{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Sarimax Usage\n",
    "If you are not a magician who can easily infer correct SARIMAX orders from looking on PACF (partial autocorrelation function) and ACF (autocorrelation function), you want to rather leverage AutoSarima which finds them for you - set `init_with_autoarima` to True. \n",
    "\n",
    "If you want to further configure the search space of AutoARIMA, then you can provide all parameters of pmdarima.arima.AutoARIMA as `autoarima_dict` arguments.\n",
    "\n",
    "When you run cross-validation with enabled AutoARIMA (`init_with_autoarima`), it's often advisable to find the correct order only during the first fit call and reuse this model on all other splits in order to simulate the out-of-sample performance.\n",
    "\n",
    "The signature of SarimaxWrapper contains parameters of pmdarima.arima.ARIMA, not AutoARIMA class.\n",
    "\n",
    "For more parameters check [pmdarima docs](http://alkaline-ml.com/pmdarima/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['figure.figsize'] = [12, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.utils import get_sales_data\n",
    "\n",
    "df = get_sales_data(n_dates=100, \n",
    "                    n_assortments=1, \n",
    "                    n_states=1, \n",
    "                    n_stores=1)\n",
    "X, y = df[[\"Open\"]], df['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.wrappers import SarimaxWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "SarimaxWrapper?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SarimaxWrapper(\n",
    "    autoarima_dict={'d':1, 'm':7, 'max_p':2, 'max_q':2}, \n",
    "    init_with_autoarima=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = (model.fit(X[:-10], y[:-10])\n",
    "         .predict(X[-10:])\n",
    "         .merge(y, left_index=True, right_index=True, how='outer')\n",
    "         .tail(50)    \n",
    ")\n",
    "preds.plot(title=f\"MAE:{(preds['Sales']-preds['sarimax']).abs().mean().round(3)}\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now access the models parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might also directly pass the orders if you know, what are you doing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SarimaxWrapper(order=(1, 1, 2), seasonal_order=(1, 0, 2, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = (model.fit(X[:-10], y[:-10])\n",
    "         .predict(X[-10:])\n",
    "         .merge(y, left_index=True, right_index=True, how='outer')\n",
    "         .tail(50)    \n",
    ")\n",
    "preds.plot(title=f\"MAE:{(preds['Sales']-preds['sarimax']).abs().mean().round(3)}\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  }
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