tsa.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08VaHKrzKi7r",
        "outputId": "d788e60e-705b-426f-d54d-f298f22e479c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement x13-arima-seats (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for x13-arima-seats\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "#pip install x13-arima-seats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "31Y5M88PKi7w"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import statsmodels as sm\n",
        "#from x13_arima_seats import x13_arima_select_order\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "iI_ja_X4Ki7y",
        "outputId": "6fbaaa55-cfd4-4db3-d45b-90dacecfffda"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "module 'statsmodels' has no attribute 'datasets'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\harum\\GitHub\\Gadget\\IppanMicro\\tsa.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/harum/GitHub/Gadget/IppanMicro/tsa.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df \u001b[39m=\u001b[39m sm\u001b[39m.\u001b[39;49mdatasets\u001b[39m.\u001b[39mco2\u001b[39m.\u001b[39mload_pandas()\u001b[39m.\u001b[39mdata\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/harum/GitHub/Gadget/IppanMicro/tsa.ipynb#W2sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df\u001b[39m.\u001b[39mco2\u001b[39m.\u001b[39minterpolate(inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/harum/GitHub/Gadget/IppanMicro/tsa.ipynb#W2sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m df \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mresample(\u001b[39m'\u001b[39m\u001b[39mM\u001b[39m\u001b[39m'\u001b[39m)\u001b[39m.\u001b[39msum()\n",
            "\u001b[1;31mAttributeError\u001b[0m: module 'statsmodels' has no attribute 'datasets'"
          ]
        }
      ],
      "source": [
        "df = sm.datasets.co2.load_pandas().data\n",
        "df.co2.interpolate(inplace=True)\n",
        "df = df.resample('M').sum()\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K7Tn2ct3Ki7z",
        "outputId": "a5b475ce-cee5-481e-ad9e-c1e101d13718"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'read'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-c8d53116589f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres_ord\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx13\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx13_arima_select_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_ord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_ord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msorder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/lib/python3.6/site-packages/statsmodels/tsa/x13.py\u001b[0m in \u001b[0;36mx13_arima_select_order\u001b[0;34m(endog, maxorder, maxdiff, diff, exog, log, outlier, trading, forecast_periods, start, freq, print_stdout, x12path, prefer_x13)\u001b[0m\n\u001b[1;32m    574\u001b[0m                                  \u001b[0mforecast_periods\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforecast_periods\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m                                  \u001b[0mmaxorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxdiff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxdiff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m                                  start=start, freq=freq, prefer_x13=prefer_x13)\n\u001b[0m\u001b[1;32m    577\u001b[0m     model = re.search(\"(?<=Final automatic model choice : ).*\",\n\u001b[1;32m    578\u001b[0m                       results.results)\n",
            "\u001b[0;32m~/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m~/lib/python3.6/site-packages/statsmodels/tsa/x13.py\u001b[0m in \u001b[0;36mx13_arima_analysis\u001b[0;34m(endog, maxorder, maxdiff, diff, exog, log, outlier, trading, forecast_periods, retspec, speconly, start, freq, print_stdout, x12path, prefer_x13)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_spec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx12path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mftempin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mftempout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m         \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mprint_stdout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'read'"
          ]
        }
      ],
      "source": [
        "res_ord = sm.tsa.x13.x13_arima_select_order(df.co2)\n",
        "print(res_ord.order, res_ord.sorder)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.6"
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  },
  "nbformat": 4,
  "nbformat_minor": 0
}