Serie temporal - Skforecast - LES PRESES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2eede2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from pmdarima.arima import auto_arima\n",
    "\n",
    "# Tratamiento de datos\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Gráficos\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['lines.linewidth'] = 1.5\n",
    "\n",
    "# Modelado y Forecasting\n",
    "# ==============================================================================\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "\n",
    "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n",
    "from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom\n",
    "from skforecast.ForecasterAutoregDirect import ForecasterAutoregDirect\n",
    "from skforecast.model_selection import grid_search_forecaster\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.utils import save_forecaster\n",
    "from skforecast.utils import load_forecaster\n",
    "\n",
    "# Configuración warnings\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "# warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb9399a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lugar_nacimiento=pd.read_csv('t674mun_171390LES PRESES.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4d86f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "lugar_nacimiento=lugar_nacimiento.rename(columns={'Unnamed: 0': 'Año'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d5fff16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Año', 'Catalunya', 'Mateixa comarca', 'Altra comarca',\n",
       "       'Resta d'Espanya', 'Estranger', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lugar_nacimiento.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed7c87e3",
   "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>Año</th>\n",
       "      <th>Catalunya</th>\n",
       "      <th>Mateixa comarca</th>\n",
       "      <th>Altra comarca</th>\n",
       "      <th>Resta d'Espanya</th>\n",
       "      <th>Estranger</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000</td>\n",
       "      <td>1243</td>\n",
       "      <td>1069</td>\n",
       "      <td>174</td>\n",
       "      <td>114</td>\n",
       "      <td>16</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>1281</td>\n",
       "      <td>1097</td>\n",
       "      <td>184</td>\n",
       "      <td>110</td>\n",
       "      <td>28</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2002</td>\n",
       "      <td>1346</td>\n",
       "      <td>1138</td>\n",
       "      <td>208</td>\n",
       "      <td>115</td>\n",
       "      <td>50</td>\n",
       "      <td>1511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2003</td>\n",
       "      <td>1390</td>\n",
       "      <td>1163</td>\n",
       "      <td>227</td>\n",
       "      <td>113</td>\n",
       "      <td>51</td>\n",
       "      <td>1554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2004</td>\n",
       "      <td>1470</td>\n",
       "      <td>1206</td>\n",
       "      <td>264</td>\n",
       "      <td>119</td>\n",
       "      <td>55</td>\n",
       "      <td>1644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2005</td>\n",
       "      <td>1490</td>\n",
       "      <td>1219</td>\n",
       "      <td>271</td>\n",
       "      <td>114</td>\n",
       "      <td>53</td>\n",
       "      <td>1657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>1498</td>\n",
       "      <td>1219</td>\n",
       "      <td>279</td>\n",
       "      <td>115</td>\n",
       "      <td>79</td>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2007</td>\n",
       "      <td>1480</td>\n",
       "      <td>1198</td>\n",
       "      <td>282</td>\n",
       "      <td>122</td>\n",
       "      <td>84</td>\n",
       "      <td>1686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2008</td>\n",
       "      <td>1500</td>\n",
       "      <td>1210</td>\n",
       "      <td>290</td>\n",
       "      <td>118</td>\n",
       "      <td>90</td>\n",
       "      <td>1708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2009</td>\n",
       "      <td>1506</td>\n",
       "      <td>1218</td>\n",
       "      <td>288</td>\n",
       "      <td>120</td>\n",
       "      <td>105</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2010</td>\n",
       "      <td>1507</td>\n",
       "      <td>1220</td>\n",
       "      <td>287</td>\n",
       "      <td>115</td>\n",
       "      <td>102</td>\n",
       "      <td>1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>1518</td>\n",
       "      <td>1230</td>\n",
       "      <td>288</td>\n",
       "      <td>115</td>\n",
       "      <td>112</td>\n",
       "      <td>1745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>1531</td>\n",
       "      <td>1245</td>\n",
       "      <td>286</td>\n",
       "      <td>110</td>\n",
       "      <td>111</td>\n",
       "      <td>1752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>1553</td>\n",
       "      <td>1257</td>\n",
       "      <td>296</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>1779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>1560</td>\n",
       "      <td>1254</td>\n",
       "      <td>306</td>\n",
       "      <td>110</td>\n",
       "      <td>102</td>\n",
       "      <td>1772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>1559</td>\n",
       "      <td>1254</td>\n",
       "      <td>305</td>\n",
       "      <td>105</td>\n",
       "      <td>100</td>\n",
       "      <td>1764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>1559</td>\n",
       "      <td>1243</td>\n",
       "      <td>316</td>\n",
       "      <td>119</td>\n",
       "      <td>103</td>\n",
       "      <td>1781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>1550</td>\n",
       "      <td>1236</td>\n",
       "      <td>314</td>\n",
       "      <td>119</td>\n",
       "      <td>95</td>\n",
       "      <td>1764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>1561</td>\n",
       "      <td>1237</td>\n",
       "      <td>324</td>\n",
       "      <td>114</td>\n",
       "      <td>88</td>\n",
       "      <td>1763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>1582</td>\n",
       "      <td>1254</td>\n",
       "      <td>328</td>\n",
       "      <td>113</td>\n",
       "      <td>93</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>1626</td>\n",
       "      <td>1278</td>\n",
       "      <td>348</td>\n",
       "      <td>107</td>\n",
       "      <td>122</td>\n",
       "      <td>1855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>1653</td>\n",
       "      <td>1290</td>\n",
       "      <td>363</td>\n",
       "      <td>107</td>\n",
       "      <td>115</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>1644</td>\n",
       "      <td>1278</td>\n",
       "      <td>366</td>\n",
       "      <td>107</td>\n",
       "      <td>124</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Año  Catalunya  Mateixa comarca  Altra comarca  Resta d'Espanya  \\\n",
       "22  2000       1243             1069            174              114   \n",
       "21  2001       1281             1097            184              110   \n",
       "20  2002       1346             1138            208              115   \n",
       "19  2003       1390             1163            227              113   \n",
       "18  2004       1470             1206            264              119   \n",
       "17  2005       1490             1219            271              114   \n",
       "16  2006       1498             1219            279              115   \n",
       "15  2007       1480             1198            282              122   \n",
       "14  2008       1500             1210            290              118   \n",
       "13  2009       1506             1218            288              120   \n",
       "12  2010       1507             1220            287              115   \n",
       "11  2011       1518             1230            288              115   \n",
       "10  2012       1531             1245            286              110   \n",
       "9   2013       1553             1257            296              113   \n",
       "8   2014       1560             1254            306              110   \n",
       "7   2015       1559             1254            305              105   \n",
       "6   2016       1559             1243            316              119   \n",
       "5   2017       1550             1236            314              119   \n",
       "4   2018       1561             1237            324              114   \n",
       "3   2019       1582             1254            328              113   \n",
       "2   2020       1626             1278            348              107   \n",
       "1   2021       1653             1290            363              107   \n",
       "0   2022       1644             1278            366              107   \n",
       "\n",
       "    Estranger  Total  \n",
       "22         16   1373  \n",
       "21         28   1419  \n",
       "20         50   1511  \n",
       "19         51   1554  \n",
       "18         55   1644  \n",
       "17         53   1657  \n",
       "16         79   1692  \n",
       "15         84   1686  \n",
       "14         90   1708  \n",
       "13        105   1731  \n",
       "12        102   1724  \n",
       "11        112   1745  \n",
       "10        111   1752  \n",
       "9         113   1779  \n",
       "8         102   1772  \n",
       "7         100   1764  \n",
       "6         103   1781  \n",
       "5          95   1764  \n",
       "4          88   1763  \n",
       "3          93   1788  \n",
       "2         122   1855  \n",
       "1         115   1875  \n",
       "0         124   1875  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lugar_nacimiento_lespreses=lugar_nacimiento.sort_values(by='Año', ascending=True)\n",
    "lugar_nacimiento_lespreses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f7fb82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "serie_extranjeros_lespreses = lugar_nacimiento_lespreses[['Año', 'Estranger']]\n",
    "serie_extranjeros_lespreses= lugar_nacimiento_lespreses.set_index('Año')\n",
    "serie_extranjeros_lespreses.index = pd.to_datetime(serie_extranjeros_lespreses.index, format='%Y')\n",
    "serie_extranjeros_lespreses = serie_extranjeros_lespreses['Estranger']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0461a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de filas con missing values: 0.0\n"
     ]
    }
   ],
   "source": [
    "print(f'Número de filas con missing values: {lugar_nacimiento.isnull().any(axis=1).mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a9305386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fechas train : 2000-01-01 00:00:00 --- 2015-01-01 00:00:00  (n=16)\n",
      "Fechas test  : 2016-01-01 00:00:00 --- 2022-01-01 00:00:00  (n=7)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Separación datos train-test\n",
    "# ==============================================================================\n",
    "steps = 7 #se utilizan los últimos 7 años como conjunto test para evaluar la capacidad del modelo\n",
    "datos_train = serie_extranjeros_lespreses[:-steps]\n",
    "datos_test  = serie_extranjeros_lespreses[-steps:]\n",
    "\n",
    "print(f\"Fechas train : {datos_train.index.min()} --- {datos_train.index.max()}  (n={len(datos_train)})\")\n",
    "print(f\"Fechas test  : {datos_test.index.min()} --- {datos_test.index.max()}  (n={len(datos_test)})\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "datos_train.plot(ax=ax, label='train')\n",
    "datos_test.plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8eea12e",
   "metadata": {},
   "source": [
    "### ForecasterAutoreg\n",
    "\n",
    "Se crea y entrena un modelo ForecasterAutoreg a partir de un regresor RandomForestRegressor y una ventana temporal de 7 lags. Esto último significa que, el modelo, utiliza como predictores los 7 años anteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0d20f04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#datos_test.index = pd.to_datetime(datos_test.index, format='%Y-%m-%d')\n",
    "#datos_train.index = pd.to_datetime(datos_train.index, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "281d1c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Año\n",
       "2016-01-01    103\n",
       "2017-01-01     95\n",
       "2018-01-01     88\n",
       "2019-01-01     93\n",
       "2020-01-01    122\n",
       "2021-01-01    115\n",
       "2022-01-01    124\n",
       "Name: Estranger, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7cdc83f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = pd.infer_freq(datos_train.index)\n",
    "datos_train.index = pd.date_range(start=datos_train.index[0], periods=len(datos_train), freq=freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "597eaf93",
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = pd.infer_freq(datos_test.index)\n",
    "datos_test.index = pd.date_range(start=datos_test.index[0], periods=len(datos_test), freq=freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22dcdce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "================= \n",
       "ForecasterAutoreg \n",
       "================= \n",
       "Regressor: RandomForestRegressor(random_state=123) \n",
       "Lags: [1 2 3 4 5 6 7] \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Window size: 7 \n",
       "Weight function included: False \n",
       "Exogenous included: False \n",
       "Type of exogenous variable: None \n",
       "Exogenous variables names: None \n",
       "Training range: [Timestamp('2000-01-01 00:00:00'), Timestamp('2015-01-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: AS-JAN \n",
       "Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} \n",
       "Creation date: 2023-04-11 17:28:01 \n",
       "Last fit date: 2023-04-11 17:28:01 \n",
       "Skforecast version: 0.7.0 \n",
       "Python version: 3.10.5 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear y entrenar forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterAutoreg(\n",
    "                regressor = RandomForestRegressor(random_state=123),\n",
    "                lags = 7\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=datos_train)\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cae648",
   "metadata": {},
   "source": [
    "### Predicciones\n",
    "\n",
    "Una vez entrenado el modelo, se predicen los datos de test (5 años a futuro)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f09f9082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    103.42\n",
       "2017-01-01    104.76\n",
       "2018-01-01    104.58\n",
       "2019-01-01    104.70\n",
       "2020-01-01    104.46\n",
       "2021-01-01    104.82\n",
       "2022-01-01    104.96\n",
       "Freq: AS-JAN, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps = 7\n",
    "predicciones = forecaster.predict(steps=steps)\n",
    "predicciones.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e846c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "datos_train.plot(ax=ax, label='train')\n",
    "datos_test.plot(ax=ax, label='test')\n",
    "predicciones.plot(ax=ax, label='predicciones')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e2eb62",
   "metadata": {},
   "source": [
    "### Error de las predicciones en el conjunto de test\n",
    "\n",
    "Se cuantifica el error que comete el modelo en sus predicciones. En este caso, se emplea como métrica el mean squared error (mse)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b519ed48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error de test (mse): 183.00371428571438\n"
     ]
    }
   ],
   "source": [
    "# Error test\n",
    "\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = datos_test,\n",
    "                y_pred = predicciones\n",
    "            )\n",
    "\n",
    "print(f\"Error de test (mse): {error_mse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79906e4",
   "metadata": {},
   "source": [
    "### Ajuste de hiperparámetros (tuning)\n",
    "\n",
    "El ForecasterAutoreg entrenado ha utilizado una ventana temporal de 6 lags y un modelo Random Forest con los hiperparámetros por defecto. Sin embargo, no hay ninguna razón por la que estos valores sean los más adecuados. Para identificar la mejor combinación de lags e hiperparámetros, la librería Skforecast dispone de la función grid_search_forecaster con la que comparar los resultados obtenidos con cada configuración del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2bf4c609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of models compared: 12.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loop lags_grid:   0%|                                               | 0/2 [00:00<?, ?it/s]\n",
      "loop param_grid:   0%|                                              | 0/6 [00:00<?, ?it/s]\u001b[A\n",
      "loop param_grid:  17%|██████▎                               | 1/6 [00:00<00:00,  6.29it/s]\u001b[A\n",
      "loop param_grid:  33%|████████████▋                         | 2/6 [00:00<00:02,  1.93it/s]\u001b[A\n",
      "loop param_grid:  50%|███████████████████                   | 3/6 [00:01<00:01,  2.75it/s]\u001b[A\n",
      "loop param_grid:  67%|█████████████████████████▎            | 4/6 [00:01<00:01,  1.90it/s]\u001b[A\n",
      "loop param_grid:  83%|███████████████████████████████▋      | 5/6 [00:02<00:00,  2.53it/s]\u001b[A\n",
      "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:02<00:00,  1.90it/s]\u001b[A\n",
      "loop lags_grid:  50%|███████████████████▌                   | 1/2 [00:02<00:02,  2.84s/it]\u001b[A\n",
      "loop param_grid:   0%|                                              | 0/6 [00:00<?, ?it/s]\u001b[A\n",
      "loop param_grid:  17%|██████▎                               | 1/6 [00:00<00:00,  5.86it/s]\u001b[A\n",
      "loop param_grid:  33%|████████████▋                         | 2/6 [00:00<00:02,  1.94it/s]\u001b[A\n",
      "loop param_grid:  50%|███████████████████                   | 3/6 [00:01<00:01,  2.84it/s]\u001b[A\n",
      "loop param_grid:  67%|█████████████████████████▎            | 4/6 [00:01<00:01,  1.96it/s]\u001b[A\n",
      "loop param_grid:  83%|███████████████████████████████▋      | 5/6 [00:02<00:00,  2.57it/s]\u001b[A\n",
      "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:02<00:00,  1.93it/s]\u001b[A\n",
      "loop lags_grid: 100%|███████████████████████████████████████| 2/2 [00:05<00:00,  2.82s/it]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10] \n",
      "  Parameters: {'max_depth': 3, 'n_estimators': 500}\n",
      "  Backtesting metric: 65.35105600000006\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grid search de hiperparámetros\n",
    "\n",
    "steps = 7\n",
    "forecaster = ForecasterAutoreg(\n",
    "                regressor = RandomForestRegressor(random_state=123),\n",
    "                lags      = 7 # Este valor será remplazado en el grid search\n",
    "             )\n",
    "\n",
    "# Lags utilizados como predictores\n",
    "lags_grid = [5,10]\n",
    "\n",
    "# Hiperparámetros del regresor\n",
    "param_grid = {'n_estimators': [100, 500],\n",
    "              'max_depth': [3, 5, 7]}\n",
    "\n",
    "resultados_grid = grid_search_forecaster(\n",
    "                        forecaster         = forecaster,\n",
    "                        y                  = datos_train,\n",
    "                        param_grid         = param_grid,\n",
    "                        lags_grid          = lags_grid,\n",
    "                        steps              = steps,\n",
    "                        refit              = True,\n",
    "                        metric             = 'mean_squared_error',\n",
    "                        initial_train_size = 15,\n",
    "                        fixed_train_size   = False,\n",
    "                        return_best        = True,\n",
    "                        verbose            = False\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d858489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lags</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>n_estimators</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>65.351056</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>65.383396</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 500}</td>\n",
       "      <td>65.383396</td>\n",
       "      <td>7</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>73.273600</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>73.273600</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 100}</td>\n",
       "      <td>73.273600</td>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>75.748011</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>79.744900</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 100}</td>\n",
       "      <td>79.744900</td>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 500}</td>\n",
       "      <td>79.793214</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 500}</td>\n",
       "      <td>80.174116</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 500}</td>\n",
       "      <td>80.174116</td>\n",
       "      <td>7</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               lags                                 params  \\\n",
       "7   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 3, 'n_estimators': 500}   \n",
       "9   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 5, 'n_estimators': 500}   \n",
       "11  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 7, 'n_estimators': 500}   \n",
       "6   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 3, 'n_estimators': 100}   \n",
       "8   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 5, 'n_estimators': 100}   \n",
       "10  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  {'max_depth': 7, 'n_estimators': 100}   \n",
       "0                   [1, 2, 3, 4, 5]  {'max_depth': 3, 'n_estimators': 100}   \n",
       "2                   [1, 2, 3, 4, 5]  {'max_depth': 5, 'n_estimators': 100}   \n",
       "4                   [1, 2, 3, 4, 5]  {'max_depth': 7, 'n_estimators': 100}   \n",
       "1                   [1, 2, 3, 4, 5]  {'max_depth': 3, 'n_estimators': 500}   \n",
       "3                   [1, 2, 3, 4, 5]  {'max_depth': 5, 'n_estimators': 500}   \n",
       "5                   [1, 2, 3, 4, 5]  {'max_depth': 7, 'n_estimators': 500}   \n",
       "\n",
       "    mean_squared_error  max_depth  n_estimators  \n",
       "7            65.351056          3           500  \n",
       "9            65.383396          5           500  \n",
       "11           65.383396          7           500  \n",
       "6            73.273600          3           100  \n",
       "8            73.273600          5           100  \n",
       "10           73.273600          7           100  \n",
       "0            75.748011          3           100  \n",
       "2            79.744900          5           100  \n",
       "4            79.744900          7           100  \n",
       "1            79.793214          3           500  \n",
       "3            80.174116          5           500  \n",
       "5            80.174116          7           500  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resultados Grid Search\n",
    "\n",
    "resultados_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a089cf30",
   "metadata": {},
   "source": [
    "Los mejores resultados se obtienen si se utiliza una ventana temporal de 10 lags y una configuración de Random Forest {'max_depth': 3, 'n_estimators': 500}."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91422f3",
   "metadata": {},
   "source": [
    "### Modelo final\n",
    "\n",
    "Finalmente, se entrena de nuevo un ForecasterAutoreg con la configuración óptima encontrada mediante validación. Este paso no es necesario si se indica return_best = True en la función grid_search_forecaster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "056fe98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear y entrenar forecaster con mejores hiperparámetros\n",
    "\n",
    "regressor = RandomForestRegressor(max_depth=3, n_estimators=500, random_state=123)\n",
    "forecaster = ForecasterAutoreg(\n",
    "                regressor = regressor,\n",
    "                lags      = 10\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=datos_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9592c584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicciones\n",
    "predicciones = forecaster.predict(steps=steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6845e75e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "datos_train.plot(ax=ax, label='train')\n",
    "datos_test.plot(ax=ax, label='test')\n",
    "predicciones.plot(ax=ax, label='predicciones')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13dd830b",
   "metadata": {},
   "source": [
    "### Error del Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f186f120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error de test (mse) 185.91639049206339\n"
     ]
    }
   ],
   "source": [
    "error_mse = mean_squared_error(\n",
    "                y_true = datos_test,\n",
    "                y_pred = predicciones\n",
    "            )\n",
    "\n",
    "print(f\"Error de test (mse) {error_mse}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}