+LxhXnVPCbz91ORur8/jcE/v4xI938dzBFm4LI3Dsw+/6CsgoClVwFg4ri7OoDWEZHGiygsfrK4Pn6YM1ZrQoK3VWAWR/X6UYWAZLCqw+Ri8daSM3PYXzqvLmXQZFiSXRUga/BD5sZxVdDPQaY5qBZ4HrRCTfDhxfZ2+bd0pznCGb1Y2OeanrHGJlSeic8jXlOXgmtKXYXd+N22MmtZgOh5IcJz/62EX85fXn8PyhVtwew52bw3MRARRmppKa5PArOK/X0NTjmrGLxZoKFtwyOBCi4+dESrKdswogN/YMk5WWHBNffVpyEpX56RgDl51VNGXmkaIkImFV9ojIo8AWoEhEGrAyhFIAjDHfBbYCNwHHgSHgo/a+LhH5KrDdfqmvGGOmCkTPGeW5zpDTwerstMqzSkJbBmvsyV4Hm/v8v5C31XbiEKiZYSOzJIdw/1VncelZRZzsGJjy/SficAhlAcfUMTDCqMdL1QxdLCuKs3hsRwO9w+5JF+MDTb1UF2SQM40rrDg7LaIeUBPxjeuMVW7/8qIsTncNq4tIWZSEpQyMMXdOs98A94fY9zDwcOSiRZeyXCe76oNPLPMFTs8qDp3Fs9RuS3E4IKNo28musOMFU3H+kjzOXxK5W6I81+l3E532u1hmlqPvCyLXtg+wsXq8cjvQ1Ddl8NhHSXYa+xp7Z/T+EP2Cs0hZUZTJK0fbuVyDx8oiJG4CyHNNWY6T7iH3pJROwO/6WVEc2k2U5BDOKcvxB5Fdbg976nu4KMJ4QTSpCCg8m20mju/YJ7qK+lxu6jqHwlIGxdlpdA6MhFVgF4zG7qGYBI993HPJUr723nOj1iRPURYSi0cZ2Nk3weIGx9sGqMh1hswk8rG2PJtDLX0YY9hd38OoxzujeEG0KM+14iAer6Ghe+rq4+moLsgg2SGTgsi+CWHrpgge+yjJTsNroHMw8rhBn8tNn2ssppbByuKssFN7FSXRWDTKYKrCs+PtA+MamoVidVkOPUNuWvtG2FbbiQjULIudZVCel86Y19AxMEKj3eBtOoUWipQkB9WFkxvW+YLH4VoGMLPCs1imlSqKsoiUQVlu8FoDr9dwom0wrOBtYFuKN092sq4iJ6ZVqr65Ak09w1Hxt68szprkJtrf1Etxdpp/zvFUFM+iJUVjDNNKFUVZVMrAushMrEJu7nMx7PaEpQxW2xlFe073sKu+h4uWx85FBIETz1w0dA/PusHbyuIs6joHGfN4/dsOhhk8BvwKYya1BrGsPlYUZREpg6y0ZLLTkidZBr7g8copCs585DitthQ/33Ga0bHYxgsgYOJZzzCN3bO3DFYUZ+L2GH9mksvt4VjbQNjKYDadSxt7hklNdlCUOXlGsaIoc8+iUQZg/ZKeOOTmhK0Mws3xX1OeQ1OvCxHYHMN4AUBeRgrOFAcHm/oYdntm7WIJTC8FONraj8drWB9iQthEnClJZDuTZ6YM7BoDhxZ7KUpMWHTKoGVCcPN4+wB5GSkUZqaG9RprbVfRmrIccjNi29VSRKjITeetU1Yd32yDryv96aWWMtjf6Aseh6cMYObjLxtm2FdJUZTosLiUQY6TlgmWwfG2AVYWZ4Vd9eob7jKxZXWsKM9zBvT0mV3MIC8jlcLMVP/UswNNvWQ7k1lSEP5FumSG4y99loGiKLFhUSmD8lwn7f0j4wKkJ9qmblA3kQuW5pPtTOb6dWVzIWLE+FJmITrB15XFWdTaE78ONPWxtjwnovYQxdnOiJWBy+2hY2BEM4kUJYYsKmVQmuvEa86kPnYPjtI5OBpRT6DSHCf7vnx9zIPHPnzppdlRavC2wm5Y5/EaDrf0TdmpNBjFWWkRt7HWTCJFiT2LShmUB6RiQkBPogiUQbzhG3ITrQvpyuIsugZH2VnXjcvtDTuTyEdJThpDox4GR8bCfo4WnClK7FlUyqAsx641sJVBJGml8YpPwUXLxeLrUfTLvdbYiUiCx2BZBkBE1oFaBooSexaXMghiGaQlOxb0RcgXM5ht8NiHTzFu3ddCWrLDn2EULr6pcpHEDRq7h0lyiH88qaIo88+iUgb5GSmkJjv8zeqOtw2wojhrQQ8yqcpPJyM1iXPKQrffjvT1UpMcdA2Osrosm+SkyE6RmRSeNfYMU5bjjPi9FEWJHjPrarZAERFrBoDPTdQ+wPlLZjaYJl7ITEvm//5iCwVh1klMR3KSg6WFGVblcYTBYzjTkiKSWoOG7qEFbZ0pSiKw6H6Kldq1Bi63h4bu4YjdIPFISZR/VftcRZEGjwHy0lNIdgitEXQubewenvGENkVRokNYVxARuUFEjojIcRH5fJD9/yYie+zbURHpCdjnCdj3y2gKPxPKc5209LmobR/EmIWdSTRX+ILIkQaPwRrHeU5ZNrtDTJWbiNvjpaVv5rObFUWJDtO6iUQkCfgWcC3QAGwXkV8aYw761hhj/ixg/Z8AGwNeYtgYc370RJ4dZblOWntHONZmja9UZTCZ69eVcbR1wD/3OVIuW1XEw6+dZGBkjKxp5iu09LrwGk0rVZRYE45lsBk4boypNcaMAj8Fbpli/Z3Ao9EQbi4oy3Ey6vGy/VQXDoFlhQvfTRRtNizJ46GP1JCWnDSj51+xqhi3x/Bmbee0azWtVFHig3CUQSVwOuBxg71tEiKyFFgOvBiw2SkiO0Rkm4jcGupNROQ+e92O9vb2MMSaGb68/N8f72RJQQbOlJld8JTQXLA0H2eKg1ePdUy7VgvOFCU+iHYA+Q7gcWNM4NT5pcaYGuAu4N9FZGWwJxpjHjTG1BhjaoqLi6Ms1hl8Q25OdgxG1JNICR9nShKblxfy6rHplbrPMtAh9IoSW8JRBo3AkoDHVfa2YNzBBBeRMabR/lsL/B/j4wnzTmBhk8YL5o4rVhVxon2Qpp7hKdc1dA9RnJ2mFpqixJhwlMF2YJWILBeRVKwL/qSsIBFZDeQDbwRsyxeRNPt+EXApcHDic+eT4uw0f5HZQm5DEe9cvsqy7l6bxlXUqHMMFCUumFYZGGPGgE8CzwKHgMeMMQdE5Csi8p6ApXcAPzXGmIBta4AdIrIXeAn4p8AspFiQ5BBK7CrZlWoZzBlnl2ZRkp3GK9O4iqIxrlNRlNkTVgWyMWYrsHXCti9OePzlIM97HTh3FvLNCaU5VhWyuonmDhHhslVFvHi4DY/XBG354fUamnpccTMbQlEWM4uuAhmsNMaS7LSo9P9XQnPFqmJ6htwcaOoNur9jYIRRj1ctA0WJAxZVbyIff3ndOXQORj6aUYmMS88qAuDVYx2cV5U3aX9Dj29cpyoDRYk1i9IyWFaUyQVL42OGcSJTnJ3GmvKckCmmDf4ag+i031YUZeYsSmWgzB9XrCpiZ1130Mln/oIztQwUJeaoMlDmlMvt1hRvnewat90Yw+76bnLTU6btX6QoytyjykCZU2qW5ZOW7BiXYur1Gv7m6f08d7CVuy+qjqF0iqL40J9kypxitaYo8PcpGvN4+ewTb/PkrkY+sWUlf3n9OTGWUFEUUMtAmQeuWFXM8bYB6juH+NRP9/DkrkY+c+3ZfPb6cxBZuCNHFSWRUMtAmXMuP7sItsIHH3yD5l4Xf/OuNdx7+YpYi6UoSgCqDJQ555zSbIqz02judfH3t67nQxcvjbVIiqJMQJWBMueICN/4wAa8xnDVOSWxFkdRlCCoMlDmhSvPnrsZFYqizB4NICuKoiiqDBRFURRVBoqiKAqqDBRFURRUGSiKoiioMlAURVFQZaAoiqKgykBRFEUBxBgTaxkmISLtQN0Mn14EdERRnNmi8kxNvMkzW+LteFSeqUkkeZYaY2Zc3RmXymA2iMgOY0xNrOXwofJMTbzJM1vi7XhUnqlRec6gbiJFURRFlYGiKIqSmMrgwVgLMAGVZ2riTZ7ZEm/Ho/JMjcpjk3AxA0VRFCVyEtEyUBRFUSJElYGiKIqiymAhIDo1fkr085lb9POdmkT5fFQZ2IhIXH0WIrJERFYCGGNMrE84EblYRG6IpQyBiEiRiOSC//OJq/9fpMSb/Hr+TU2inX+gygAAEbkauEtE8mMtC4CI3AT8FviWiPwWYvuFFJHrge8woTIyhvLcCGwFvicijwMYY7yxkCUa6Pk3rTx6/k0v0xUictmslJIxZlHfgEsBL/A88EEgP8bybATeBi6xHz8K5MVQni1YX8KN9uMM7Cw0+7HMszxXAYeBa4Fc4Bng87E+j2ZxPHr+TS2Pnn/Ty3SlfQ69AVwCOGbyOskz1iIJgIgkA/lYX0ID3AI4ROQZY0y3vUaM/YnPE17gJWPMGyJSBVwNfF1EioG7jDFD8yWT/curCKgFUkQkC/gBMGzfv9MY45oPeWxZUoG1wBeMMc/b278PnD+X7z1X6Pk3NXr+hSVXKnAWcBtQDnwR+HsRecNEaK0samVgjBkTkReAJGPMoIikATdifSF/a4zpmucvIsAIsFpE/gt4F/BN4AGsL8FTwPXzJZMxxthuglHgH4ENwD8AT9pyPQXcOB/y2O8xYpvlyQEXgBbg4rl+/7lAz7+p0fMvLLlGReQXgMsYMyAiGcBfA/8oIq8bYzzhvtaiVAYiciVwGbALOGaMOQ5gjPmJ7XO7HmgTkY1YZvsX5kGeS4HdwDbgY1havgD4tjFmEPiAiGwVkWJjTPscGVEedAAACmdJREFUy7PUGFMHYF+kfgekA4XGmG/baz4IPCEiOcaYvjmW51KsL9wRYLcxpjFg9zCQZa+7F6g2xnxxLuWZLXr+TSuPnn/Ty3QxcB7wJtBkjBkAMMZ8w7ZivgD8qYhcjnUOfXO611x0AWQRuRZ4GMv3eC3woIhs8e03xvwI+BHwDeBPgMfmSZ5MrIvAE8BZxpjtwCBwgb3udqAE61fSXMrzHuCkiPytb5t9MXga+O+ApXcBhVjujbmU5932+1ZhuVE+JiLOgOBhLbBXRD4A3Ac8PpfyzBY9/6aVR8+/6WV6F/BTrPjOZ4B/ERF/p1NjzNexzpsXgC8Dz4X1wnMZ2IjHG/Ap4LP2/RzgQ8ABYEvAmvcC/cDaGMqzAXg3sB/r4rAbWD/HspQA/4v1q2Inlm904hoHcI8t17o5lmcVsAPYbD9+J1agtSBgTSGWn/sgsCbW55eef3r+zcP/7MvA3fb9c4BPAr8BLghYcyvQG8k5NOeCx9sNS3v/74RtHwKeBVbYj6+djy/iFPJ82JYnHVhnfzGr50EWAd5h319ln+BfmLAmz/6yzvmJj+XG/BDgDNj2NHBVwOMsrF9JZ8f63JrF/1vPP6PnXwRyfQ34XsDjIlsh/Ld93wn8QaTKcl6Ej6cbVkbAXuAbAdsKsIJkl9uP5y1dbQp5vuOTZ57kmHTMAV/Iv7IfXwNUMMPUtQjlcUx4nGT/fRK42b6/2f4yOudannn4f+v5N3mbnn/BZcsDXgH+ImDb+kCl5JM3ktuiihmISJIxZhQrY+MiEfkmgDGmC0jB9o8a+9OMsTwOnzzzwcRjFpFkY8wxLD/pbSLyLPCfQIqZhwKbIO/hO1dPA80icjPwz0C2McY11/JEAz3/QqPnX3iIiMMY0wN8FrhURD5ny7sf8AAX2o/DziLyv/Y8nXdxg32SjYlIKfBL4CjQjuUffbcx5ugil2dSzraI/B1wP5Zfe38s5RGRL2L51EeBe40x++ZTnpniO454+X/HsTxxcf6FkieW518QWS7EUkingGPAvcA1xpiTM3n9hE0tFZEyY0zLhG0O+8S/CCjDSqe7DStw9r25PPEXiDy+L0ANsMEY830ROQtYDbxzLr+IYciz0RjzPazPZi2WT/3EXMkzW+z/6RrgJLDPGNNl/xKP1f97ocjjidH5N508837+2Wmh52NZI68ZYzoCZFqPVbD4HuCPsbKqbp2pIgASM2aAFUn3An8QZN8lwB7gBpVnSnneaT8WICcO5LnWfpxGnAeLsX5V7wMewkrb/AyQHMP/90KUZz7Pv3DkmdfzD8t1txfrl/+jWNXfvn1X2/Juiep7ztcJMI8nWgVWrvQ3sVLk7pmw/27gJt+JpvJMKU/EQag5licl1udXGMezzr54+HrpvBcrAyUtRv/vhSzPfJx/kcgzL+cfcC5WnyFfZtXfAp/DqnXIxmpf8oFo/8/m/MDm+4ZVzHO1ff8qrKKQe4Ksm/MTTeVZePJE4XgKsdI1kwK2PQ/UxOjzVXkWkDz2e+Vj1wxgpYqeAH4NfAt4BCi290VVec/Lwc3TB1gGlAXZvsW+wHzYfnwFUKTyqDxzcDzlE7b5fl3+FrjYvn8+kKvyqDwhZCqbsO29wEfs+5XA97FTW6P+/vNxkPPwId4GvIrVV+VzWM20AvdfhZWv/AiWSVil8qg883g8DwMrgPfZF5oSlUflmUKmz06UKWDdQwSxnKNxW/CppSJSiNWD4w8BN1b15jlYbXh/FrDuASxf29VmDtPBVJ6FJc9sCed47Pz9jVgFSh81c5sVo/IsIHmmkellY8yjAetuA/4GuM0YUxttORIhtTQJ6ANOGmN6RKQTq1Jxi4i0G2NeFJE1WJr+mnm4sKg8C0ue2TLV8XQaY17Aqui9ANhk7A6lKo/KE4ZMV4hIq/2duA/4M+D9c6EIIEGKzkTkP7C6Ln7KWC1vK7D6q4waY74pVo/vNGMPDFF5VJ5oMsXxuI0x/yoi1UCWMeagyqPyRCiT7ztRBmTMlSKABd7CWs7M+/wWlmb9nIhkGmOasBpt3SIihcaYofm4sKg8C0ue2RLG8bxHrP7/9fNxYVF5FpY8Ycp0i4gUGWNa5lIRwAJVBiJWL3Fzpn/ICawGUunAd0WkCDgbGMPywak8Kk/UiPB4RlQelWeWMs3pDAm/TAvJTSQiBVjj3YYCtqUaa/RbFZav7yNY5eIFwCeMMbtUHpUnGsTb8ag8C0ueeJXJj5njlKlo3bC6F/4KS3veQ0BRCNbQiZ9j91wHcoFMlUflSdTjUXkWljzxKtM4+ebzzWbxIZ6N1YtjLVYR0tex+nVcjtX6dxtWupXKo/Ik/PGoPAtLnniVaeJtoaSWFgENxg7qiEg+Vqe+27Ha7d5ijGkVmdz+VuVReaJAvB2PyrOw5IlXmcaxUALI+4E+Efkb+/FGrL7rLmCZMaYV5m8oiMqz4OSZLfF2PCrPwpInXmUaR9wGkO1gSr8xpldEkrFaFNyPpcCMMeYWEbkTuAGr9fGcHojKs7DkmS3xdjwqz8KSJ15lmpJo+pyidcPqb38Y+HPsDn32dgdWMyffPNL7+P/tnTFrFFEURs9FUwREbdYQVBQREUQQQYsgWAg2io2NaIrY2ZlCAvFfiEEwhaaw0EKw0T6dIBIQxCKNiBJREGMv1+JNcNliN7Mzmblv+A4Mu7M7MOfb5vLm7XsXHshHPl3OI5+8fKI6jTrCjQzMrEdq7PwF+Ar8AJ67+8+B6+aB28Cs7+xeOvLJyKcq0fLIJy+fqE7bIeKcwSYwD9wh7Vh5HLhhZgfg/2IN0n4etxr4EeWTl09VouWRT14+UZ1GEmZkYGk/kO+kdnP9CzKuAxeBdXd/aGZnvYFFGPLJy6cq0fLIJy+fqE5lCDEyMLMrwBtgCXhqZie3vnP3l8Aq0DOzV8CqmR2Uj3zqIloe+eTlE9WpNG1OWAAGHKZo7gxMAfeADeDUwLXPgM/AafnIp4t55JOXT1SnsbO0LpCemy2TWrptPba6C3wDThTn06TOV2fkI58u55FPXj5RncbK0dqN06TKOVJD6hfAwsD3C8AKMFmc75GPfLqaRz55+UR1qpSnlZvCVeAD6TnaEnCNNHxa7LvmKPCYotLKRz5dzSOfvHyiOlU9Gt+byMxmSJs03XT3NTNbBs4DM8BbM9tF+o/uBVLruf3AjjU6kU9ePlWJlkc+eflEdaqFpqsP6Qeb6zvvAa+L98eAJ8Aj4D0NTLTIJy+fruWRT14+UZ1qydX4DdNky96+94eANWC6+OwIsBvYJx/5dD2PfPLyiepUx9H4OgN3/+vuf4pTA34Dv9x9w8xmgfvAhLtvykc+dRMtj3zy8onqVAchViCb2Qrpf7mXScOvVpdnyycvn6pEyyOfvHwgplNZWi0GxR4dE8Cn4vWSu6/LRz5NEC2PfPLyieo0LlFGBnPAO3f/2LYLyGcU0XyqEi2PfIYTzQdiOpUlSjEI1f5QPsOJ5lOVaHnkM5xoPhDTqSwhioEQQoh2CbFrqRBCiHZRMRBCCKFiIIQQQsVACCEEKgZCCCFQMRBCCAH8A4Cwg0cQPn2xAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYoqNGDGzGL_"
      },
      "source": [
        "If we do not specify which change directions we are looking for, `CUSUMDetector` will look for both increases and decreases.  In the case below where there is only an increase, it will detect that increase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V15v-Q9ezGMA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "1343554c-71a7-4bf5-b313-3bfcf4c7c8e0"
      },
      "source": [
        "# detect increase\n",
        "timeseries = TimeSeriesData(\n",
        "    df.loc[:,['time','increase']]\n",
        ")\n",
        "detector = CUSUMDetector(timeseries)\n",
        "\n",
        "# run detector\n",
        "change_points = detector.detector()\n",
        "\n",
        "# plot the results\n",
        "plt.xticks(rotation=45)\n",
        "detector.plot(change_points)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": "markdown",
      "metadata": {
        "id": "sgl6WjkyzGMB"
      },
      "source": [
        "# 4. Feature Extraction with Kats\n",
        "\n",
        "We provide the `TsFeatures` module to calculate a set of meaningful features for a time series, including:\n",
        "- STL (Seasonal and Trend decomposition using Loess) Features\n",
        "    - Strength of Seasonality\n",
        "    - Strength of Trend\n",
        "    - Spikiness\n",
        "    - Linearity\n",
        "- Amount of Level Shift\n",
        "- Presence of Flat Segments\n",
        "- ACF and PACF Features\n",
        "- Hurst Exponent\n",
        "- ARCH Statistic\n",
        "\n",
        "Given a collection of time series, these features can be used to identify specific series that are similar or outlying.  This `TsFeatures` module is similar to the one that is [freely available in R](https://pkg.robjhyndman.com/tsfeatures/index.html).\n",
        "\n",
        "These features also play a crucial role in many downstream projects, including \n",
        "1. “Meta-learning”, i.e., choosing the best forecasting model based on characteristics of the input time series \n",
        "2. Time series classification and clustering analysis\n",
        "3. Nowcasting algorithms for better short-term forecasting\n",
        "\n",
        "Now we will show you how to use `TsFeatures` get the features for the `air_passenger` data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sRZnZWYzGMC"
      },
      "source": [
        "# Initiate feature extraction class\n",
        "from kats.tsfeatures.tsfeatures import TsFeatures\n",
        "tsFeatures = TsFeatures()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xzKNavYuzGMC"
      },
      "source": [
        "features_air_passengers = TsFeatures().transform(air_passengers_ts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQqj10CgzGMC"
      },
      "source": [
        "Now we can see the dictionary of features as follows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MXuyIZHzGMG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90ecf9dc-aafc-4c5d-d7b2-aec492d39c87"
      },
      "source": [
        "# features_air_passengers\n",
        "features_air_passengers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'binarize_mean': 0.4444444444444444,\n",
              " 'crossing_points': 7,\n",
              " 'diff1y_acf1': 0.30285525815216935,\n",
              " 'diff1y_acf5': 0.2594591065999471,\n",
              " 'diff1y_pacf5': 0.21941234780081417,\n",
              " 'diff2y_acf1': -0.19100586757092733,\n",
              " 'diff2y_acf5': 0.13420736423784568,\n",
              " 'diff2y_pacf5': 0.2610103428699484,\n",
              " 'entropy': 0.42873655617524487,\n",
              " 'firstmin_ac': 8,\n",
              " 'firstzero_ac': 52,\n",
              " 'flat_spots': 2,\n",
              " 'heterogeneity': 126.06450625819339,\n",
              " 'histogram_mode': 155.8,\n",
              " 'holt_alpha': 0.9950208833037283,\n",
              " 'holt_beta': 0.0042554614726079635,\n",
              " 'hurst': -0.08023291030513457,\n",
              " 'hw_alpha': 0.9999999850988388,\n",
              " 'hw_beta': 1.4661372087721957e-14,\n",
              " 'hw_gamma': 1.3205834372066383e-08,\n",
              " 'length': 144,\n",
              " 'level_shift_idx': 118,\n",
              " 'level_shift_size': 15.599999999999966,\n",
              " 'linearity': 0.853638165603188,\n",
              " 'lumpiness': 3041164.5629058965,\n",
              " 'mean': 280.2986111111111,\n",
              " 'peak': 6,\n",
              " 'seas_acf1': 0.6629043863684492,\n",
              " 'seas_pacf1': 0.1561695525558904,\n",
              " 'seasonality_strength': 0.3299338017939569,\n",
              " 'spikiness': 111.69732482853489,\n",
              " 'stability': 12303.627266589507,\n",
              " 'std1st_der': 27.206287853461966,\n",
              " 'trend_strength': 0.9383301875692747,\n",
              " 'trough': 3,\n",
              " 'unitroot_kpss': 0.12847508180149078,\n",
              " 'var': 14291.97333140432,\n",
              " 'y_acf1': 0.9480473407524915,\n",
              " 'y_acf5': 3.392072131604336,\n",
              " 'y_pacf5': 1.0032882494015292}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PqQ8ExrazGMH"
      },
      "source": [
        "# 5. Summary\n",
        "\n",
        "in this tutorial, we have shown the basic operations of **Kats** in time series application, including basic introductions to forecasting, detection, feature extraction in Kats.\n"
      ]
    }
  ]
}