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December 24, 2022 19:45
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arima_vs_ma.ipynb
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| { | |
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| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyMOZCr7H4IJQ/r0FczHPod5", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
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| "language_info": { | |
| "name": "python" | |
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| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
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| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ngupta23/4aaab9205ef3de3921c3a5a580f7701d/arima_vs_ma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "xL_eTYkLntfi" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "!pip install --pre pycaret" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from pycaret.datasets import get_data\n", | |
| "from pycaret.time_series import TSForecastingExperiment" | |
| ], | |
| "metadata": { | |
| "id": "7aertc8fnu83" | |
| }, | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data = get_data(\"airline\", verbose=False)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 17 | |
| }, | |
| "id": "Zcx-M-ZIoYBH", | |
| "outputId": "4e6cae75-1a78-4e89-bb54-e99556db01be" | |
| }, | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
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| { | |
| "cell_type": "code", | |
| "source": [ | |
| "exp = TSForecastingExperiment()\n", | |
| "exp.setup(data=data, session_id=42, fh=6)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
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| "id": "frHRAo_Uoar-", | |
| "outputId": "d60562c8-57b6-44e2-8a45-ab801ecf89a1" | |
| }, | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<pandas.io.formats.style.Styler at 0x7f6fbb1cb130>" | |
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| "<style type=\"text/css\">\n", | |
| "#T_3ef8c_row22_col1 {\n", | |
| " background-color: lightgreen;\n", | |
| "}\n", | |
| "</style>\n", | |
| "<table id=\"T_3ef8c_\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr>\n", | |
| " <th class=\"blank level0\" > </th>\n", | |
| " <th class=\"col_heading level0 col0\" >Description</th>\n", | |
| " <th class=\"col_heading level0 col1\" >Value</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n", | |
| " <td id=\"T_3ef8c_row0_col0\" class=\"data row0 col0\" >session_id</td>\n", | |
| " <td id=\"T_3ef8c_row0_col1\" class=\"data row0 col1\" >42</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n", | |
| " <td id=\"T_3ef8c_row1_col0\" class=\"data row1 col0\" >Target</td>\n", | |
| " <td id=\"T_3ef8c_row1_col1\" class=\"data row1 col1\" >Number of airline passengers</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n", | |
| " <td id=\"T_3ef8c_row2_col0\" class=\"data row2 col0\" >Approach</td>\n", | |
| " <td id=\"T_3ef8c_row2_col1\" class=\"data row2 col1\" >Univariate</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n", | |
| " <td id=\"T_3ef8c_row3_col0\" class=\"data row3 col0\" >Exogenous Variables</td>\n", | |
| " <td id=\"T_3ef8c_row3_col1\" class=\"data row3 col1\" >Not Present</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n", | |
| " <td id=\"T_3ef8c_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n", | |
| " <td id=\"T_3ef8c_row4_col1\" class=\"data row4 col1\" >(144, 1)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n", | |
| " <td id=\"T_3ef8c_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n", | |
| " <td id=\"T_3ef8c_row5_col1\" class=\"data row5 col1\" >(144, 1)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n", | |
| " <td id=\"T_3ef8c_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n", | |
| " <td id=\"T_3ef8c_row6_col1\" class=\"data row6 col1\" >(138, 1)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n", | |
| " <td id=\"T_3ef8c_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n", | |
| " <td id=\"T_3ef8c_row7_col1\" class=\"data row7 col1\" >(6, 1)</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n", | |
| " <td id=\"T_3ef8c_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n", | |
| " <td id=\"T_3ef8c_row8_col1\" class=\"data row8 col1\" >0.0%</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n", | |
| " <td id=\"T_3ef8c_row9_col0\" class=\"data row9 col0\" >Fold Generator</td>\n", | |
| " <td id=\"T_3ef8c_row9_col1\" class=\"data row9 col1\" >ExpandingWindowSplitter</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n", | |
| " <td id=\"T_3ef8c_row10_col0\" class=\"data row10 col0\" >Fold Number</td>\n", | |
| " <td id=\"T_3ef8c_row10_col1\" class=\"data row10 col1\" >3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n", | |
| " <td id=\"T_3ef8c_row11_col0\" class=\"data row11 col0\" >Enforce Prediction Interval</td>\n", | |
| " <td id=\"T_3ef8c_row11_col1\" class=\"data row11 col1\" >False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n", | |
| " <td id=\"T_3ef8c_row12_col0\" class=\"data row12 col0\" >Seasonality Detection Algo</td>\n", | |
| " <td id=\"T_3ef8c_row12_col1\" class=\"data row12 col1\" >auto</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n", | |
| " <td id=\"T_3ef8c_row13_col0\" class=\"data row13 col0\" >Max Period to Consider</td>\n", | |
| " <td id=\"T_3ef8c_row13_col1\" class=\"data row13 col1\" >None</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n", | |
| " <td id=\"T_3ef8c_row14_col0\" class=\"data row14 col0\" >Seasonal Period(s) Tested</td>\n", | |
| " <td id=\"T_3ef8c_row14_col1\" class=\"data row14 col1\" >[12, 24, 36, 11, 48]</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n", | |
| " <td id=\"T_3ef8c_row15_col0\" class=\"data row15 col0\" >Significant Seasonal Period(s)</td>\n", | |
| " <td id=\"T_3ef8c_row15_col1\" class=\"data row15 col1\" >[12, 24, 36, 11, 48]</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n", | |
| " <td id=\"T_3ef8c_row16_col0\" class=\"data row16 col0\" >Significant Seasonal Period(s) without Harmonics</td>\n", | |
| " <td id=\"T_3ef8c_row16_col1\" class=\"data row16 col1\" >[48, 36, 11]</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n", | |
| " <td id=\"T_3ef8c_row17_col0\" class=\"data row17 col0\" >Remove Harmonics</td>\n", | |
| " <td id=\"T_3ef8c_row17_col1\" class=\"data row17 col1\" >False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n", | |
| " <td id=\"T_3ef8c_row18_col0\" class=\"data row18 col0\" >Harmonics Order Method</td>\n", | |
| " <td id=\"T_3ef8c_row18_col1\" class=\"data row18 col1\" >harmonic_max</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n", | |
| " <td id=\"T_3ef8c_row19_col0\" class=\"data row19 col0\" >Num Seasonalities to Use</td>\n", | |
| " <td id=\"T_3ef8c_row19_col1\" class=\"data row19 col1\" >1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n", | |
| " <td id=\"T_3ef8c_row20_col0\" class=\"data row20 col0\" >All Seasonalities to Use</td>\n", | |
| " <td id=\"T_3ef8c_row20_col1\" class=\"data row20 col1\" >[12]</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n", | |
| " <td id=\"T_3ef8c_row21_col0\" class=\"data row21 col0\" >Primary Seasonality</td>\n", | |
| " <td id=\"T_3ef8c_row21_col1\" class=\"data row21 col1\" >12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n", | |
| " <td id=\"T_3ef8c_row22_col0\" class=\"data row22 col0\" >Seasonality Present</td>\n", | |
| " <td id=\"T_3ef8c_row22_col1\" class=\"data row22 col1\" >True</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n", | |
| " <td id=\"T_3ef8c_row23_col0\" class=\"data row23 col0\" >Target Strictly Positive</td>\n", | |
| " <td id=\"T_3ef8c_row23_col1\" class=\"data row23 col1\" >True</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n", | |
| " <td id=\"T_3ef8c_row24_col0\" class=\"data row24 col0\" >Target White Noise</td>\n", | |
| " <td id=\"T_3ef8c_row24_col1\" class=\"data row24 col1\" >No</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n", | |
| " <td id=\"T_3ef8c_row25_col0\" class=\"data row25 col0\" >Recommended d</td>\n", | |
| " <td id=\"T_3ef8c_row25_col1\" class=\"data row25 col1\" >1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n", | |
| " <td id=\"T_3ef8c_row26_col0\" class=\"data row26 col0\" >Recommended Seasonal D</td>\n", | |
| " <td id=\"T_3ef8c_row26_col1\" class=\"data row26 col1\" >1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n", | |
| " <td id=\"T_3ef8c_row27_col0\" class=\"data row27 col0\" >Preprocess</td>\n", | |
| " <td id=\"T_3ef8c_row27_col1\" class=\"data row27 col1\" >False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n", | |
| " <td id=\"T_3ef8c_row28_col0\" class=\"data row28 col0\" >CPU Jobs</td>\n", | |
| " <td id=\"T_3ef8c_row28_col1\" class=\"data row28 col1\" >-1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n", | |
| " <td id=\"T_3ef8c_row29_col0\" class=\"data row29 col0\" >Use GPU</td>\n", | |
| " <td id=\"T_3ef8c_row29_col1\" class=\"data row29 col1\" >False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n", | |
| " <td id=\"T_3ef8c_row30_col0\" class=\"data row30 col0\" >Log Experiment</td>\n", | |
| " <td id=\"T_3ef8c_row30_col1\" class=\"data row30 col1\" >False</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n", | |
| " <td id=\"T_3ef8c_row31_col0\" class=\"data row31 col0\" >Experiment Name</td>\n", | |
| " <td id=\"T_3ef8c_row31_col1\" class=\"data row31 col1\" >ts-default-name</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th id=\"T_3ef8c_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n", | |
| " <td id=\"T_3ef8c_row32_col0\" class=\"data row32 col0\" >USI</td>\n", | |
| " <td id=\"T_3ef8c_row32_col1\" class=\"data row32 col1\" >0352</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<pycaret.time_series.forecasting.oop.TSForecastingExperiment at 0x7f6fbaceb1f0>" | |
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| "execution_count": 5 | |
| } | |
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| "source": [ | |
| "model1 = exp.create_model(\"arima\", order=(0,0,3), seasonal_order=(0, 0, 0, 0), with_intercept=False, verbose=False)\n", | |
| "type(model1), model1" | |
| ], | |
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| "id": "jes2hd50ol_O", | |
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| "execution_count": 15, | |
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| { | |
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| "(sktime.forecasting.arima.ARIMA, ARIMA(order=(0, 0, 3), with_intercept=False))" | |
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| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "exp.predict_model(model1, verbose=False)" | |
| ], | |
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| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
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| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " y_pred\n", | |
| "1960-07 487.2285\n", | |
| "1960-08 332.1251\n", | |
| "1960-09 172.5590\n", | |
| "1960-10 0.0000\n", | |
| "1960-11 0.0000\n", | |
| "1960-12 0.0000" | |
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| " <th>1960-07</th>\n", | |
| " <td>487.2285</td>\n", | |
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| " <th>1960-08</th>\n", | |
| " <td>332.1251</td>\n", | |
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| " <th>1960-09</th>\n", | |
| " <td>172.5590</td>\n", | |
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| " <th>1960-10</th>\n", | |
| " <td>0.0000</td>\n", | |
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| " <th>1960-11</th>\n", | |
| " <td>0.0000</td>\n", | |
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| " <th>1960-12</th>\n", | |
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| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-02308735-f6a4-497c-b681-f6f234ebf354 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-02308735-f6a4-497c-b681-f6f234ebf354');\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": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "eoDnJFRMpQ51" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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