27_adida_imapa_starter.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from statsforecast import StatsForecast\n",
    "from statsforecast.models import CrostonOptimized\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89c57f05",
   "metadata": {},
   "outputs": [],
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    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "224ffed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/intermittent_time_series.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4c6ea3",
   "metadata": {},
   "source": [
    "## ADIDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8be5a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model with ADIDA and Croston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "768d18c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax.bar(df.index, df['y'], color='lightgray')\n",
    "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls='--', label='Croston (optimized)')\n",
    "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n",
    "ax.set_ylabel('Value')\n",
    "ax.set_xlabel('Time steps')\n",
    "ax.legend(loc='best')\n",
    "plt.xlim(40, 100)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feed0476",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the MAE\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "print(f'MAE Croston: {mae_croston}')\n",
    "print(f'MAE ADIDA: {mae_adida}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28706aa",
   "metadata": {},
   "source": [
    "## IMAPA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed3259f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model with IMAPA and ADIDA\n",
    "\n",
    "sf = StatsForecast(\n",
    "    df=df,\n",
    "    models=models,\n",
    "    freq='H',\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "cv_df = sf.cross_validation(\n",
    "    df=df,\n",
    "    h=1,\n",
    "    step_size=1,\n",
    "    n_windows=50\n",
    ")\n",
    "\n",
    "cv_df.index = np.arange(50, 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e7710e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "\n",
    "ax.bar(df.index, df['y'], color='lightgray')\n",
    "ax.plot(cv_df.index, cv_df['IMAPA'], ls='--', label='IMAPA')\n",
    "ax.plot(cv_df.index, cv_df['ADIDA'], ls=':', label='ADIDA')\n",
    "ax.set_ylabel('Value')\n",
    "ax.set_xlabel('Time steps')\n",
    "ax.legend(loc='best')\n",
    "plt.xlim(40, 100)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87d6cbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_imapa = mean_absolute_error(cv_df['y'], cv_df['IMAPA'])\n",
    "mae_adida = mean_absolute_error(cv_df['y'], cv_df['ADIDA'])\n",
    "\n",
    "print(f'MAE Croston: {mae_imapa}')\n",
    "print(f'MAE ADIDA: {mae_adida}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98074fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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