TimeSeriesForecasting.ipynb

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        "<a href=\"https://colab.research.google.com/github/clizarraga-UAD7/Notebooks/blob/main/TimeSeriesForecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "## Time Series Forecasting"
      ],
      "metadata": {
        "id": "u7mKtiSpgx9e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "gqaWwYJu_jDH"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Import necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "import plotly.express as px\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the monthly means CO2 levels dataset from Mauna Loa Observatory\n",
        "# URL= https://gml.noaa.gov/ccgg/trends/data.html \n",
        "\n",
        "co2data = \"https://raw.githubusercontent.com/clizarraga-UAD7/Datasets/main/MaunaLoaCO2/co2_mm_mlo.csv\"\n",
        "\n",
        "# The file has Metadata first 56 lines\n",
        "# The column names are in line 57\n",
        "df_dist = pd.read_csv(co2data, header=0 , skiprows=56)\n",
        "\n",
        "\n",
        "# Print the dataframe head.\n",
        "df_dist.head()\n"
      ],
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          "height": 206
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        "id": "HDnsHN1IgxbX",
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      "execution_count": 2,
      "outputs": [
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              "   year  month  decimal date  average  deseasonalized  ndays  sdev   unc\n",
              "0  1958      3     1958.2027   315.70          314.43     -1 -9.99 -0.99\n",
              "1  1958      4     1958.2877   317.45          315.16     -1 -9.99 -0.99\n",
              "2  1958      5     1958.3699   317.51          314.71     -1 -9.99 -0.99\n",
              "3  1958      6     1958.4548   317.24          315.14     -1 -9.99 -0.99\n",
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              "     year  month  decimal date  average  deseasonalized  ndays  sdev   unc\n",
              "775  2022     10     2022.7917   415.78          419.13     30  0.27  0.10\n",
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      "source": [
        "df_dist.info()"
      ],
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        },
        "id": "-uuXgpK-h3i4",
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      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 780 entries, 0 to 779\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   year            780 non-null    int64  \n",
            " 1   month           780 non-null    int64  \n",
            " 2   decimal date    780 non-null    float64\n",
            " 3   average         780 non-null    float64\n",
            " 4   deseasonalized  780 non-null    float64\n",
            " 5   ndays           780 non-null    int64  \n",
            " 6   sdev            780 non-null    float64\n",
            " 7   unc             780 non-null    float64\n",
            "dtypes: float64(5), int64(3)\n",
            "memory usage: 48.9 KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Create a dataset from year 1990 to present\n",
        "df_last30 = df_dist[df_dist['year'] >= 1990]\n",
        "df_last30.head()\n"
      ],
      "metadata": {
        "colab": {
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        "id": "hCJhIea_mO96",
        "outputId": "b811d57e-6bcb-4467-b01c-ac92b18d6511"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     year  month  decimal date  average  deseasonalized  ndays  sdev   unc\n",
              "382  1990      1     1990.0417   353.86          353.78     25  0.34  0.13\n",
              "383  1990      2     1990.1250   355.10          354.37     28  0.66  0.24\n",
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    {
      "cell_type": "code",
      "source": [
        "# Combine 2 columns into string YearMonth\n",
        "df = df_last30.copy()\n",
        "df['YearMonth'] = df['year'].astype(str) + ' ' +  df['month'].astype(str)\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "ASf1v9hckvEh",
        "outputId": "b6ab4a12-9334-4992-8e73-ed1245991919"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     year  month  decimal date  average  deseasonalized  ndays  sdev   unc  \\\n",
              "382  1990      1     1990.0417   353.86          353.78     25  0.34  0.13   \n",
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              "386  1990      5     1990.3750   357.38          354.23     28  0.30  0.11   \n",
              "\n",
              "    YearMonth  \n",
              "382    1990 1  \n",
              "383    1990 2  \n",
              "384    1990 3  \n",
              "385    1990 4  \n",
              "386    1990 5  "
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using the [Darts Library](https://unit8co.github.io/darts/README.html#)."
      ],
      "metadata": {
        "id": "skkmrXG0g4Ov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the Darts library in Google Colab\n",
        "!pip install --quiet darts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfiz3c0-ie41",
        "outputId": "2372d24b-515d-429a-8515-3d1820604dbe"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.4/572.4 kB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m74.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.2/519.2 kB\u001b[0m \u001b[31m39.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.4/46.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m372.4/372.4 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m187.9/187.9 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.6/154.6 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m248.6/248.6 kB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.2/77.2 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.5/140.5 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/10.2 MB\u001b[0m \u001b[31m92.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m185.2/185.2 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 kB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.3/269.3 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.2/144.2 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.3/135.3 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for plotly-resampler (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fugue-sql-antlr (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import two libraries from Darts\n",
        "from darts import TimeSeries\n",
        "from darts.models import ExponentialSmoothing\n"
      ],
      "metadata": {
        "id": "QNgEB8Fah3sO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a TimeSeries, specifying the time and value columns\n",
        "series = TimeSeries.from_dataframe(df, 'YearMonth', 'average')\n",
        "\n",
        "# Set aside the last 36 months as a validation series\n",
        "train, val = series[:-36], series[-36:]"
      ],
      "metadata": {
        "id": "X2uSfUNWicMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit an exponential smoothing model and make a prediction\n",
        "model = ExponentialSmoothing()\n",
        "model.fit(train)\n",
        "prediction = model.predict(len(val), num_samples=1000)\n"
      ],
      "metadata": {
        "id": "VWXl_P-RkDwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "series.plot()\n",
        "prediction.plot(label=\"forecast\", low_quantile=0.05, high_quantile=0.95)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "pHclSz2JlrJv",
        "outputId": "791e72b5-c7d8-4fc3-ce83-042edaad6de3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using the [PyCaret Library](https://pycaret.gitbook.io/docs/)\n",
        "\n",
        "The example in the tutorial is not reproducible... [See this example](https://pycaret.gitbook.io/docs/learn-pycaret/official-blog/time-series-forecasting-with-pycaret-regression)"
      ],
      "metadata": {
        "id": "4vjunW0-3Ed_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the PyCaret Library\n",
        "!pip install pycaret --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eFCWAvIqlvNB",
        "outputId": "60b03491-60c7-4b80-edfa-29030528d008"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for plotly-resampler (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read Dataset\n",
        "filename='https://raw.githubusercontent.com/clizarraga-UAD7/Datasets/main/AirPassengers.csv'\n",
        "df0 = pd.read_csv(filename)\n",
        "df0.head()"
      ],
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          "height": 206
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        "outputId": "43fc1e02-6d9a-4051-a4b6-25507596b27d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Month  #Passengers\n",
              "0  1949-01          112\n",
              "1  1949-02          118\n",
              "2  1949-03          132\n",
              "3  1949-04          129\n",
              "4  1949-05          121"
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              "      <td>1949-04</td>\n",
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              "      <td>1949-05</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df0.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBbf4OcneycK",
        "outputId": "e3f5d2fa-59b3-4ab0-a532-b2179107601e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 144 entries, 0 to 143\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Month        144 non-null    object\n",
            " 1   #Passengers  144 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 2.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert Month object to Date variable\n",
        "df=df0.copy()\n",
        "df['Date'] = pd.to_datetime(df['Month'])\n",
        "# Drop the Month column\n",
        "df.drop(['Month'], axis=1, inplace=True)\n",
        "# Create a numeric year and month column\n",
        "df['year']=df['Date'].dt.year\n",
        "df['month']=df['Date'].dt.month\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1WrY8OwPYp1P",
        "outputId": "3098375a-0b2f-4fb6-9c31-328d0b48849e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   #Passengers       Date  year  month\n",
              "0          112 1949-01-01  1949      1\n",
              "1          118 1949-02-01  1949      2\n",
              "2          132 1949-03-01  1949      3\n",
              "3          129 1949-04-01  1949      4\n",
              "4          121 1949-05-01  1949      5"
            ],
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              "          document.querySelector('#df-b022a69f-1646-44cf-b1d6-3358416015ed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b022a69f-1646-44cf-b1d6-3358416015ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QfLYsAOYqDz",
        "outputId": "c65069d8-9a25-42be-8eea-e94a44f20aea"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 144 entries, 0 to 143\n",
            "Data columns (total 4 columns):\n",
            " #   Column       Non-Null Count  Dtype         \n",
            "---  ------       --------------  -----         \n",
            " 0   #Passengers  144 non-null    int64         \n",
            " 1   Date         144 non-null    datetime64[ns]\n",
            " 2   year         144 non-null    int64         \n",
            " 3   month        144 non-null    int64         \n",
            "dtypes: datetime64[ns](1), int64(3)\n",
            "memory usage: 4.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create 12 month moving average\n",
        "df['#Pass_12'] = df['#Passengers'].rolling(12).mean()\n",
        "\n",
        "# plot the data and MA\n",
        "\n",
        "fig = px.line(df, x='Date', y=['#Passengers', '#Pass_12'], template = 'plotly_white')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6XIqrZ0C3stl",
        "outputId": "e11364a5-86fb-4106-baf8-6bc8c11f43df"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "df1=df.copy()\n",
        "# create a sequence of numbers\n",
        "df1['Series'] = np.arange(1,len(df)+1)\n",
        "\n",
        "# drop unnecessary columns and re-arrange\n",
        "df1.drop(['Date', '#Pass_12'], axis=1, inplace=True)\n",
        "df2 = df1[['Series', 'year', 'month', '#Passengers']] \n",
        "\n",
        "# check the head of the dataset**\n",
        "df2.head()\n"
      ],
      "metadata": {
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          "height": 206
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        "id": "WlrHwz3A4i0Z",
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            "text/plain": [
              "   Series  year  month  #Passengers\n",
              "0       1  1949      1          112\n",
              "1       2  1949      2          118\n",
              "2       3  1949      3          132\n",
              "3       4  1949      4          129\n",
              "4       5  1949      5          121"
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    {
      "cell_type": "code",
      "source": [
        "# split data into train-test set\n",
        "train = df2[df2['year'] < 1960]\n",
        "test = df2[df2['year'] >= 1960]\n",
        "\n",
        "# check shape\n",
        "train.shape, test.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVcEpByR51WA",
        "outputId": "020ede5c-1d68-4b3e-c1b5-870733b4d424"
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      "execution_count": 76,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "((132, 4), (12, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import the regression module\n",
        "from pycaret.regression import *\n",
        "\n",
        "# initialize setup\n",
        "s = setup(data = train, test_data = test, target = '#Passengers', fold_strategy = 'timeseries', numeric_features = ['year', 'Series'], fold = 3, transform_target = True, session_id = 123)"
      ],
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              "      <th id=\"T_a62fa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_a62fa_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_a62fa_row0_col1\" class=\"data row0 col1\" >123</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_a62fa_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_a62fa_row1_col1\" class=\"data row1 col1\" >#Passengers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_a62fa_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_a62fa_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_a62fa_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_a62fa_row3_col1\" class=\"data row3 col1\" >(144, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_a62fa_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_a62fa_row4_col1\" class=\"data row4 col1\" >(144, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_a62fa_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_a62fa_row5_col1\" class=\"data row5 col1\" >(132, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_a62fa_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_a62fa_row6_col1\" class=\"data row6 col1\" >(12, 4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_a62fa_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_a62fa_row7_col1\" class=\"data row7 col1\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_a62fa_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_a62fa_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_a62fa_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_a62fa_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_a62fa_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_a62fa_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_a62fa_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_a62fa_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_a62fa_row12_col0\" class=\"data row12 col0\" >Transform target</td>\n",
              "      <td id=\"T_a62fa_row12_col1\" class=\"data row12 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_a62fa_row13_col0\" class=\"data row13 col0\" >Transform target method</td>\n",
              "      <td id=\"T_a62fa_row13_col1\" class=\"data row13 col1\" >yeo-johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_a62fa_row14_col0\" class=\"data row14 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_a62fa_row14_col1\" class=\"data row14 col1\" >TimeSeriesSplit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_a62fa_row15_col0\" class=\"data row15 col0\" >Fold Number</td>\n",
              "      <td id=\"T_a62fa_row15_col1\" class=\"data row15 col1\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_a62fa_row16_col0\" class=\"data row16 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_a62fa_row16_col1\" class=\"data row16 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_a62fa_row17_col0\" class=\"data row17 col0\" >Use GPU</td>\n",
              "      <td id=\"T_a62fa_row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_a62fa_row18_col0\" class=\"data row18 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_a62fa_row18_col1\" class=\"data row18 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_a62fa_row19_col0\" class=\"data row19 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_a62fa_row19_col1\" class=\"data row19 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a62fa_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_a62fa_row20_col0\" class=\"data row20 col0\" >USI</td>\n",
              "      <td id=\"T_a62fa_row20_col1\" class=\"data row20 col1\" >09f2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models(sort = 'MAE')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645,
          "referenced_widgets": [
            "ef27d8b4ec024942a801cab46ac9b916",
            "1765951dbd79499abc653c4e1f6bf96d",
            "ceb7efc5ebbc4354a9ae32dac715b588",
            "44a75db2c24b4fefb0f834c300380958",
            "e989dce0a05a4a1ba7d0807be8e97fd1",
            "89ca54af16ce475f936b699cf66977f7",
            "78ca9185ba9a4ec7bfcac4091667d3ff",
            "28bd8d38106b42d594995729f187cafb",
            "6842d78acd9b4af7a026882c0f889a39",
            "0f29eec1e52a451989343f2d78cce1ea",
            "b945325a16554957a839895c80ba165a"
          ]
        },
        "id": "2S1fmTTY7ATU",
        "outputId": "6cc589a6-220a-462d-8805-6c20bcaa1dd1"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
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              "}\n",
              "#T_f86d2_row2_col7 {\n",
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              "</style>\n",
              "<table id=\"T_f86d2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_f86d2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_f86d2_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_f86d2_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_f86d2_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_f86d2_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_f86d2_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_f86d2_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_f86d2_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row0\" class=\"row_heading level0 row0\" >omp</th>\n",
              "      <td id=\"T_f86d2_row0_col0\" class=\"data row0 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_f86d2_row0_col1\" class=\"data row0 col1\" >44.3192</td>\n",
              "      <td id=\"T_f86d2_row0_col2\" class=\"data row0 col2\" >3050.6952</td>\n",
              "      <td id=\"T_f86d2_row0_col3\" class=\"data row0 col3\" >53.8852</td>\n",
              "      <td id=\"T_f86d2_row0_col4\" class=\"data row0 col4\" >-0.4522</td>\n",
              "      <td id=\"T_f86d2_row0_col5\" class=\"data row0 col5\" >0.1714</td>\n",
              "      <td id=\"T_f86d2_row0_col6\" class=\"data row0 col6\" >0.1521</td>\n",
              "      <td id=\"T_f86d2_row0_col7\" class=\"data row0 col7\" >0.1467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row1\" class=\"row_heading level0 row1\" >br</th>\n",
              "      <td id=\"T_f86d2_row1_col0\" class=\"data row1 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_f86d2_row1_col1\" class=\"data row1 col1\" >45.5643</td>\n",
              "      <td id=\"T_f86d2_row1_col2\" class=\"data row1 col2\" >3233.7133</td>\n",
              "      <td id=\"T_f86d2_row1_col3\" class=\"data row1 col3\" >55.6766</td>\n",
              "      <td id=\"T_f86d2_row1_col4\" class=\"data row1 col4\" >-0.6553</td>\n",
              "      <td id=\"T_f86d2_row1_col5\" class=\"data row1 col5\" >0.1765</td>\n",
              "      <td id=\"T_f86d2_row1_col6\" class=\"data row1 col6\" >0.1576</td>\n",
              "      <td id=\"T_f86d2_row1_col7\" class=\"data row1 col7\" >0.1567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
              "      <td id=\"T_f86d2_row2_col0\" class=\"data row2 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_f86d2_row2_col1\" class=\"data row2 col1\" >45.7835</td>\n",
              "      <td id=\"T_f86d2_row2_col2\" class=\"data row2 col2\" >3257.2206</td>\n",
              "      <td id=\"T_f86d2_row2_col3\" class=\"data row2 col3\" >55.8542</td>\n",
              "      <td id=\"T_f86d2_row2_col4\" class=\"data row2 col4\" >-0.6687</td>\n",
              "      <td id=\"T_f86d2_row2_col5\" class=\"data row2 col5\" >0.1770</td>\n",
              "      <td id=\"T_f86d2_row2_col6\" class=\"data row2 col6\" >0.1585</td>\n",
              "      <td id=\"T_f86d2_row2_col7\" class=\"data row2 col7\" >0.0833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row3\" class=\"row_heading level0 row3\" >lar</th>\n",
              "      <td id=\"T_f86d2_row3_col0\" class=\"data row3 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_f86d2_row3_col1\" class=\"data row3 col1\" >45.8053</td>\n",
              "      <td id=\"T_f86d2_row3_col2\" class=\"data row3 col2\" >3260.6009</td>\n",
              "      <td id=\"T_f86d2_row3_col3\" class=\"data row3 col3\" >55.8841</td>\n",
              "      <td id=\"T_f86d2_row3_col4\" class=\"data row3 col4\" >-0.6721</td>\n",
              "      <td id=\"T_f86d2_row3_col5\" class=\"data row3 col5\" >0.1771</td>\n",
              "      <td id=\"T_f86d2_row3_col6\" class=\"data row3 col6\" >0.1586</td>\n",
              "      <td id=\"T_f86d2_row3_col7\" class=\"data row3 col7\" >0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row4\" class=\"row_heading level0 row4\" >lr</th>\n",
              "      <td id=\"T_f86d2_row4_col0\" class=\"data row4 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_f86d2_row4_col1\" class=\"data row4 col1\" >45.8199</td>\n",
              "      <td id=\"T_f86d2_row4_col2\" class=\"data row4 col2\" >3263.7413</td>\n",
              "      <td id=\"T_f86d2_row4_col3\" class=\"data row4 col3\" >55.9068</td>\n",
              "      <td id=\"T_f86d2_row4_col4\" class=\"data row4 col4\" >-0.6728</td>\n",
              "      <td id=\"T_f86d2_row4_col5\" class=\"data row4 col5\" >0.1772</td>\n",
              "      <td id=\"T_f86d2_row4_col6\" class=\"data row4 col6\" >0.1586</td>\n",
              "      <td id=\"T_f86d2_row4_col7\" class=\"data row4 col7\" >1.2233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row5\" class=\"row_heading level0 row5\" >huber</th>\n",
              "      <td id=\"T_f86d2_row5_col0\" class=\"data row5 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_f86d2_row5_col1\" class=\"data row5 col1\" >46.0420</td>\n",
              "      <td id=\"T_f86d2_row5_col2\" class=\"data row5 col2\" >3308.9717</td>\n",
              "      <td id=\"T_f86d2_row5_col3\" class=\"data row5 col3\" >56.4600</td>\n",
              "      <td id=\"T_f86d2_row5_col4\" class=\"data row5 col4\" >-0.8186</td>\n",
              "      <td id=\"T_f86d2_row5_col5\" class=\"data row5 col5\" >0.1802</td>\n",
              "      <td id=\"T_f86d2_row5_col6\" class=\"data row5 col6\" >0.1618</td>\n",
              "      <td id=\"T_f86d2_row5_col7\" class=\"data row5 col7\" >0.1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row6\" class=\"row_heading level0 row6\" >lasso</th>\n",
              "      <td id=\"T_f86d2_row6_col0\" class=\"data row6 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_f86d2_row6_col1\" class=\"data row6 col1\" >51.7106</td>\n",
              "      <td id=\"T_f86d2_row6_col2\" class=\"data row6 col2\" >3939.2790</td>\n",
              "      <td id=\"T_f86d2_row6_col3\" class=\"data row6 col3\" >61.4955</td>\n",
              "      <td id=\"T_f86d2_row6_col4\" class=\"data row6 col4\" >-1.4896</td>\n",
              "      <td id=\"T_f86d2_row6_col5\" class=\"data row6 col5\" >0.2374</td>\n",
              "      <td id=\"T_f86d2_row6_col6\" class=\"data row6 col6\" >0.1830</td>\n",
              "      <td id=\"T_f86d2_row6_col7\" class=\"data row6 col7\" >0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row7\" class=\"row_heading level0 row7\" >llar</th>\n",
              "      <td id=\"T_f86d2_row7_col0\" class=\"data row7 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_f86d2_row7_col1\" class=\"data row7 col1\" >51.7106</td>\n",
              "      <td id=\"T_f86d2_row7_col2\" class=\"data row7 col2\" >3939.2790</td>\n",
              "      <td id=\"T_f86d2_row7_col3\" class=\"data row7 col3\" >61.4955</td>\n",
              "      <td id=\"T_f86d2_row7_col4\" class=\"data row7 col4\" >-1.4896</td>\n",
              "      <td id=\"T_f86d2_row7_col5\" class=\"data row7 col5\" >0.2374</td>\n",
              "      <td id=\"T_f86d2_row7_col6\" class=\"data row7 col6\" >0.1830</td>\n",
              "      <td id=\"T_f86d2_row7_col7\" class=\"data row7 col7\" >0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row8\" class=\"row_heading level0 row8\" >gbr</th>\n",
              "      <td id=\"T_f86d2_row8_col0\" class=\"data row8 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_f86d2_row8_col1\" class=\"data row8 col1\" >52.4896</td>\n",
              "      <td id=\"T_f86d2_row8_col2\" class=\"data row8 col2\" >4355.4048</td>\n",
              "      <td id=\"T_f86d2_row8_col3\" class=\"data row8 col3\" >63.7434</td>\n",
              "      <td id=\"T_f86d2_row8_col4\" class=\"data row8 col4\" >-0.7220</td>\n",
              "      <td id=\"T_f86d2_row8_col5\" class=\"data row8 col5\" >0.2142</td>\n",
              "      <td id=\"T_f86d2_row8_col6\" class=\"data row8 col6\" >0.1618</td>\n",
              "      <td id=\"T_f86d2_row8_col7\" class=\"data row8 col7\" >0.3500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row9\" class=\"row_heading level0 row9\" >et</th>\n",
              "      <td id=\"T_f86d2_row9_col0\" class=\"data row9 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_f86d2_row9_col1\" class=\"data row9 col1\" >52.5137</td>\n",
              "      <td id=\"T_f86d2_row9_col2\" class=\"data row9 col2\" >4610.3152</td>\n",
              "      <td id=\"T_f86d2_row9_col3\" class=\"data row9 col3\" >65.4170</td>\n",
              "      <td id=\"T_f86d2_row9_col4\" class=\"data row9 col4\" >-0.7951</td>\n",
              "      <td id=\"T_f86d2_row9_col5\" class=\"data row9 col5\" >0.2162</td>\n",
              "      <td id=\"T_f86d2_row9_col6\" class=\"data row9 col6\" >0.1613</td>\n",
              "      <td id=\"T_f86d2_row9_col7\" class=\"data row9 col7\" >0.3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row10\" class=\"row_heading level0 row10\" >en</th>\n",
              "      <td id=\"T_f86d2_row10_col0\" class=\"data row10 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_f86d2_row10_col1\" class=\"data row10 col1\" >52.5924</td>\n",
              "      <td id=\"T_f86d2_row10_col2\" class=\"data row10 col2\" >3937.1760</td>\n",
              "      <td id=\"T_f86d2_row10_col3\" class=\"data row10 col3\" >61.2301</td>\n",
              "      <td id=\"T_f86d2_row10_col4\" class=\"data row10 col4\" >-1.4778</td>\n",
              "      <td id=\"T_f86d2_row10_col5\" class=\"data row10 col5\" >0.2367</td>\n",
              "      <td id=\"T_f86d2_row10_col6\" class=\"data row10 col6\" >0.1870</td>\n",
              "      <td id=\"T_f86d2_row10_col7\" class=\"data row10 col7\" >0.0900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row11\" class=\"row_heading level0 row11\" >rf</th>\n",
              "      <td id=\"T_f86d2_row11_col0\" class=\"data row11 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_f86d2_row11_col1\" class=\"data row11 col1\" >54.6745</td>\n",
              "      <td id=\"T_f86d2_row11_col2\" class=\"data row11 col2\" >4794.1368</td>\n",
              "      <td id=\"T_f86d2_row11_col3\" class=\"data row11 col3\" >66.2176</td>\n",
              "      <td id=\"T_f86d2_row11_col4\" class=\"data row11 col4\" >-0.8488</td>\n",
              "      <td id=\"T_f86d2_row11_col5\" class=\"data row11 col5\" >0.2207</td>\n",
              "      <td id=\"T_f86d2_row11_col6\" class=\"data row11 col6\" >0.1674</td>\n",
              "      <td id=\"T_f86d2_row11_col7\" class=\"data row11 col7\" >0.4167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row12\" class=\"row_heading level0 row12\" >dt</th>\n",
              "      <td id=\"T_f86d2_row12_col0\" class=\"data row12 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_f86d2_row12_col1\" class=\"data row12 col1\" >55.1616</td>\n",
              "      <td id=\"T_f86d2_row12_col2\" class=\"data row12 col2\" >4909.7071</td>\n",
              "      <td id=\"T_f86d2_row12_col3\" class=\"data row12 col3\" >67.6420</td>\n",
              "      <td id=\"T_f86d2_row12_col4\" class=\"data row12 col4\" >-0.9792</td>\n",
              "      <td id=\"T_f86d2_row12_col5\" class=\"data row12 col5\" >0.2324</td>\n",
              "      <td id=\"T_f86d2_row12_col6\" class=\"data row12 col6\" >0.1745</td>\n",
              "      <td id=\"T_f86d2_row12_col7\" class=\"data row12 col7\" >0.1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row13\" class=\"row_heading level0 row13\" >xgboost</th>\n",
              "      <td id=\"T_f86d2_row13_col0\" class=\"data row13 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_f86d2_row13_col1\" class=\"data row13 col1\" >58.8813</td>\n",
              "      <td id=\"T_f86d2_row13_col2\" class=\"data row13 col2\" >5279.1889</td>\n",
              "      <td id=\"T_f86d2_row13_col3\" class=\"data row13 col3\" >70.2055</td>\n",
              "      <td id=\"T_f86d2_row13_col4\" class=\"data row13 col4\" >-1.1030</td>\n",
              "      <td id=\"T_f86d2_row13_col5\" class=\"data row13 col5\" >0.2417</td>\n",
              "      <td id=\"T_f86d2_row13_col6\" class=\"data row13 col6\" >0.1840</td>\n",
              "      <td id=\"T_f86d2_row13_col7\" class=\"data row13 col7\" >0.1900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row14\" class=\"row_heading level0 row14\" >ada</th>\n",
              "      <td id=\"T_f86d2_row14_col0\" class=\"data row14 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_f86d2_row14_col1\" class=\"data row14 col1\" >63.4000</td>\n",
              "      <td id=\"T_f86d2_row14_col2\" class=\"data row14 col2\" >5911.5778</td>\n",
              "      <td id=\"T_f86d2_row14_col3\" class=\"data row14 col3\" >74.7116</td>\n",
              "      <td id=\"T_f86d2_row14_col4\" class=\"data row14 col4\" >-1.4124</td>\n",
              "      <td id=\"T_f86d2_row14_col5\" class=\"data row14 col5\" >0.2614</td>\n",
              "      <td id=\"T_f86d2_row14_col6\" class=\"data row14 col6\" >0.2003</td>\n",
              "      <td id=\"T_f86d2_row14_col7\" class=\"data row14 col7\" >0.3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row15\" class=\"row_heading level0 row15\" >knn</th>\n",
              "      <td id=\"T_f86d2_row15_col0\" class=\"data row15 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_f86d2_row15_col1\" class=\"data row15 col1\" >63.9905</td>\n",
              "      <td id=\"T_f86d2_row15_col2\" class=\"data row15 col2\" >7090.8256</td>\n",
              "      <td id=\"T_f86d2_row15_col3\" class=\"data row15 col3\" >78.6650</td>\n",
              "      <td id=\"T_f86d2_row15_col4\" class=\"data row15 col4\" >-1.4488</td>\n",
              "      <td id=\"T_f86d2_row15_col5\" class=\"data row15 col5\" >0.2581</td>\n",
              "      <td id=\"T_f86d2_row15_col6\" class=\"data row15 col6\" >0.1879</td>\n",
              "      <td id=\"T_f86d2_row15_col7\" class=\"data row15 col7\" >0.1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row16\" class=\"row_heading level0 row16\" >lightgbm</th>\n",
              "      <td id=\"T_f86d2_row16_col0\" class=\"data row16 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_f86d2_row16_col1\" class=\"data row16 col1\" >75.6311</td>\n",
              "      <td id=\"T_f86d2_row16_col2\" class=\"data row16 col2\" >7274.8381</td>\n",
              "      <td id=\"T_f86d2_row16_col3\" class=\"data row16 col3\" >85.0036</td>\n",
              "      <td id=\"T_f86d2_row16_col4\" class=\"data row16 col4\" >-2.6040</td>\n",
              "      <td id=\"T_f86d2_row16_col5\" class=\"data row16 col5\" >0.3238</td>\n",
              "      <td id=\"T_f86d2_row16_col6\" class=\"data row16 col6\" >0.2503</td>\n",
              "      <td id=\"T_f86d2_row16_col7\" class=\"data row16 col7\" >0.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row17\" class=\"row_heading level0 row17\" >dummy</th>\n",
              "      <td id=\"T_f86d2_row17_col0\" class=\"data row17 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_f86d2_row17_col1\" class=\"data row17 col1\" >129.0126</td>\n",
              "      <td id=\"T_f86d2_row17_col2\" class=\"data row17 col2\" >21856.8627</td>\n",
              "      <td id=\"T_f86d2_row17_col3\" class=\"data row17 col3\" >138.1257</td>\n",
              "      <td id=\"T_f86d2_row17_col4\" class=\"data row17 col4\" >-6.5548</td>\n",
              "      <td id=\"T_f86d2_row17_col5\" class=\"data row17 col5\" >0.5446</td>\n",
              "      <td id=\"T_f86d2_row17_col6\" class=\"data row17 col6\" >0.3958</td>\n",
              "      <td id=\"T_f86d2_row17_col7\" class=\"data row17 col7\" >0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f86d2_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
              "      <td id=\"T_f86d2_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_f86d2_row18_col1\" class=\"data row18 col1\" >173.5759</td>\n",
              "      <td id=\"T_f86d2_row18_col2\" class=\"data row18 col2\" >33683.0409</td>\n",
              "      <td id=\"T_f86d2_row18_col3\" class=\"data row18 col3\" >180.7955</td>\n",
              "      <td id=\"T_f86d2_row18_col4\" class=\"data row18 col4\" >-17.6483</td>\n",
              "      <td id=\"T_f86d2_row18_col5\" class=\"data row18 col5\" >1.2368</td>\n",
              "      <td id=\"T_f86d2_row18_col6\" class=\"data row18 col6\" >0.6075</td>\n",
              "      <td id=\"T_f86d2_row18_col7\" class=\"data row18 col7\" >0.1467</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef27d8b4ec024942a801cab46ac9b916"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "omp_model = create_model('omp')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
            "eefb453f2d69469bb616b589aa731d42",
            "9ee4a55722eb4474a5b778a2c6e7e336",
            "0f1b499cdb0645299b8ab789711333c5",
            "8bbf2509e34841a6a82fec5b01f97cbf",
            "c6030209c832450cbedf8495d94b4a77",
            "85b054cd2c66460d94b3247164186bbb",
            "1d3364c2a89a4c0ea8e923f73bbe6511",
            "e7546bfd664a461b990ad4fcd6088698",
            "057412ca93514058a148cccbd7e44297",
            "9e8babeec80d4de1b544c8c0f873fffd",
            "954e7bcf72b24481a325b1cc4c13c398"
          ]
        },
        "id": "Zs4Shov4FFHK",
        "outputId": "49a0aca2-250c-4b99-b4bb-985ef5c8f936"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ff3d822b8b0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_6687b_row3_col0, #T_6687b_row3_col1, #T_6687b_row3_col2, #T_6687b_row3_col3, #T_6687b_row3_col4, #T_6687b_row3_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_6687b\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_6687b_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_6687b_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_6687b_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_6687b_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_6687b_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_6687b_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_6687b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_6687b_row0_col0\" class=\"data row0 col0\" >37.8196</td>\n",
              "      <td id=\"T_6687b_row0_col1\" class=\"data row0 col1\" >2503.0394</td>\n",
              "      <td id=\"T_6687b_row0_col2\" class=\"data row0 col2\" >50.0304</td>\n",
              "      <td id=\"T_6687b_row0_col3\" class=\"data row0 col3\" >-1.5293</td>\n",
              "      <td id=\"T_6687b_row0_col4\" class=\"data row0 col4\" >0.2067</td>\n",
              "      <td id=\"T_6687b_row0_col5\" class=\"data row0 col5\" >0.1854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6687b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_6687b_row1_col0\" class=\"data row1 col0\" >33.3443</td>\n",
              "      <td id=\"T_6687b_row1_col1\" class=\"data row1 col1\" >1708.9433</td>\n",
              "      <td id=\"T_6687b_row1_col2\" class=\"data row1 col2\" >41.3394</td>\n",
              "      <td id=\"T_6687b_row1_col3\" class=\"data row1 col3\" >0.3109</td>\n",
              "      <td id=\"T_6687b_row1_col4\" class=\"data row1 col4\" >0.1351</td>\n",
              "      <td id=\"T_6687b_row1_col5\" class=\"data row1 col5\" >0.1109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6687b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_6687b_row2_col0\" class=\"data row2 col0\" >61.7936</td>\n",
              "      <td id=\"T_6687b_row2_col1\" class=\"data row2 col1\" >4940.1030</td>\n",
              "      <td id=\"T_6687b_row2_col2\" class=\"data row2 col2\" >70.2859</td>\n",
              "      <td id=\"T_6687b_row2_col3\" class=\"data row2 col3\" >-0.1381</td>\n",
              "      <td id=\"T_6687b_row2_col4\" class=\"data row2 col4\" >0.1724</td>\n",
              "      <td id=\"T_6687b_row2_col5\" class=\"data row2 col5\" >0.1599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6687b_level0_row3\" class=\"row_heading level0 row3\" >Mean</th>\n",
              "      <td id=\"T_6687b_row3_col0\" class=\"data row3 col0\" >44.3192</td>\n",
              "      <td id=\"T_6687b_row3_col1\" class=\"data row3 col1\" >3050.6952</td>\n",
              "      <td id=\"T_6687b_row3_col2\" class=\"data row3 col2\" >53.8852</td>\n",
              "      <td id=\"T_6687b_row3_col3\" class=\"data row3 col3\" >-0.4522</td>\n",
              "      <td id=\"T_6687b_row3_col4\" class=\"data row3 col4\" >0.1714</td>\n",
              "      <td id=\"T_6687b_row3_col5\" class=\"data row3 col5\" >0.1521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_6687b_level0_row4\" class=\"row_heading level0 row4\" >Std</th>\n",
              "      <td id=\"T_6687b_row4_col0\" class=\"data row4 col0\" >12.4907</td>\n",
              "      <td id=\"T_6687b_row4_col1\" class=\"data row4 col1\" >1374.7832</td>\n",
              "      <td id=\"T_6687b_row4_col2\" class=\"data row4 col2\" >12.1276</td>\n",
              "      <td id=\"T_6687b_row4_col3\" class=\"data row4 col3\" >0.7834</td>\n",
              "      <td id=\"T_6687b_row4_col4\" class=\"data row4 col4\" >0.0292</td>\n",
              "      <td id=\"T_6687b_row4_col5\" class=\"data row4 col5\" >0.0309</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "# Use the best model (OMP) to make prediction\n",
        "prediction_holdout = predict_model(best);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Tx8gm4p-7biE",
        "outputId": "ef5e7485-a394-4853-bfc2-7caea22cceb2"
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      "execution_count": 80,
      "outputs": [
        {
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              "      <th id=\"T_41417_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_41417_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_41417_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_41417_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_41417_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_41417_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_41417_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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              "      <th id=\"T_41417_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_41417_row0_col0\" class=\"data row0 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_41417_row0_col1\" class=\"data row0 col1\" >484.7935</td>\n",
              "      <td id=\"T_41417_row0_col2\" class=\"data row0 col2\" >235277.5642</td>\n",
              "      <td id=\"T_41417_row0_col3\" class=\"data row0 col3\" >485.0542</td>\n",
              "      <td id=\"T_41417_row0_col4\" class=\"data row0 col4\" >-2204438.9317</td>\n",
              "      <td id=\"T_41417_row0_col5\" class=\"data row0 col5\" >3.8726</td>\n",
              "      <td id=\"T_41417_row0_col6\" class=\"data row0 col6\" >52.1733</td>\n",
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      "source": [
        "# predict on new data\n",
        "new_data = df2.copy()\n",
        "predictions2 = predict_model(best, data = new_data)\n"
      ],
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              "      <th id=\"T_860cd_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_860cd_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_860cd_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_860cd_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_860cd_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_860cd_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_860cd_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
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              "      <td id=\"T_860cd_row0_col0\" class=\"data row0 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_860cd_row0_col1\" class=\"data row0 col1\" >271.8992</td>\n",
              "      <td id=\"T_860cd_row0_col2\" class=\"data row0 col2\" >86958.6944</td>\n",
              "      <td id=\"T_860cd_row0_col3\" class=\"data row0 col3\" >294.8876</td>\n",
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      "source": [
        "predictions2.head()"
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          "height": 206
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      "execution_count": 83,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Series  year  month  #Passengers  prediction_label\n",
              "0       1  1949      1          112        120.962681\n",
              "1       2  1949      2          118        122.327867\n",
              "2       3  1949      3          132        123.706393\n",
              "3       4  1949      4          129        125.098368\n",
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    {
      "cell_type": "code",
      "source": [
        "# generate predictions on the original dataset**\n",
        "predictions = predict_model(best, data=df2)\n",
        "\n",
        "# add a date column in the dataset\n",
        "predictions['Date'] = pd.date_range(start='1949-01-01', end = '1960-12-01', freq = 'MS')\n",
        "\n",
        "# line plot\n",
        "fig = px.line(predictions, x='Date', y=['#Passengers', 'prediction_label'], template = 'plotly_white')\n",
        "\n",
        "# add a vertical rectange for test-set separation\n",
        "fig.add_vrect(x0='1960-01-01', x1='1960-12-01', fillcolor='grey', opacity=0.25, line_width=0)\n",
        "\n",
        "fig.show()\n"
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OJKUAQJ8D8lx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "(Incomplete example ....)"
      ],
      "metadata": {
        "id": "wO2g3x_2D8_h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using [sktime library](https://www.sktime.net/en/latest/index.html)"
      ],
      "metadata": {
        "id": "_HWxxbkNb6b2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install sktime in Google Colab\n",
        "\n",
        "!pip install --quiet sktime"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXZ5CvWucIi6",
        "outputId": "39842bd0-f03e-42d7-a7a4-7b3635bb1119"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.9/16.9 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sktime.datasets import load_airline\n",
        "from sktime.utils.plotting import plot_series\n",
        "from sktime.forecasting.naive import NaiveForecaster"
      ],
      "metadata": {
        "id": "AJXa6N9U812A"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# in the example, we use the airline data set.\n",
        "y = load_airline()\n",
        "plot_series(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "CohMsJjK9ZR8",
        "outputId": "f9508dea-76d3-4f51-93ad-9dd139e8a2cb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 1600x400 with 1 Axes>,\n",
              " <Axes: ylabel='Number of airline passengers'>)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a forecasting horizon \n",
        "from sktime.forecasting.base import ForecastingHorizon"
      ],
      "metadata": {
        "id": "bpgbsH__9dEs"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fh = ForecastingHorizon(\n",
        "    pd.PeriodIndex(pd.date_range(\"1961-01\", periods=36, freq=\"M\")), is_relative=False\n",
        ")\n",
        "fh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmK7Di2pdQC-",
        "outputId": "10105704-e36e-4533-d41e-f5cfda217cec"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ForecastingHorizon(['1961-01', '1961-02', '1961-03', '1961-04', '1961-05', '1961-06',\n",
              "             '1961-07', '1961-08', '1961-09', '1961-10', '1961-11', '1961-12',\n",
              "             '1962-01', '1962-02', '1962-03', '1962-04', '1962-05', '1962-06',\n",
              "             '1962-07', '1962-08', '1962-09', '1962-10', '1962-11', '1962-12',\n",
              "             '1963-01', '1963-02', '1963-03', '1963-04', '1963-05', '1963-06',\n",
              "             '1963-07', '1963-08', '1963-09', '1963-10', '1963-11', '1963-12'],\n",
              "            dtype='period[M]', is_relative=False)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the forecasting algorithm\n",
        "\n",
        "forecaster = NaiveForecaster(strategy=\"last\")"
      ],
      "metadata": {
        "id": "seB7990PdUQY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the forecaster to seen data\n",
        "forecaster.fit(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ESFPdSKPd0hb",
        "outputId": "af2eb6a7-f26a-43de-c0a6-00461652be85"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NaiveForecaster()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>NaiveForecaster()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">NaiveForecaster</label><div class=\"sk-toggleable__content\"><pre>NaiveForecaster()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Requesting forecasting\n",
        "y_pred = forecaster.predict(fh)\n",
        "\n",
        "# plotting predictions and past data\n",
        "plot_series(y, y_pred, labels=[\"y\", \"y_pred\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "VBdELwsP-Www",
        "outputId": "babc5840-80f2-40bf-e5b9-53824d639803"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 1600x400 with 1 Axes>,\n",
              " <Axes: ylabel='Number of airline passengers'>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# step 1: data specification\n",
        "y = load_airline()\n",
        "\n",
        "# step 2: specifying forecasting horizon\n",
        "fh = np.arange(1, 37)\n",
        "\n",
        "# step 3: specifying the forecasting algorithm\n",
        "forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
        "\n",
        "# step 4: fitting the forecaster\n",
        "forecaster.fit(y)\n",
        "\n",
        "# step 5: querying predictions\n",
        "y_pred = forecaster.predict(fh)"
      ],
      "metadata": {
        "id": "gah5tl0J-wbn"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# optional: plotting predictions and past data\n",
        "plot_series(y, y_pred, labels=[\"y\", \"y_pred\"]);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "8ZQdsURd_D0t",
        "outputId": "ce250285-699f-40ac-c0c2-a315bd02e11d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Another way. Using a temporal train and test split\n",
        "from sktime.forecasting.model_selection import temporal_train_test_split\n",
        "\n",
        "y = load_airline()\n",
        "y_train, y_test = temporal_train_test_split(y, test_size=36)\n",
        "# we will try to forecast y_test from y_train"
      ],
      "metadata": {
        "id": "-b4iFrUU_IFi"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting for illustration\n",
        "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"])\n",
        "print(y_train.shape[0], y_test.shape[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "myD6NuGUW2Kh",
        "outputId": "5aafee5e-f250-45e1-a715-f1676794a4cc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "108 36\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making forecasts for y_tests from y_train\n",
        "# we can simply take the indices from `y_test` where they already are stored\n",
        "fh = ForecastingHorizon(y_test.index, is_relative=False)\n",
        "\n",
        "forecaster = NaiveForecaster(strategy=\"last\", sp=12)\n",
        "\n",
        "forecaster.fit(y_train)\n",
        "\n",
        "# y_pred will contain the predictions\n",
        "y_pred = forecaster.predict(fh)"
      ],
      "metadata": {
        "id": "c0tnuSddW6H_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting for illustration\n",
        "plot_series(y_train, y_test, y_pred, labels=[\"y_train\", \"y_test\", \"y_pred\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "KA6JgkPiXLoo",
        "outputId": "67b11780-9005-47fe-de5e-860ab9bfbaf2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EjIO7nlmXSaU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}