examples/statsmodels/arima/Seasonal ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export Seasonal ARIMA model into PMML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa.api import SARIMAX\n",
    "from nyoka import StatsmodelsToPmml\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('international-airline-passengers.csv', engine = 'python', skipfooter = 3)\n",
    "data['Month'] = pd.to_datetime(data['Month'], format='%Y-%m')\n",
    "data.set_index(['Month'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SARIMAX(data,\n",
    "                        order = (3, 1, 1),\n",
    "                        seasonal_order = (3, 1, 1, 12))\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export into PMML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmml_f_name = 'seasonal_international_airline_passengers.pmml'\n",
    "StatsmodelsToPmml(\n",
    "            results_obj = results,\n",
    "            pmml_file_name = pmml_f_name,\n",
    "            conf_int=[80,95]\n",
    "           )"
   ]
  }
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