Serie temporal - Skforecast - ANGLES.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_170084ANGLES.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": 6,
   "id": "ed7c87e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>4119</td>\n",
       "      <td>2827</td>\n",
       "      <td>1292</td>\n",
       "      <td>637</td>\n",
       "      <td>253</td>\n",
       "      <td>5009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2001</td>\n",
       "      <td>4070</td>\n",
       "      <td>2774</td>\n",
       "      <td>1296</td>\n",
       "      <td>614</td>\n",
       "      <td>289</td>\n",
       "      <td>4973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2002</td>\n",
       "      <td>4044</td>\n",
       "      <td>2748</td>\n",
       "      <td>1296</td>\n",
       "      <td>600</td>\n",
       "      <td>376</td>\n",
       "      <td>5020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2003</td>\n",
       "      <td>4003</td>\n",
       "      <td>2709</td>\n",
       "      <td>1294</td>\n",
       "      <td>593</td>\n",
       "      <td>453</td>\n",
       "      <td>5049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2004</td>\n",
       "      <td>4003</td>\n",
       "      <td>2687</td>\n",
       "      <td>1316</td>\n",
       "      <td>598</td>\n",
       "      <td>495</td>\n",
       "      <td>5096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2005</td>\n",
       "      <td>4006</td>\n",
       "      <td>2675</td>\n",
       "      <td>1331</td>\n",
       "      <td>568</td>\n",
       "      <td>617</td>\n",
       "      <td>5191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>4013</td>\n",
       "      <td>2667</td>\n",
       "      <td>1346</td>\n",
       "      <td>554</td>\n",
       "      <td>644</td>\n",
       "      <td>5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2007</td>\n",
       "      <td>3999</td>\n",
       "      <td>2655</td>\n",
       "      <td>1344</td>\n",
       "      <td>536</td>\n",
       "      <td>746</td>\n",
       "      <td>5281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2008</td>\n",
       "      <td>3981</td>\n",
       "      <td>2654</td>\n",
       "      <td>1327</td>\n",
       "      <td>534</td>\n",
       "      <td>931</td>\n",
       "      <td>5446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2009</td>\n",
       "      <td>4018</td>\n",
       "      <td>2672</td>\n",
       "      <td>1346</td>\n",
       "      <td>525</td>\n",
       "      <td>1026</td>\n",
       "      <td>5569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2010</td>\n",
       "      <td>4009</td>\n",
       "      <td>2680</td>\n",
       "      <td>1329</td>\n",
       "      <td>516</td>\n",
       "      <td>1035</td>\n",
       "      <td>5560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>4062</td>\n",
       "      <td>2712</td>\n",
       "      <td>1350</td>\n",
       "      <td>508</td>\n",
       "      <td>1067</td>\n",
       "      <td>5637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>4095</td>\n",
       "      <td>2727</td>\n",
       "      <td>1368</td>\n",
       "      <td>491</td>\n",
       "      <td>1133</td>\n",
       "      <td>5719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>4077</td>\n",
       "      <td>2713</td>\n",
       "      <td>1364</td>\n",
       "      <td>476</td>\n",
       "      <td>1109</td>\n",
       "      <td>5662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>4058</td>\n",
       "      <td>2691</td>\n",
       "      <td>1367</td>\n",
       "      <td>455</td>\n",
       "      <td>1093</td>\n",
       "      <td>5606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>4023</td>\n",
       "      <td>2670</td>\n",
       "      <td>1353</td>\n",
       "      <td>448</td>\n",
       "      <td>1069</td>\n",
       "      <td>5540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>4014</td>\n",
       "      <td>2656</td>\n",
       "      <td>1358</td>\n",
       "      <td>436</td>\n",
       "      <td>1023</td>\n",
       "      <td>5473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017</td>\n",
       "      <td>3998</td>\n",
       "      <td>2661</td>\n",
       "      <td>1337</td>\n",
       "      <td>421</td>\n",
       "      <td>1060</td>\n",
       "      <td>5479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>3977</td>\n",
       "      <td>2630</td>\n",
       "      <td>1347</td>\n",
       "      <td>401</td>\n",
       "      <td>1141</td>\n",
       "      <td>5519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>3988</td>\n",
       "      <td>2618</td>\n",
       "      <td>1370</td>\n",
       "      <td>395</td>\n",
       "      <td>1216</td>\n",
       "      <td>5599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>3956</td>\n",
       "      <td>2602</td>\n",
       "      <td>1354</td>\n",
       "      <td>388</td>\n",
       "      <td>1310</td>\n",
       "      <td>5654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>3938</td>\n",
       "      <td>2588</td>\n",
       "      <td>1350</td>\n",
       "      <td>397</td>\n",
       "      <td>1374</td>\n",
       "      <td>5709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>3938</td>\n",
       "      <td>2563</td>\n",
       "      <td>1375</td>\n",
       "      <td>377</td>\n",
       "      <td>1367</td>\n",
       "      <td>5682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Año  Catalunya  Mateixa comarca  Altra comarca  Resta d'Espanya  \\\n",
       "22  2000       4119             2827           1292              637   \n",
       "21  2001       4070             2774           1296              614   \n",
       "20  2002       4044             2748           1296              600   \n",
       "19  2003       4003             2709           1294              593   \n",
       "18  2004       4003             2687           1316              598   \n",
       "17  2005       4006             2675           1331              568   \n",
       "16  2006       4013             2667           1346              554   \n",
       "15  2007       3999             2655           1344              536   \n",
       "14  2008       3981             2654           1327              534   \n",
       "13  2009       4018             2672           1346              525   \n",
       "12  2010       4009             2680           1329              516   \n",
       "11  2011       4062             2712           1350              508   \n",
       "10  2012       4095             2727           1368              491   \n",
       "9   2013       4077             2713           1364              476   \n",
       "8   2014       4058             2691           1367              455   \n",
       "7   2015       4023             2670           1353              448   \n",
       "6   2016       4014             2656           1358              436   \n",
       "5   2017       3998             2661           1337              421   \n",
       "4   2018       3977             2630           1347              401   \n",
       "3   2019       3988             2618           1370              395   \n",
       "2   2020       3956             2602           1354              388   \n",
       "1   2021       3938             2588           1350              397   \n",
       "0   2022       3938             2563           1375              377   \n",
       "\n",
       "    Estranger  Total  \n",
       "22        253   5009  \n",
       "21        289   4973  \n",
       "20        376   5020  \n",
       "19        453   5049  \n",
       "18        495   5096  \n",
       "17        617   5191  \n",
       "16        644   5211  \n",
       "15        746   5281  \n",
       "14        931   5446  \n",
       "13       1026   5569  \n",
       "12       1035   5560  \n",
       "11       1067   5637  \n",
       "10       1133   5719  \n",
       "9        1109   5662  \n",
       "8        1093   5606  \n",
       "7        1069   5540  \n",
       "6        1023   5473  \n",
       "5        1060   5479  \n",
       "4        1141   5519  \n",
       "3        1216   5599  \n",
       "2        1310   5654  \n",
       "1        1374   5709  \n",
       "0        1367   5682  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lugar_nacimiento_angles=lugar_nacimiento.sort_values(by='Año', ascending=True)\n",
    "lugar_nacimiento_angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f7fb82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "serie_extranjeros_angles = lugar_nacimiento_angles[['Año', 'Estranger']]\n",
    "serie_extranjeros_angles = lugar_nacimiento_angles.set_index('Año')\n",
    "serie_extranjeros_angles.index = pd.to_datetime(serie_extranjeros_angles.index, format='%Y')\n",
    "serie_extranjeros_angles = serie_extranjeros_angles['Estranger']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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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PSTpYUqUxZoWkKUrOVg9KmmmSJ+LOtdb+xFr7sTHmESUnCMUknWutjTuv8zNJz0vySrrHWvtxGj4PAADIN7GYAk/dL//0+2XLeyhyyQ2K77Kn21Xha7Zm9vrJm9g8dTPHXyvp2k1sf1bSs9tUHQAAwGaY1csV+tu18i75NHkpy1PPlwqL3S4Lm8AViQAAQPaxVr7Z0xV8+HbJH1T43CsVH32w21VhMwidAAAgq5i6agWn/lG+D95QbLe91frDS2TLK90uC1tA6AQAAFnD+9bLCt37Z6k1otZTL1DbuONY6D1LEDoBAEDmCzcr+M+/yv/Kc4oPHKrIjy+X7bu921VhGxA6AQBARvN89oFCf79Wpnqdoseepuh3TpN8RJhsw98YAADITG1RBZ64V/5nH5bt0UfhK/6qxOBd3a4KKSJ0AgCAjONZsVjBO6+V9/NFajtoolpP+akUKnS7LHQAoRMAAGSOREL+GY8r8NjfZQuKFb7wOsX33M/tqtAJCJ0AACAjmOq1Ct59vXzz31Fsz/3VeuYvZLuVu10WOgmhEwAAuM73+iwFp90oxWOKnPELxQ46mqWQcgyhEwAAuKc1rOC0m+R/5XnFB++qyNmXyfbq53ZVSANCJwDkMGutDL1FyFBm5VKFbr1SnlXLFD12sqLHnip5iSa5ir9ZAMhBrXGrWz5s1E0fNqlHgUejewS0d8+A9u4R0G4Vfvk8BFG4y/fK8wred6NsqECRX/5J8V1HuV0S0ozQCQA55qUvIrr49XotbIjpyAEh+T3Sy6ta9cjisCSp0Ge0Z6V/QxAd3TOgypDX5aqRN1ojCv7zFvlfflaxnUeo9ZzfyJZ1d7sqdAFCJwDkiDUtcV0+r16PLQ5rhxKvHju8uw7rH5KUHGZf3hzXvLVRvbk2qnnrovrrR02K2eRzdyjxau+eAe3j9IYOK6c3FJ3PfLFMoVunyPPFMkW/c6qix01mOD2P8DcNAFkunrCa+mmzrnmnQZG41a9GlOii4SUq8G0MjcYYbVfs03bFPn1vx+QC2+GY1XvV0Q1B9KUvWvXIomRvaJHPaGSlX6N7bhyW705vKDrA9+oMBe+7QTYQUuTiPyo+fG+3S0IXI3QCQBZ7d31UF71Wp/eq23Rw36D+PKZUg0v9W/XcAp/Rvr2C2rdXUFKyN3RZk9Mbui4ZRm/6sElxpzd0UDev9u4R0OieQe3dM6BhZT556Q3FlkRbk8PpLz2j+E57KHLOb2TLK92uCi4gdAJAFqprTeiadxo09dNm9SzwaOpB5fruDgUdmqlujNHAEp8Glvg0aVCyN7QlltC769s2BNFZK1v1sNMbWuwzGtkj0G6Skl8V9IaiHbPqc4VuvVLeFYsVPeYHiv7f6Qyn5zH+5gEgi1hr9ejisK6YV6/1kYR+tEuRLh/ZTaUBT1rer9Dn0f69g9q/91d7Q99cm+wJfWNtVDd+2LihN3RwN5/2rPRrcKlPg7v5NKibT4NKfSrxp6c+ZC7f6/9T8N4/S/6Awhf/QfHd93G7JLiM0AkAWWJBXZt+MbdeL69q1chKvx45rLtGVAa6tIb2vaEnOL2hzW0JvbO+TfPWJc8Nnbs2qscWh2XbPa93gUeDnCA62Amig7slXyfgZYg+p0RbFXzgVvlffErxocOTw+kVPd2uChmA0AkAGS4cs/rL+426+aNGFfiM/rJvqU4fWpQx51MW+T06oE9QB/QJbtgWjlktaYxpYX1MixpiWtiQvP/0soiqWxMbjvMaaftirwaXJntF2/eQ9i3yysPC9lnFrF6h0G1T5P18kaJHn6Lo985kOB0b0BIAIIPNWB7RL+fWaVlTXCcMKtA1e5eqZ0HmnzdZ4DMaVu7XsPJvTmqqbU0kg2h9Mowucn6+srpFLbGN/aMFXqMdu3m/EkS/vM+5o5nH98ZsBe/5k+T1K/zz6xXfY4zbJSHDEDoBIAOtaIrp12/W66llEQ0t9Wn6EZU6sF1PYjYrD3o0qkdAo3p89dQAa61WtSS+EkQXNsT0cU1MzyyLqF0eVXnQJIfqS/0bziPdq0cgbee2YjOirQo+eJv8L0xPXjv9p1NkuzOcjm8idAJABmlLWP1tfpOuf7dRcWv1m5HddN5uxXlx3qMxRn2LvOpb5P1GwG5LWH3eGN8QRL8MpS9/0aqHFrYkny9plzJfciZ9z+Ss+sGlPobo08isWaHQbVfJu6xK0aNOUvR7P5R8RAtsGi0DADLE3DWt+vnrdZpfG9OE/kH9YUyZBpbwNS1Jfo/RoNLkBKQJX9vXEE3onfXRDTPqn1wa1n0LkkG0LGC0d7vLfY6sDKgbvaGdwvvmiwpN/aPk9Sp80XWKj9jP7ZKQ4fg2AwCX1UTimvJWg+6valG/Qq/uP7RCE7cLdWjNzXzSLeDRwX1DOrhv8pKfCWtVVR/bcLnPeWuj+t+7jbJyekPLfV+57vzgbj7+rLdFW1SBh25XYNZ/FB80TJFzp8h27+V2VcgChE4AcEnCWj1Q1aIpbzWoPprQebsV65IRJSpmTcsO8Rijncr82qnMr1OHFkmS6qMJvb1u43Xn/700rH84vaHlQac3tIfTG9ojwLqi38KsWekMpy9Q9IgTFJ30I8m3dVfAAgidAOCCj2vadPHrdZq7NqoxPQP6y75l2rWCX97pUhrw6NB+IR3ab2Nv6AKnN/TLYfkZKxolSR4jDSv3b+wN7RHQjt28ed8b6p33UnI43RiFL7hG8ZFj3S4JWYbQCQBdqKktoT+816jbP25SacCjv+5fpu8PKWSySxfzGKOdy/zaucyv05ze0LrWhN5at/G6848tbtE9nzVLkroHPRrVc+MlPysTm3v1HNMWVeBfdyow83HFd9xFkZ/+VrZHH7erQhYidAJAF1jaGNPzyyO65cMmrWyJ67Shhbpyr26sN5lByoIeHdY/pMP6J3tD4wmrz+pjG647P29tVM8vj0iSZubJFR3NulUK3XalvEs+U3T88Yqe+GOG05EyQicApEEsYTVvXTKkPL88ok/qYpKkPbr7NfXgco3plRtrbuYyr2fjAveTd0r2hta2JvRhTZvKmj53ubr08741R6Gp10uSwuf9TvFRB7hcEbIdoRMAOklda0KzViZD5syVEdW2WvmMtH/voE4dWqQJ/UMaVMrXbjYrD3p0YJ+gqqrcriRN4jF5P31PvjnPyf/6/xTfYSdFzr2S4XR0Cr79ACBF1lma5/nlET23IqK5a6KK2+T5fxP6h3TEgAId0i/IVXKQ2RIJeRZ+JN/c2fLNe0mehlrZUEFydvrxP5T8gS2/BrAVCJ0AsA2icavX17TqOWfYfHFjXJK0a7lPFw4v1oQBIe1VGZDXw8QgZDBr5Vn6WTJovvmCPDXrZP0BxUfsq9Z9Dk1eNz3AKSDoXIROANiCdeG4Zq6I6PkVEc1e2arGNqugVzqwd1Dn7las8f1DGlDM1ykyn2fF4mTQfGO2PGu/kPX6FB++t6KTzlZsz/2lgkK3S0QO41sSAL7GWquPa51h8+VhvbWuTVZS7wKPvrtDgSYMCOmgPkEVsYA4soBZvUK+N5JB07tyqazxKD5spKLH/ECxvQ6QikrcLhF5gtAJAJLCMas5q1r1/IrksPmK5uSw+chKvy7ds0QT+oe0R3d/3i8QjuxgqtfI98YLyaC5dIEkKT50d0VOu1DxUQfKlla4XCHyEaETQF5qjVutaonr36u9endZtV76olXhuFWRz+iQvkFdMqJE4/uH1KuQdTSRHUxdtXzzXkoGzaqPJEnxHXZW68k/VWzvg2W793S5QuQ7QieAnBCNW62PJLQ+End+JrQuklB1JK514eTj6khC6yJxVUcSamizzjOD2q64TT8YWqgjBoS0f6+gQj56M5Elmhrke+vlZND85D0Zm1C8/45qPf6Hio0+RLZXP7crBDYgdALISNG4VXVrQuvCyZD4lRAZaRciw3Gtb02oIWo3+To+I1WGPOoe8qgy5NVePQLqHvSoR4FXPUIe9Qqv0oQ9BjFsjuwRbpHvnVeSQfOjeTLxuBK9+qntOz9QbPQhSvTfwe0KgU0idALICI8satE9nzZrndNTWf8tIdLbLkT2CHm1Z2VAlSGPc/OqsiB5v4fzuDRgNhsoq6osgROZL9oq7/uvyz93trzvz5VpiypR0VNtEyYpts+hSmw/RKIdI8MROgG4ylqrP73fqOvebdSwMp/26L4xRPYIeZ0eSo96FGwMkR5+uSLPBB6+Q4FZ/1GitFxtB09MBs1BwyQPKyggexA6AbgmlrD6+et1mragRScOKtBf9y9XwEugBL6ubdxxio86UPGd95A8TG5DdiJ0AnBFU1tCZ75YoxkrWvWL3Ut0+cgShrmBb2H7DVS830C3ywA6ZIv98saYe4wxa40xH7XbVmGMmWmMqXJ+ljvbjTHmFmPMQmPMB8aYke2eM9k5vsoYMzk9HwdANljTEtfE/67X/1a26qb9ynTFXt0InACQ47bmZJB/SDria9sulTTLWjtE0iznsSQdKWmIcztb0h1SMqRKmiJpH0mjJU35MqgCyC9V9W06/Jl1WlAf04PjKnT6TkVulwQA6AJbDJ3W2pcl1Xxt87GS7nPu3yfpuHbbp9mkuZLKjDF9JE2QNNNaW2OtrZU0U98MsgBy3Nw1rRr/zDq1xKyePqJSRwwocLskAEAXSXXaWy9r7Srn/mpJvZz7/SQtb3fcCmfbt20HkCeeXBrWsc+vV0XQo5lH99DIHgG3SwIAdKEOTySy1lpjzKYX1OuAqqqqzn5J5DjaTOZ6+Aufbljs124lCd2wS4tiaxpVtcbtqjai7SAVtBukIpfbzZAhQza7P9XQucYY08dau8oZPl/rbF8paUC74/o721ZKOvhr21/c3BtsqXCgvaqqKtpMBkpYq9/Oa9Cti5t09HYh3XVQuQp9mbWuIG0HqaDdIBX53m5S/fafLunLGeiTJT3Zbvtpziz2MZLqnWH45yWNN8aUOxOIxjvbAOSoSMzqrBdrdevHTfrRzkWadkhFxgVOAEDX2WJPpzHmISV7KSuNMSuUnIV+vaRHjDFnSVom6QTn8GclHSVpoaQWSWdIkrW2xhjzO0nznOOuttZ+fXISgBxR15rQKbOq9dqaqK4e1U3n7VbMkkgAkOe2GDqttSd/y65xmzjWSjr3W17nHkn3bFN1ALLO500xTZpRrcWNMd19ULmO37HQ7ZIAABmAKxIB6DQfVEd1wsxqtcStHh9fqQP7BN0uCQCQIQidADrF7JURnTa7RqUBj547qlLDyv1ulwQAyCCc1Q+gwx6satYJM6u1fYlXMyf2IHACAL6Bnk4AKbPW6s/vN+radxt1cN+gph1SoW4B/i8LAPgmQieAlMQSVhe/Xqf7FrToxEEF+uv+5Qp4maEOANg0QieAbdbUltCZL9ZoxopW/WL3El0+soQlkQAAm0XoBLBN1objOvF/1Xq/uk037lumM3YucrskAEAWIHQC2GpV9W06fka11kUSenBchY4YUOB2SQCALEHoBLBV3ljTqpNmVctrjJ46olJ79Qi4XRIAIIswzRTAFk1fGtaxz69XecCjmUf3IHACALYZoRPAZt05v0mTX6jR8Aq/ZkzsoR26MUACANh2/PYAsEkJazXlrQb99aMmHbVdSHcfVK5CH/9PBQCkhtAJ4Bustfrxy7V6dHFYP9y5SH/Yp1ReD0siAQBSR+gE8A2PLQ7r0cVhXTqiRJeMYA1OAEDHMVYG4Cua2xKa8la99uju1y/3IHACADoHPZ0AvuLGD5r0RUtC9xxcwZA6AKDT0NMJYIOljTH99eNGTdqxQGN6Bd0uBwCQQwidADa44s16eY3RlaNK3S4FAJBjCJ0AJEkvfRHR059H9PPdS9SvyOt2OQCAHEPoBKBYwurSN+q1fbFXP9u12O1yAAA5iIlEADT102Z9UhfT/YdWKORj8hAAoPPR0wnkuepIXNe926CD+gQ1cbuQ2+UAAHIUoRPIc9e926imNqvr9yllTU4AQNoQOoE89mFNm+79rFln7VykXcr9bpcDAMhhhE4gT1lrdekbdSoLeHTZnt3cLgcAkOMInUCeenJpRK+ujurykSUqC/JVAABIL37TAHmoJZbQFfPqtWu5T6cPLXK7HABAHmDJJCAP3fJhk1Y0x/W3Ayu5vjoAoEvQ0wnkmeVNMd38YZOOG1igsb25vjoAoGsQOoE889t5DbKyunpvJg8BALoOoRPII6+sbtUTS8O6YHiJtivm7BoAQNchdAJ5Iu5cX71/kVcXDOf66gCArkVXB5An7lvQoo9q2nTvweUq9PH/TQBA1+I3D5AH6loTuuadBu3fO6DjBha4XQ4AIA8ROoE8cN27DaqLJnT9PmVcXx0A4ApCJ5Dj5te2aeqnzTp9aJGGV3B9dQCAOwidQA6z1urXb9Sr2G90+cgSt8sBAOQxQieQw57+PKKXVrXqsj27qXvI63Y5AIA8RugEclQkZnXFm/Xapcyns3bm+uoAAHexZBKQo277uEnLmuJ6ckJ3+bi+OgDAZfR0Ajnoi+a4bvigURO3C+mgviG3ywEAgNAJ5KIr36pXzFpdM7rU7VIAAJBE6ARyzhtrWvXI4rDO27VEA0s4gwYAkBkInUAOSVirS96oV99Cjy7aneurAwAyR4dCpzHmImPMx8aYj4wxDxljQsaYHYwxbxhjFhpj/mWMCTjHBp3HC539AzvlEwDY4J9VLXqvuk1XjSpVkZ//UwIAMkfKv5WMMf0knS9plLV2N0leSSdJ+oOkG621gyXVSjrLecpZkmqd7Tc6xwHoJPXRhH73doPG9Azo+B25vjoAILN0tCvEJ6nAGOOTVChplaRDJT3m7L9P0nHO/WOdx3L2jzNcBBroNH98r1HrIwldv08p11cHAGSclEOntXalpD9L+lzJsFkv6W1JddbamHPYCkn9nPv9JC13nhtzju+e6vsD2GhBXZvunN+kU4cWakRlwO1yAAD4hpSnthpjypXsvdxBUp2kRyUd0TllSVVVVZ31UsgT+dpmrJUumB9UyOPR98vWq6pqvdslZZ18bTvoGNoNUpHL7WbIkCGb3d+R9VQOk7TEWrtOkowx/5a0v6QyY4zP6c3sL2mlc/xKSQMkrXCG40slVadaONBeVVVV3raZ55aH9Xptja4dXap9du3vdjlZJ5/bDlJHu0Eq8r3ddOSczs8ljTHGFDrnZo6TNF/SC5KOd46ZLOlJ5/5057Gc/bOttbYD7w/kvda41WVv1GtIqU8/4vrqAIAMlnJPp7X2DWPMY5LekRST9K6kv0t6RtLDxphrnG1TnadMlXS/MWahpBolZ7oD6IC/zW/S4sa4Hju8uwJeJg8BADJXhy5XYq2dImnK1zYvljR6E8dGJE3qyPsB2Gh1S1x/eq9REwaEdFh/rq8OAMhsrB4NZKmr3m5Qa8Lqur25vjoAIPMROoEs9Na6qB5a2KKfDivWoFKurw4AyHyETiDLJKzVJXPr1KvAo1+MKHG7HAAAtgpdJECWeXhhi95e36bbx5aphOurAwCyBL+xgCzS2JbQVW83aK9Kv04aXOh2OQAAbDV6OoEs8pf3G7UmnNAD47rLw/XVAQBZhJ5OIEssbojp9o+bdPLgQo3qwfXVAQDZhdAJZInL3qxXwGM0Za9ubpcCAMA2I3QCWeCpZWE9tzyiX44oUe9Cr9vlAACwzQidQIZ7ellYZ71Yo90r/PrJsGK3ywEAICWETiCDPbKoRZNfqNEe3f2afkSlglxfHQCQpQidQIa699Nm/fjlWu3fO6gnJlSqLMg/VwBA9uK3GJCBbvmwURe9XqfxA0J65LDuKmYReABAlmOdTiCDWGt13buN+tP7jfruDgW688By+T0MqQMAsh+hE8gQ1lpd9ma97pjfrFOHFOqm/crkJXACAHIEoRPIAPGE1UWv12naghadM6xI140uleGKQwCAHELoBFzWlrD6ycu1enxJWL/co0SX7VlC4AQA5BxCJ+CiSMzq9Bdr9NzyiK4e1U3nDy9xuyQAANKC0Am4pKktoVNm1WjOqlbdsG+Zzty5yO2SAABIG0In4IK61oQmzVyvd9a36W8HluvEQYVulwQAQFoROoEuti4c13dnVOuzujb945AKHbN9gdslAQCQdoROoAutbI7r/55fr+VNcT18WHcd2i/kdkkAAHQJQifQRZY2xvSd59arrjWhf0/orn17Bd0uCQCALkPoBLrAp3VtOu659YompOlHVGpEZcDtkgAA6FKETiDN3lsf1fdmVMvnkZ45slK7lPvdLgkAgC7ncbsAIJfNXdOq7zy3XoV+o/8e1YPACQDIW4ROIE1eWBnRd2dUq2eBV/89slI7dmNgAQCQvwidQBo8syysE/9XrR27+fTfoyrVv5jACQDIb4ROoJM9uqhFp71Qo927+/X0EZXqUeB1uyQAAFxH6AQ60T8+a9bZL9dqv14BPTGhUmVB/okBACAROoFO89ePGnXha3Ua3z+oRw6vVImff14AAHyJE82ADrLW6vfvNeqP7zXq/wYW6M4DyxXwGrfLAgAgoxA6gQ6w1uryefW6/eNm/WBIoW7er0xeD4ETAICvI3QCKYonrC56vU7TFrToJ8OKdN3oUnkMgRMAgE0hdAIpaEtYnTOnVo8tDusXe5To8j1LZAicAAB8K0InsJWstVrUENMrq6N6dHGLXl0d1VWjuumC4SVulwYAQMYjdALfwlqrxQ1xvbK6dcNtVUtCktS7wKOb9yvT5J2KXK4SAIDsQOgEHNZaLWlMhsw5q74aMnsVeHRAn6DG9g5qbO+ABnXzMZwOAMA2IHQib1lrtbQxrjmrW/WKEzK/aBcykwEzqLF9AhpMyAQAoEMIncgb1lota4rrZSdgvroqqpUtcUlSz/Yhs3dAQ0oJmQAAdCZCJ3LWlyHzy6HyV1dHtaI5GTJ7hDwbejEP6B0kZAIAkGaETuSUpY2x5KSfVa16pV3IrHRC5oXDAzqgT1BDCZkAAHQpQiey3pKGmK5aEND7763W8qZkyOwe9Ghsn4AuHF6ssX2C2omQCQCAqwidyGprWuI69vn1Wh/26rD+fp2/W7HG9g5q5zJCJgAAmaRDodMYUybpbkm7SbKSzpT0maR/SRooaamkE6y1tSaZAG6WdJSkFkmnW2vf6cj7I781tSV04v+qtT6S0O27teq4kf3dLgkAAHwLTweff7Ok56y1O0vaQ9Inki6VNMtaO0TSLOexJB0paYhzO1vSHR18b+SxWMLqjBdq9EFNm+45uFy7liTcLgkAAGxGyqHTGFMq6UBJUyXJWhu11tZJOlbSfc5h90k6zrl/rKRpNmmupDJjTJ9U3x/5y1qrn79ep5krW/WXMWU6YkCB2yUBAIAt6EhP5w6S1km61xjzrjHmbmNMkaRe1tpVzjGrJfVy7veTtLzd81c424Bt8qf3GzVtQYt+sXuJztiZy1ACAJANOnJOp0/SSEnnWWvfMMbcrI1D6ZIka601xthUXryqqqoDpSFXPbXGq+uqgjq6Z0wnlKxRVdWaDftoM0gVbQepoN0gFbncboYMGbLZ/R0JnSskrbDWvuE8fkzJ0LnGGNPHWrvKGT5f6+xfKWlAu+f3d7Zt0pYKR/6ZtTKi6xZW6+C+Qd17WF8FvBtnp1dVVdFmkBLaDlJBu0Eq8r3dpDy8bq1dLWm5MWYnZ9M4SfMlTZc02dk2WdKTzv3pkk4zSWMk1bcbhgc26/3qqCbPrtFOZT5NO6TiK4ETAABkvo6u03mepAeMMQFJiyWdoWSQfcQYc5akZZJOcI59VsnlkhYquWTSGR18b+SJz5tiOnFmtcqCHj16eKW6BTq66AIAAOhqHQqd1tr3JI3axK5xmzjWSjq3I++H/FPXmtCkGdVqiVs9P6FSfYu8bpcEAABSwBWJkLEiMatTZlVrSWNMj4+v1C7lfrdLAgAAKSJ0IiMlrNU5c2r12pqoph5UrgP6BN0uCQAAdAAnxyEj/XZeg55YGtbVo7rpezsWul0OAADoIEInMs4dHzfp1o+b9KNdinTebsVulwMAADoBoRMZ5cmlYV32Zr2O3i6k60eXyhiWRgIAIBcQOpEx5q5p1dkv12jvHgHdfVCFvB4CJwAAuYLQiYxQVd+mk2dVq3+RVw8dVqECH4ETAIBcQuiE69a0xPW9GdXyGaPHx1eqe4i1OAEAyDUsmQRXNbUldOL/qrU+ktDTR1RqYAlNEgCAXMRveLgmlrA644UafVDTpofGddfIHgG3SwIAAGlC6IQrrLX6+et1mrmyVTftV6YJA0JulwQAANKIczrhij+936hpC1r0i91LdPpORW6XAwAA0ozQiS73QFWzrnu3UScNKtDlI0vcLgcAAHQBQie61OyVEV3wap0O7hvULfuXs/g7AAB5gtCJLvNBdVSnza7RTmU+TTukQgEvgRMAgHxB6ESX+LwpphNmVqss6NGjh1eqW4CmBwBAPmH2OtKurjWhSTOq1RK3en5CpfoWsfg7AAD5htCJtIrErE6ZVa0ljTE9Pr5Su5T73S4JAAC4gNCJtElYq3Pm1Oq1NVFNPahcB/QJul0SAABwCSfWIW1+O69BTywN63ejuul7Oxa6XQ4AAHARoRNpccfHTbr14yadvUuRfrZbsdvlAAAAlzG8jk5lrdXjS8K67M16TdwupN+PLmUtTgAAQOhE56htTehfi1o0bUGz5tfGNLpHQHcdVCGvh8AJAAAIneiAhLWasyqq+6ua9dSysFrj0p6Vft24b5lOGFSgAh+BEwAAJBE6sc2+aI7rwYUtun9Bs5Y1xVUaMJo8tEinDi3S8AqWRAIAAN9E6MRWaUtYzVge0bSqFs1cEVHCSgf0DuiKkd00cXt6NQEAwOYROrFZi+pj+mdVsx5c2KI14YR6F3h04fBi/WBIkXbsRvMBAABbh9SAbwjHrKYvC2vagma9ujoqr5HG9w/ptKGFOrx/SD4mBwEAgG1E6MQGH1RHdf+CFv1rcYsaolY7lHj127266eTBhepTyPXSAQBA6gidea4+mtDji5O9mu9Vtynolb6zfYFOHVqksb0D8rDGJgAA6ASEzjxkrdXra6KatqBZTy6NKBy32rXcpz/uU6pJgwpVHuRCVQAAoHMROvPI2nBcDy9s0bQFLVrYEFOJ3+ikwQU6bWiRRnT3c+UgAACQNoTOHJewVrNWtmragmb99/OIYlbat1dAF+1epuMGFqjIT68mAABIP0JnDlvdEtdP59Rq9hetqgx5dM6uxTp1SKGGlrGAOwAA6FqEzhz138/D+tkrdWqJWf15TKlOG1qkgJfhcwAA4A5CZ45piSX0m3kNmvpps3av8Ovug8rp2QQAAK4jdOaQD2va9MMXa/RZfUzn7VasK0Z2U5DeTQAAkAEInTkgYa3umN+sq96qV0XQoyfGd9ch/UJulwUAALABoTPLrWmJ66ev1GrWylYdOSCkW8eWqXuIqwcBAIDMQujMYs8tT04Wam6zumHfMp2xUyFrbQIAgIxE6MxC4ZjVb+fV665Pm7VbhV9TDyrXTkwWAgAAGYzQmWU+rmnTD1+q0Sd1Mf101yJN2auUyUIAACDjETqzhLVWd37SrClv1ass4NG/x3fXoUwWAgAAWYLQmQXWhuM6d06tZq5s1YQBId02tkyVTBYCAABZpMMX3jbGeI0x7xpjnnYe72CMecMYs9AY8y9jTMDZHnQeL3T2D+zoe+eDGcsj2v8/azVndav+NKZUD4+rIHACAICs0+HQKekCSZ+0e/wHSTdaawdLqpV0lrP9LEm1zvYbnePwLSIxq1/NrdMJ/6tWjwKPXjimp360SzGz0wEAQFbqUOg0xvSXdLSku53HRtKhkh5zDrlP0nHO/WOdx3L2jzMkqE2aX9umQ59aq79/0qyfDCvS7Ik9tUs5s9MBAED26ug5nTdJ+pWkEudxd0l11tqY83iFpH7O/X6SlkuStTZmjKl3jl/fwRpyhrVWf/+kWb99q16lAY8eO7y7DuvPZCEAAJD9Ug6dxpiJktZaa982xhzcaRU5qqqqOvslM1pNVLq6KqhXa73avzyu3w5pUUW4SXn2x9Ah+dZm0HloO0gF7QapyOV2M2TIkM3u70hP5/6SvmOMOUpSSFI3STdLKjPG+Jzezv6SVjrHr5Q0QNIKY4xPUqmk6lQLzyUzV0T007dq1dCW0B/3KdWPdini3M1tVFVVlVdtBp2HtoNU0G6QinxvNymf02mt/bW1tr+1dqCkkyTNttZ+X9ILko53Dpss6Unn/nTnsZz9s621NtX3zwWRmNUlc+s0aWa1eoQ8mj2xp84exmQhAACQe9KxTuclkh42xlwj6V1JU53tUyXdb4xZKKlGyaCat+bXJq8sNL82prN3KdJVo0pV4CNsAgCA3NQpodNa+6KkF537iyWN3sQxEUmTOuP9spm1Vnd/2qzfzKtXid+jRw7rrvEDmCwEAAByG1ck6kL10YTOmVOrZz+P6PB+Qd12QLl6FrDQOwAAyH2Ezi5SVd+mU2bVaElDTNeNLtU5w5gsBAAA8gehsws8vzyiH71UI7/H6D9HVGps76DbJQEAAHQpQmcaWWt1wwdNuuadBg2v8Ouf4yq0XTF/5AAAIP+QgNKkuS2hc1+p03+WhnX8jgW6Zf8yFfo641L3AAAA2YfQmQZLG2P6/qxqfVIX09Wjuum83Vh7EwAA5DdCZyd76YtWnfFijeLW6tHDu2tcP5ZDAgAAYLy3k1hrdfvHTfrujPXqWZC8uhCBEwAAIImezk4QiVld9HqdHlrYoqO2C+nOA8tV4ifPAwAAfInQ2UFfNMd16uxqvb2+TZeOKNGvRpTIw/mbAAAAX0Ho7IA31rTq1Bdq1NJm9c9DKzRx+wK3SwIAAMhIhM4U3fdZs34xt079i7x6ckKldin3u10SAABAxiJ0bqNo3OrXb9Zr6qfNOrRvUPccXKGyIOdvAgAAbA6hcxusC8d12gs1en1NVOfvVqwpe3WT18P5mwAAAFtC6NxK762P6geza7Q+EtddB5Zr0qBCt0sCAADIGoTOrfDIohad/2qtKkNePXdUD42oDLhdEgAAQFYhdG5GLGF15VsNuvXjJu3XK6D7DqlQjwKv22UBAABkHULnt6htTejMF2v0whet+tHORbpun1L5OX8TAAAgJYTOTZhf26bvz6rWiua4btm/TKcNLXK7JAAAgKxG6Pyap5aF9ZOXa1XsN3rmyEqN7hl0uyQAAICsR+h0JKzV9e816o/vNWqvSr/uP7S7+hZx/iYAAEBnIHRKaogm9OOXa/Xf5RGdMrhQN+xbppCP8zcBAAA6S96HzkX1MZ0yq1oLG2K6fp9S/XiXIhlD4AQAAOhMeRk6rbV6v7pN/1ka1j2fNctnjJ6YUKkD+3D+JgAAQDrkTehsHzT/szSspY1x+Yw0rl9QfxxTpu1L8uaPAgAAoMvldNKy1uqDmjb9Z0kyaC5pjMtrpIP7BnXx7iWauH2ByoMet8sEAADIeTkXOq21+rAm2aP5xJKNQfOgPkFdtHuJJm4XUkWIWekAAABdKSdCp7VWH9XG9J8lLXpiSViLnaB5oBM0j94upO4ETQAAANdkbej8Mmg+uSSsJ5a2aFFDMmge0CeoC4aXaOL2BE0AAIBMkVWh01qrj2tjyclAS8Ja2BCTx0gH9A7qvN2SQbOSoAkAAJBxMj50Wms1/8uguTSsqvpk0BzbO6hzdy3WxO1D6lFA0AQAAMhkGRs659e26YklYT25NKwF7YLmOcOKdQxBEwAAIKtkbOjc7z9r5THS/r0C+vGwUh2zfYF6EjQBAACyUsaGzr/sS9AEAADIFRkbOs/audjtEgAAANBJuBwPAAAA0o7QCQAAgLQjdAIAACDtCJ0AAABIO0InAAAA0o7QCQAAgLQjdAIAACDtCJ0AAABIO0InAAAA0i7l0GmMGWCMecEYM98Y87Ex5gJne4UxZqYxpsr5We5sN8aYW4wxC40xHxhjRnbWhwAAAEBm60hPZ0zSxdbaYZLGSDrXGDNM0qWSZllrh0ia5TyWpCMlDXFuZ0u6owPvDQAAgCyScui01q6y1r7j3G+U9ImkfpKOlXSfc9h9ko5z7h8raZpNmiupzBjTJ9X3BwAAQPbolHM6jTEDJe0p6Q1Jvay1q5xdqyX1cu73k7S83dNWONuADhsyZIjbJSBL0XaQCtoNUpHv7abDodMYUyzpcUkXWmsb2u+z1lpJtqPvAQAAgOzWodBpjPErGTgfsNb+29m85sthc+fnWmf7SkkD2j29v7MNAAAAOc6X6hONMUbSVEmfWGtvaLdruqTJkq53fj7ZbvvPjDEPS9pHUn27YXhJUmlpqUm1HgAAAGQukxwBT+GJxoyVNEfSh5ISzubLlDyv8xFJ20laJukEa22NE1JvlXSEpBZJZ1hr3+pY+QAAAMgGHZm9/oq11lhrd7fWjnBuz1prq62146y1Q6y1h0kqMsa8IOljSQdLusVaO1zS4m1dz9MYM9k5vsoYM7ljHx2ZrDPXgTXGxI0x7zm36W59JnSNFNrOzsaY140xrcaYX3zttY4wxnzmtKtLN/V+yA2d3G6WGmM+dL5z6FzJcSm0ne87v6c+NMa8ZozZo91r5fR3Tso9nVv9BsnzOvtYa98xxpRIelvJZZROl1Rjrb3e+YMtt9ZeYow5StJ5ko5Schj+ZmvtPsaYCklvSRql5OSktyXtZa2tTesHgCs6q904r9VkrS1243Og66XQdnpK2t45ptZa+2fndbySFkg6XMnVNuZJOtlaO7+LPxK6QGe1G+e1lkoaZa1d37WfAm5Ioe3sp+SpibXGmCMlXenknJz/zkn7ZTA7cT3PCZJmWmtrnKA5U8mheuQg1oFFqra17Vhr11pr50lq+9pLjZa00Fq72FoblfSw8xrIQZ3YbpBnUmg7r7XrMJur5MRqKQ++c7r02uumY+t5ss5nnupgu5GkkDHmLWPMXGPMcemvGJliK9vOt+E7J091sN1IydG4GcaYt40xZ6enSmSiFNrOWZL+69zP+e+clGevbyvztfU8jdk4Ud1aa40xrOeJb+ikdrO9tXalMWZHSbONMR9aaxelqWRkCL5zkIpOajdjne+cnpJmGmM+tda+nKaSkSG2te0YYw5RMnSO7dJCXdQlPZ2mc9bzZJ3PPNNJ7UbW2i9/Lpb0opL/C0UO28a28234zskzndRu2n/nrJX0hJLDpshh29p2jDG7S7pb0rHW2mpnc85/56Q9dBqzxfU8pW+u53maSRqjjet5Pi9pvDGm3JkBNt7ZhhzUWe3GaS9B5zUrJe0vKWdOysY3pdB2vs08SUOMMTsYYwKSTnJeAzmos9qNMabImUwiY0yRkr+rPur8ipEptrXtGGO2k/RvSadaaxe0Oz7nv3O6YvZ6p63naYw503muJF1rrb03rcXDNZ3VbpxZgnc6r+GRdJO1dmqXfhh0qRTaTm8lV8bo5hzfJGmYMzx2lKSbJHkl3WOtvbYrPwu6Tme1G0mVSvZuSslT2B6k3eS2FNrO3ZK+52yTpJi1dpTzWjn9nZP20AkAAAB06ex1AAAA5CdCJwAAANKO0AkAAIC0I3QCAAAg7QidAAAASDtCJwB0AWPMycaYNcaYg40x/zXGhNyuCQC6EqETADqRMeZFY0ztlxclaOdASQdIOl/SEmttpOurAwD3sE4nAHQSY8xASYsk1Uv6sbX2UXcrAoDMQU8nAHSe0yTNlfQPbbz8nYwx/zDG3GaMecYY02iMecMYM6jd/v2MMfOMMfXOz/26vnQASC9CJwB0ntMkPeDcJhhjerXbd5KkqySVS1oo6VpJMsZUSHpG0i2Suku6QdIzxpjuXVg3AKQdoRMAOoFz/eXtJT1irX1byWH2U9od8oS19k1rbUzJUDrC2X60pCpr7f3W2pi19iFJn0o6puuqB4D0I3QCQOeYLGmGtXa98/hBtRtil7S63f0WScXO/b6Sln3ttZZJ6peOIgHALT63CwCAbGeMKZB0giSvMebLcBmUVGaM2WMLT/9CyR7S9raT9FznVgkA7qKnEwA67jhJcUnDlBw2HyFpF0lzlDzPc3OelTTUGHOKMcZnjDnReZ2n01UsALiB0AkAHTdZ0r3W2s+ttau/vEm6VdL3tZlRJWtttaSJki6WVC3pV5ImthumB4CcwDqdAAAASDt6OgEAAJB2hE4AAACkHaETAAAAaUfoBAAAQNoROgEAAJB2hE4AAACkHaETAAAAaUfoBAAAQNoROgEAAJB2/w+eVpWnB+GB2wAAAABJRU5ErkJggg==\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_angles[:-steps]\n",
    "datos_test  = serie_extranjeros_angles[-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": 10,
   "id": "281d1c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Año\n",
       "2016-01-01    1023\n",
       "2017-01-01    1060\n",
       "2018-01-01    1141\n",
       "2019-01-01    1216\n",
       "2020-01-01    1310\n",
       "2021-01-01    1374\n",
       "2022-01-01    1367\n",
       "Name: Estranger, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "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 17:16:15 \n",
       "Last fit date: 2023-04-11 17:16:15 \n",
       "Skforecast version: 0.7.0 \n",
       "Python version: 3.10.5 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "f09f9082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    1078.10\n",
       "2017-01-01    1084.62\n",
       "2018-01-01    1086.18\n",
       "2019-01-01    1085.38\n",
       "2020-01-01    1083.70\n",
       "2021-01-01    1083.28\n",
       "2022-01-01    1084.08\n",
       "Freq: AS-JAN, Name: pred, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps = 7\n",
    "predicciones = forecaster.predict(steps=steps)\n",
    "predicciones.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "id": "b519ed48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error de test (mse): 34211.78657142857\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": 17,
   "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,  6.08it/s]\u001b[A\n",
      "loop param_grid:  33%|████████████▋                         | 2/6 [00:00<00:02,  1.81it/s]\u001b[A\n",
      "loop param_grid:  50%|███████████████████                   | 3/6 [00:01<00:01,  2.64it/s]\u001b[A\n",
      "loop param_grid:  67%|█████████████████████████▎            | 4/6 [00:01<00:01,  1.86it/s]\u001b[A\n",
      "loop param_grid:  83%|███████████████████████████████▋      | 5/6 [00:02<00:00,  2.50it/s]\u001b[A\n",
      "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:02<00:00,  1.83it/s]\u001b[A\n",
      "loop lags_grid:  50%|███████████████████▌                   | 1/2 [00:02<00:02,  2.93s/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,  6.28it/s]\u001b[A\n",
      "loop param_grid:  33%|████████████▋                         | 2/6 [00:01<00:02,  1.76it/s]\u001b[A\n",
      "loop param_grid:  50%|███████████████████                   | 3/6 [00:01<00:01,  2.55it/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,  2.51it/s]\u001b[A\n",
      "loop param_grid: 100%|██████████████████████████████████████| 6/6 [00:02<00:00,  1.94it/s]\u001b[A\n",
      "loop lags_grid: 100%|███████████████████████████████████████| 2/2 [00:05<00:00,  2.89s/it]\u001b[A"
     ]
    },
    {
     "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] \n",
      "  Parameters: {'max_depth': 5, 'n_estimators': 100}\n",
      "  Backtesting metric: 882.0900000000026\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\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": 18,
   "id": "0d858489",
   "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>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>2</th>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>882.090000</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>882.090000</td>\n",
       "      <td>7</td>\n",
       "      <td>100</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>949.995684</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>949.995684</td>\n",
       "      <td>7</td>\n",
       "      <td>500</td>\n",
       "    </tr>\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>1001.849104</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>1001.849104</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>1001.849104</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>1017.610000</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>1017.610000</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>1017.610000</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>1239.683070</td>\n",
       "      <td>3</td>\n",
       "      <td>500</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>1324.377664</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               lags                                 params  \\\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",
       "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",
       "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",
       "1                   [1, 2, 3, 4, 5]  {'max_depth': 3, 'n_estimators': 500}   \n",
       "0                   [1, 2, 3, 4, 5]  {'max_depth': 3, 'n_estimators': 100}   \n",
       "\n",
       "    mean_squared_error  max_depth  n_estimators  \n",
       "2           882.090000          5           100  \n",
       "4           882.090000          7           100  \n",
       "3           949.995684          5           500  \n",
       "5           949.995684          7           500  \n",
       "7          1001.849104          3           500  \n",
       "9          1001.849104          5           500  \n",
       "11         1001.849104          7           500  \n",
       "6          1017.610000          3           100  \n",
       "8          1017.610000          5           100  \n",
       "10         1017.610000          7           100  \n",
       "1          1239.683070          3           500  \n",
       "0          1324.377664          3           100  "
      ]
     },
     "execution_count": 18,
     "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 5 lags y una configuración de Random Forest {'max_depth': 5, 'n_estimators': 100}."
   ]
  },
  {
   "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": 19,
   "id": "056fe98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crear y entrenar forecaster con mejores hiperparámetros\n",
    "\n",
    "regressor = RandomForestRegressor(max_depth=5, n_estimators=100, random_state=123)\n",
    "forecaster = ForecasterAutoreg(\n",
    "                regressor = regressor,\n",
    "                lags      = 5\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=datos_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9592c584",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicciones\n",
    "predicciones = forecaster.predict(steps=steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "id": "f186f120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error de test (mse) 33859.10939047616\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
}