29_intermittent_error_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7722505",
   "metadata": {},
   "outputs": [],
   "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, ADIDA, TSB\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9c10d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "501318a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 00:00:00</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 01:00:00</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 02:00:00</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 03:00:00</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id                   ds   y\n",
       "0          1  2023-01-01 00:00:00  43\n",
       "1          1  2023-01-01 01:00:00  87\n",
       "2          1  2023-01-01 02:00:00  89\n",
       "3          1  2023-01-01 03:00:00  87\n",
       "4          1  2023-01-01 04:00:00  73"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/intermittent_time_series.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0094b8bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [TSB(0.1, 0.1), ADIDA(), CrostonOptimized()]\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": 8,
   "id": "b44dc8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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['TSB'], ls='--', label='TSB')\n",
    "ax.plot(cv_df.index, cv_df['CrostonOptimized'], ls=':', label='Croston')\n",
    "ax.plot(cv_df.index, cv_df['ADIDA'], ls='-.', label='ADIDA')\n",
    "\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": "markdown",
   "id": "d1459658",
   "metadata": {},
   "source": [
    "## CFE, CFE_min, CFE_max, PIS, NOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dcc24fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def errors(y_true, y_pred):\n",
    "    \n",
    "    # CFE\n",
    "    cfe_all = np.cumsum(y_true - y_pred)\n",
    "    cfe = cfe_all.iloc[-1]\n",
    "    \n",
    "    cfe_max = np.max(cfe_all)\n",
    "    cfe_min = np.min(cfe_all)\n",
    "    \n",
    "    # PIS\n",
    "    pis_all = -np.cumsum(cfe_all)\n",
    "    pis = pis_all.iloc[-1]\n",
    "    \n",
    "    # NOS\n",
    "    nos = len(cfe_all[cfe_all > 0])\n",
    "\n",
    "    errors = {\n",
    "        \"CFE\": cfe,\n",
    "        \"CFE_max\": cfe_max,\n",
    "        \"CFE_min\": cfe_min,\n",
    "        \"PIS\": pis,\n",
    "        \"NOS\": nos\n",
    "    }\n",
    "    \n",
    "    return errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "312c4b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "croston_errors = errors(cv_df['y'], cv_df['CrostonOptimized'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9dd24bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CFE': -14.413212,\n",
       " 'CFE_max': 34.96125411987305,\n",
       " 'CFE_min': -332.0770568847656,\n",
       " 'PIS': 5527.8726,\n",
       " 'NOS': 3}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "croston_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "66468c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CFE': 39.358646,\n",
       " 'CFE_max': 92.94744873046875,\n",
       " 'CFE_min': -259.12799072265625,\n",
       " 'PIS': 3711.2573,\n",
       " 'NOS': 8}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adida_errors = errors(cv_df['y'], cv_df['ADIDA'])\n",
    "adida_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b15a2443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CFE': 5.303444,\n",
       " 'CFE_max': 57.37592315673828,\n",
       " 'CFE_min': -291.7810363769531,\n",
       " 'PIS': 4825.8857,\n",
       " 'NOS': 6}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsb_errors = errors(cv_df['y'], cv_df['TSB'])\n",
    "tsb_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0d266cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = ['Croston', 'ADIDA', 'TSB']\n",
    "y = [croston_errors['CFE'], adida_errors['CFE'], tsb_errors['CFE']]\n",
    "\n",
    "errors = [\n",
    "    [abs(croston_errors['CFE_min']), abs(adida_errors['CFE_min']), abs(tsb_errors['CFE_min'])],\n",
    "    [croston_errors['CFE_max'], adida_errors['CFE_max'], tsb_errors['CFE_max']]\n",
    "]\n",
    "\n",
    "ax.errorbar(x, y, yerr=errors, fmt='o')\n",
    "ax.set_xlabel('Models')\n",
    "ax.set_ylabel('CFE')\n",
    "\n",
    "for i, v in enumerate(y):\n",
    "    plt.text(x=i+0.03, y=v, s=str(round(v,2)), va='center')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e144cd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb786e17",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}