prediccion_reserva_tranco_beas[rediam].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicción de la reserva mensual en embalses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introducción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Disponer de una secuencia o serie temporal de datos con una cadencia constante permite la aplicación de diferentes técnicas estadísticas que son capaces de extraer información sobre la estructura interna de dicha serie. Conociendo dicha estructura es posible hacer una estimación de los datos que continuarían la serie. Como ocurre con toda estimación, hay un error asociado y una incertidumbre que aumenta a medida que nos alejeamos del último dato de la serie. Por tanto la cuestión estriba en determinar un modelo estadístico que nos permita hacer predicciones con un grado de error que considerememos aceptable.\n",
    "\n",
    "En la Red de Información Ambiental de Andalucía están disponibles los datos diarios de reserva de los principales embalses en su territorio desde 2009 hasta la actualidad. Estos datos pueden ser descargados en formato geopackage desde [aquí](https://descargasrediam.cica.es/repo/s/RUR?path=%2F04_RECURSOS_NATURALES%2F04_AGUAS%2F01_SUPERFICIALES%2F00_SUPERFICIALES%2FEmbalses_al_dia%2FInfGeografica%2FInfVectorial%2Fgpkg).\n",
    "\n",
    "Como ejemplo de aplicación de estos modelos de predicción se van a emplear los datos disponibles de reserva del embalse de Tranco de Beas en Jaén. Dado la que la evolución de la reserva suele ser muy lenta de un día para otro, tiene más sentido emplear la reserva mensual donde queda más patente su evolución.\n",
    "\n",
    "Los pasos a seguir son los siguientes:\n",
    "\n",
    "1. **Lectura de datos**: Se crea una tabla de datos a partir de los datos recogidos de nuestra fuente.\n",
    "\n",
    "2. **Análisis exploratorio**: Se ve como se distribuyen los datos de la serie, se analiza la estacionariedad de la serie y se calculan sus parámetros estadísticos.\n",
    "\n",
    "3. **Preparación de los datos**.\n",
    "     - Detección y eliminación de datos anómalos. \n",
    "     - Detección e imputación de huecos de datos.\n",
    "     - Transformaciones de datos.\n",
    "     \n",
    "     \n",
    "5. **Cálculo de los modelos de predicción**.\n",
    "\n",
    "6. **Efectividad de los modelos**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Carga de librerías y lectura de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width = 12, repr.plot.high = 6)\n",
    "library(xts)         # Manejo de series de tiempo\n",
    "library(sf)          # Manejo de base de datos geográficas\n",
    "library(forecast)    # Modelos de predicción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los datos necesarios son descargados desde [Descargas Rediam](https://descargasrediam.cica.es/repo/s/RUR) dentro del proyecto **Embalses al día**. Esta base de datos es actualizada de diariamente.\n",
    "\n",
    "En este caso necesitamos descargar una base de datos en formato geopackage. El mecanismo descarga empleado es tal que el archivo descargado está comprimido en formato zip que luego es necesario descomprimir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se monta la ruta de descarga desde la fuente de datos (Descargas Rediam)\n",
    "url_base <- 'https://descargasrediam.cica.es/repo/s/RUR/download?path='\n",
    "project_folder <- '/04_RECURSOS_NATURALES/04_AGUAS/01_SUPERFICIALES/00_SUPERFICIALES/Embalses_al_dia/'\n",
    "file_folder <- 'InfGeografica/InfVectorial/gpkg'\n",
    "url <- paste0(url_base,\n",
    "              gsub(\"/\", \"%2F\", paste0(project_folder, file_folder)),\n",
    "              '&file=embdia.gpkg')\n",
    "\n",
    "# Se ejecuta la descarga\n",
    "download.file(url, 'data/embdia.zip',quiet=TRUE, mode=\"wb\")\n",
    "\n",
    "# Se descomprime el archivo descargado en la carpeta \"data\"\n",
    "unzip('data/embdia.zip',exdir = 'data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez descargada la base de datos, extraemos los datos de la tabla \"datos_reserva\" que contiene el histórico de reservas diarias desde 2009 hasta la fecha actual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading layer `datos_reserva' from data source `H:\\cloud\\OneDrive\\OneDrive - UNED\\ws_datascience\\projects\\forecasting_series_temporales_arima_reserva_embalses\\data\\gpkg\\embdia.gpkg' using driver `GPKG'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"no simple feature geometries present: returning a data.frame or tbl_df\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cod_est</th><th scope=col>fecha</th><th scope=col>reserva</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>E01</td><td>2009-01-02</td><td>166.00</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>E02</td><td>2009-01-02</td><td>  6.39</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>E03</td><td>2009-01-02</td><td> 12.35</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>E04</td><td>2009-01-02</td><td> 24.90</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>E05</td><td>2009-01-02</td><td> 27.47</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>E06</td><td>2009-01-02</td><td>235.84</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & cod\\_est & fecha & reserva\\\\\n",
       "  & <chr> & <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & E01 & 2009-01-02 & 166.00\\\\\n",
       "\t2 & E02 & 2009-01-02 &   6.39\\\\\n",
       "\t3 & E03 & 2009-01-02 &  12.35\\\\\n",
       "\t4 & E04 & 2009-01-02 &  24.90\\\\\n",
       "\t5 & E05 & 2009-01-02 &  27.47\\\\\n",
       "\t6 & E06 & 2009-01-02 & 235.84\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | cod_est &lt;chr&gt; | fecha &lt;date&gt; | reserva &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | E01 | 2009-01-02 | 166.00 |\n",
       "| 2 | E02 | 2009-01-02 |   6.39 |\n",
       "| 3 | E03 | 2009-01-02 |  12.35 |\n",
       "| 4 | E04 | 2009-01-02 |  24.90 |\n",
       "| 5 | E05 | 2009-01-02 |  27.47 |\n",
       "| 6 | E06 | 2009-01-02 | 235.84 |\n",
       "\n"
      ],
      "text/plain": [
       "  cod_est fecha      reserva\n",
       "1 E01     2009-01-02 166.00 \n",
       "2 E02     2009-01-02   6.39 \n",
       "3 E03     2009-01-02  12.35 \n",
       "4 E04     2009-01-02  24.90 \n",
       "5 E05     2009-01-02  27.47 \n",
       "6 E06     2009-01-02 235.84 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lectura de la tabla de datos diarios de reserva\n",
    "data <- sf::st_read('data/gpkg/embdia.gpkg', layer ='datos_reserva')\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se seleccionan los datos del embalse Tranco de Beas, correspondiente al codigo de estación 'E01'\n",
    "datemb <- subset(data, cod_est == 'E01')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos los primeros de la tabla:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cod_est</th><th scope=col>fecha</th><th scope=col>reserva</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>E01</td><td>2009-01-02</td><td>166.00</td></tr>\n",
       "\t<tr><th scope=row>71</th><td>E01</td><td>2009-01-09</td><td>174.31</td></tr>\n",
       "\t<tr><th scope=row>139</th><td>E01</td><td>2009-01-19</td><td>180.28</td></tr>\n",
       "\t<tr><th scope=row>203</th><td>E01</td><td>2009-01-20</td><td>181.67</td></tr>\n",
       "\t<tr><th scope=row>332</th><td>E01</td><td>2009-01-23</td><td>190.05</td></tr>\n",
       "\t<tr><th scope=row>397</th><td>E01</td><td>2009-01-24</td><td>202.14</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & cod\\_est & fecha & reserva\\\\\n",
       "  & <chr> & <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & E01 & 2009-01-02 & 166.00\\\\\n",
       "\t71 & E01 & 2009-01-09 & 174.31\\\\\n",
       "\t139 & E01 & 2009-01-19 & 180.28\\\\\n",
       "\t203 & E01 & 2009-01-20 & 181.67\\\\\n",
       "\t332 & E01 & 2009-01-23 & 190.05\\\\\n",
       "\t397 & E01 & 2009-01-24 & 202.14\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | cod_est &lt;chr&gt; | fecha &lt;date&gt; | reserva &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | E01 | 2009-01-02 | 166.00 |\n",
       "| 71 | E01 | 2009-01-09 | 174.31 |\n",
       "| 139 | E01 | 2009-01-19 | 180.28 |\n",
       "| 203 | E01 | 2009-01-20 | 181.67 |\n",
       "| 332 | E01 | 2009-01-23 | 190.05 |\n",
       "| 397 | E01 | 2009-01-24 | 202.14 |\n",
       "\n"
      ],
      "text/plain": [
       "    cod_est fecha      reserva\n",
       "1   E01     2009-01-02 166.00 \n",
       "71  E01     2009-01-09 174.31 \n",
       "139 E01     2009-01-19 180.28 \n",
       "203 E01     2009-01-20 181.67 \n",
       "332 E01     2009-01-23 190.05 \n",
       "397 E01     2009-01-24 202.14 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(datemb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y también vemos los últimos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>cod_est</th><th scope=col>fecha</th><th scope=col>reserva</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;date&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>306748</th><td>E01</td><td>2021-04-04</td><td>306.03</td></tr>\n",
       "\t<tr><th scope=row>306821</th><td>E01</td><td>2021-04-05</td><td>306.24</td></tr>\n",
       "\t<tr><th scope=row>306894</th><td>E01</td><td>2021-04-06</td><td>306.41</td></tr>\n",
       "\t<tr><th scope=row>306967</th><td>E01</td><td>2021-04-07</td><td>306.67</td></tr>\n",
       "\t<tr><th scope=row>307040</th><td>E01</td><td>2021-04-08</td><td>306.88</td></tr>\n",
       "\t<tr><th scope=row>307113</th><td>E01</td><td>2021-04-09</td><td>306.67</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & cod\\_est & fecha & reserva\\\\\n",
       "  & <chr> & <date> & <dbl>\\\\\n",
       "\\hline\n",
       "\t306748 & E01 & 2021-04-04 & 306.03\\\\\n",
       "\t306821 & E01 & 2021-04-05 & 306.24\\\\\n",
       "\t306894 & E01 & 2021-04-06 & 306.41\\\\\n",
       "\t306967 & E01 & 2021-04-07 & 306.67\\\\\n",
       "\t307040 & E01 & 2021-04-08 & 306.88\\\\\n",
       "\t307113 & E01 & 2021-04-09 & 306.67\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 3\n",
       "\n",
       "| <!--/--> | cod_est &lt;chr&gt; | fecha &lt;date&gt; | reserva &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 306748 | E01 | 2021-04-04 | 306.03 |\n",
       "| 306821 | E01 | 2021-04-05 | 306.24 |\n",
       "| 306894 | E01 | 2021-04-06 | 306.41 |\n",
       "| 306967 | E01 | 2021-04-07 | 306.67 |\n",
       "| 307040 | E01 | 2021-04-08 | 306.88 |\n",
       "| 307113 | E01 | 2021-04-09 | 306.67 |\n",
       "\n"
      ],
      "text/plain": [
       "       cod_est fecha      reserva\n",
       "306748 E01     2021-04-04 306.03 \n",
       "306821 E01     2021-04-05 306.24 \n",
       "306894 E01     2021-04-06 306.41 \n",
       "306967 E01     2021-04-07 306.67 \n",
       "307040 E01     2021-04-08 306.88 \n",
       "307113 E01     2021-04-09 306.67 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(datemb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preparación de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este apartado se van analizar los datos diarios descargados en busca de datos anómalos y de datos faltantes. Una vez se hayan corregido estos aspecto, se creará la serie de datos mensuales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Eliminación de datos anómalos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En primer lugar examinamos la gráfica de los datos de reservas diarias descargadso del embalse Tranco de Beas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\n",
      "text/plain": [
       "Plot with title \"Embalses Tranco de Beas. Reserva diaria (hm3)\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(datemb[,2:3],type = 'l',col='blue',main ='Embalses Tranco de Beas. Reserva diaria (hm3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que hay días en donde el datos de reserva no se ajusta a lo esperado. Estos datos son detectados visualmente y eliminados.\n",
    "\n",
    "Para facilitar este proceso la serie de datos se pasa a una estructura de datos xts, que facilita la gestión y consulta de series temporales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           reserva\n",
       "2009-01-02  166.00\n",
       "2009-01-09  174.31\n",
       "2009-01-19  180.28\n",
       "2009-01-20  181.67\n",
       "2009-01-23  190.05\n",
       "2009-01-24  202.14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAANlBMVEUAAP8zMzNxcXGGhoaW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\nlV7VszveAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transformación a xts\n",
    "resdiaria_xts <- xts(datemb$reserva, order.by = datemb$fecha)\n",
    "colnames(resdiaria_xts) <- 'reserva'\n",
    "head(resdiaria_xts)\n",
    "\n",
    "# Eliminación de anomalías\n",
    "resdiaria_xts['2009-05-30'] <-NA\n",
    "resdiaria_xts['2010-09-03'] <-NA\n",
    "resdiaria_xts['2011-03-26'] <-NA\n",
    "resdiaria_xts['2011-12-06'] <-NA\n",
    "resdiaria_xts['2011-12-07'] <-NA\n",
    "resdiaria_xts['2012-01-06'] <-NA\n",
    "resdiaria_xts['2015-04-10'] <-NA\n",
    "resdiaria_xts['2018-04-16'] <-NA\n",
    "\n",
    "plot(resdiaria_xts, col='blue', main = 'Embalses Tranco de Beas. Reserva diaria (hm3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Imputación de datos faltantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación se van a detectar las fechas en donde no existe ningún dato imputado. Una vez localizados se genera una serie temporal con los huecos correspondientes, que podrán ser visualizados posteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAANlBMVEUAAP8zMzNxcXGGhoaW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\nG9/JAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Se crea una serie de datos de soporte que contenga todos los dias entre el primer y el último deatos de la serie.\n",
    "seq_daily <- seq(from=first(index(resdiaria_xts)),\n",
    "                 to = last(index(resdiaria_xts)),\n",
    "                 by='days')\n",
    "dummy_xts <- xts(rep(0,length(seq_daily)), order.by = seq_daily)\n",
    "\n",
    "# Se combinan como columnas la serie de soporte y la original para general los huecos.\n",
    "resdiaria01_xts <- cbind(dummy_xts, resdiaria_xts)[,2]\n",
    "plot(resdiaria01_xts,\n",
    "     col='blue',\n",
    "     main = ' Embalses Tranco de Beas. Reserva diaria (hm3).\\n Localización de huecos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se observa la falta de datos se localiza, salvo algún valor esporádico, en los años 2009 y 2010. No obstante es posible interpolar los datos en estos huecos. Hay una amplia variedad de métodos para hacer esta interpolación. En este caso se va a usar la [interpolación por splines](https://es.wikipedia.org/wiki/Spline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           reserva   resint\n",
       "2009-01-02     166 166.0000\n",
       "2009-01-03      NA 168.0734\n",
       "2009-01-04      NA 169.7921\n",
       "2009-01-05      NA 171.1916\n",
       "2009-01-06      NA 172.3076\n",
       "2009-01-07      NA 173.1755"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resdiaria_int <- na.spline(resdiaria01_xts)\n",
    "colnames(resdiaria_int) <- 'resint'\n",
    "resdiaria02_xts <- cbind(resdiaria01_xts,resdiaria_int)\n",
    "head(resdiaria02_xts)\n",
    "resdiaria03_xts <- resdiaria02_xts$resint\n",
    "colnames(resdiaria03_xts) <- 'reserva'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La gráfica de la serie temporal con los datos interpolados es la siguiente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAANlBMVEUzMzNxcXGGhoaWlpaj\no6Opqamurq64uLjCwsLKysrS0tLZ2dng4ODn5+ft7e3z8/P/AAD////RB4hrAAAACXBIWXMA\nABJ0AAASdAHeZh94AAAgAElEQVR4nO2djXabOhAGnaa0TW/b2O//sjc2/yBAK1ZiJc2c08YG\nPq1A9oRgwLcHAACY5HZ1BwAAwA2CBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAw\nCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYA\nMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIG\nADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqC\nBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAK\nggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADBKEkHfRtxzN0JKBeRsdvTJ249/IQ2GRo9b\n3t5WW9P/3N6PNvDnz7fb28/Pr0fvtz/nuwkAASBo/9pDjTexZk9Ej1uWC/rb7ffBBv58G7r7\n+/ZNo6MAIAZB+9cei/wIaDA0etyyOPPf7fZ5kPxxu318qfnZ3c/b7b8THQSAYFIJWj5Xah4t\nOW+31U39HVDpRNSvZRHfnkc49pM/v78Ni7yzCw1wDekF/fXk88ft7ePx9/329t844edr7t+v\nXbfb+58h9Of7c6/z72vecFR0NnVV5OvBn5eC5m19pb99vBb47/sQ/vPeLTHwtdiPz66tseCs\nQv9znD1WcnZtL+pcwW4V/txuz63y8/Y8Cjypsepq2+xsfV+bYDm95d/t9qvry6+vjTIZg3FQ\n+m4/Nf3rdlM/dA4AHlwi6Nfxze+vv/j/Gyc89+r+dAcC/nSh3+PzR3dU9HM+dV2kbe/7oq33\n1+OnoX+2k/+OD3+NrbwWe2vbGgvOKnx0xynG2ZNKzq5N96C/zaPuFexX4e0lyNf/f1YNT7r6\n+m++vq/8cnrLR3vMYtgovx2D8uTze7vB/mt/AEBqEh+Dbp/8eL7nv/5vfdW6+f1lgdenV79v\nw0kGb8+dtz+vxX49FfPxsulk6qzI8OAr/rlo6/3zqeNvL928Hj93sZ8PP99bWb/4eM18f7U1\nKbhYjdcnfZPZk0rOrk23wMdja1UmU/tV+Pmc+++1Hz2pse7q67/F+j7zy+kt39sVbjfKRx+Y\nD0q7WCvmv0/XA0B6LhH0v8n/7d/jr/2898ny43/9ztt7l36fTZ0VGR78WUy+DUrq9fT5/e/r\nk7C28vDJ3Xs/5TYruFiN758P9+yNrk2i79urMp/aHi957t7+nqzPbfxbZNrVyeTJJl1Pb/nW\nPpxulNWgtIt1G2b1+wYAknCJoOf/3wZfPH98fvyY7BW+/uR+/2/WyGzqrMjiwaKt9Slpb/3k\nt0Uj/YJDpxer8fZvMXuo5OzasPDbn3lDWys4FP32te/6veveUMPZVdf6rqavk7MGpv8/+dn9\n+TDJAkBCLjkGPf9/5ovvC0l9fOv3O6dCHKc6igwPlm2tBD031XKKS9Cvn79ef/JPZ4+VnF3r\noj/76e5VcQn6q9Sj/aRwWsPVVdf6rqa7VntH0J/dry4EDXANxgT9dNjvz5lk/v1664U4abKf\n6ijSP1i3tRT04R70xmosZ08qObvWL/yjO57hXhXHr4Ln8eeP9hyKeQ1XVx3bbjl9ndwVtOM3\nGAAkxIag24Op35eTu8S/18P3xcle/xbaWAl643fA91e5jWPQ3+fHoOdnl/UNf7yMPpm9FNuy\na8O8t/acEPeqrBp88ty//uaqMevqUrKzTbBKfptsjS1Bf39vH3a1l38RAEAKUgl6+ef73Cbt\n6Qi/X/u0f14nv3Xzvj2f/+1PcvjRaXwydVZk/mDZVvejP4vjW/vB5Pwsjt/9lNus4Go1fs5m\nTypNuja1dPf4oz1c4V6VydQx+pVojwNPaqy7+vpvvb6r6S0/Jp8Obgn6R/fx5HOP/+/rNxi7\n0QDJsSHo4TzoX6+FXueeveb97WLPfd5v7bx/s6mzIvMHy7b6OT/H8M9Btz2Tk4snBZer8dwP\nnsyeVJp0zSHo9qy6jVWZTB2jz0MT/xZrs+5q/wtlsb6r6S2/h/Ogh2VWg9Ldi+O15/zf7fDW\nHQAQAxuC/vejt+TH83q/z+cuW7vEvx9vw4V2v74M1t4Objp1UmTxYNlW/+O/9yH8fDg/5+Ln\ns0a34FhwthrfuqsLx9ljpUnXXIL+6HbI3asyTJ1Eh+usJzVWXW2XX63vanrL53gl4WNL0K+7\n2XVXXv46vnUHAMSAN12FvMuOKQsXB9jjuTfW3Rbha/fo++LR814Dk9srTOY8nrskD9ecscWW\nz36nxlVqtvBGg4/ni945Z9m96CDoCmnvZucNd7MDPf6Nx/TaR3+nj/4uDiuOcx6vPxnHdsY5\nY4sdv7urf12lZgtvNPh4/anrmrPsXnwQdI28rv72hftBgyI/ngczX5/Rfzwf/XjKdHz04/mx\n9Mf4qdA45/XB9URX00zfYsd7Z1lXqenCWw22InaXmncvPgi6Rv5IDlrwjSqgyFv3ycfzRNHX\nPun36aPuc5HJfWP6OY+3tz9Ta45zxhY7+usaXKUmC282+LUH8+aes+xefBA0AKTmuQs7unJ8\ndFvIduLTnzMJr7Q87kH/N9/DnZeaLLzd4K/bb/ecZffig6ABIDH/PQ8V3Bzee3/OmBwanhtx\nasaFK/8bb1L2Y/Yn36LUfGF3g39fd+p1zVl2Lz4IGgDS8u+tvfnM47EU9Ovm5e9yQbctdnPe\nxqVWpeYLuxt8e/vcmLPsXnwQNAAkpROkS9CP/77dvn/ebo/hqolxye6na87Uz3+m3/u5KjVf\n2Nngj/ZrRJyl+u4lI0mp7gKPd8edko+/Q9VxgYT0i1c3rrHg0guA9Px962/q+Pr/bfroxb/2\n29ombuzmOKz5Nm3xxc/JWaHrUvOFnQ2OV9U5O/HqXjJSCvp2+7Y4/fbP23H9lUZ9QgdN7E4G\ngHj87u9u8204OeLb5ASKx3MfePiUbz5n+oadzPk9u3nkRA+OUvOFnQ2OgnZ2Ytq9+KQVtPsu\nnPLWdBIIGiA14ymeP5/HIn60H7v1j7597f5+fh/vXjbOeTJ9w45z5ieN/huPcLhKzRd2N7g9\nZ9m9+KQS9OP5vR63xf2NEDRAZbwPu6fdjcI+p48+Xg/GPdRxzpPpG3acM7b45Nd4hMNVar6w\nu8HtOcvuxSehoIe7bf59XsHz/qffs37OeV7s3l0g/+f5BSCTGyF1R4Fuj+7uPWPoa0J7XfxT\n/N/a41bjbZcmbbbLD2Xb6I/P49oAoMvk+MH4zhsffXzr79HVMnl3Lk5AHubMj0h8G5dxlZov\n7G5wZ86ye9FJKuh/r/tX/uk20Z/Rtd1tP59XWf4e5k7Dt9fJLbfn/YmHUHtDzLf2RmuvLyW5\nTW5cOm3zNpwh0zU8uVPnbm0AgAtJKuj2wetGEL/7b69+Tv47uXH+677FfyY34x8E/bzL/uRG\n+L+eIv14mbV18me3VHvr/2mby7If/XK3g9oAABdygaAnD7vn06+eut1uH+vwbfIlIF3ovTtF\npvV8fyhj+PKsRZuz1t4n3xa1WxsA4EIuEfTnx4/3qWunX976+grq6T30pwsuHvdHO/r2p0tN\n29wou1puVRsA4EJSH4N+Hj34vjTr7MfHt+Ew8hiWC3r92F12tzYAwIWkPovjx+tTufffn1t7\n0F/8+/U2/6rWHaXO2t/dg16XXS23qg0AcCEpBf3f2+TLVLeOQb/4t7SvQ9Dv47egTgS9fQx6\nUvb7xjHoVW0AgAtJJeiO59GDl6Z/Tl37Z3Imxbfn3L+OsziWj389ndrKeCLo4SyOP8uzOCZl\nf/fzbge1AQAuJK2gX/fi+PV6+Dql7XnpyHPPtTsX+XUVS7fo6jzo4XEX+vzWtvNvJujxIPKk\nzdf8SVnXedDO2gAAF5JS0P3d7D6elwR+Pi3773t3wPe/9+H0iX8/3txXEvaPh9CvLxv/+PeY\nCfpL32/dlYRjm90ngEPZl5V/9McydmoDAFxIUYdbOXoMACVRlNEQNACURFFGQ9AAUBJFGQ1B\nA0BJYDQAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKMg\naAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACj\nIGgAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKMgaAAAoyBoAACjIGgAAKPEFXST\nIFF7FdVIsz1HsYpqpI6BIZJbRAkEnXsVtchTzghaMUOk5ogSCDr3KoqCbhC0ZoZIzRElEHTu\nVbQiDYJWzhCpOaIEgs69ilKk6QS91ZrdN0LhA0Mkz4gSCDr3KgjaaBUiVUeUQNC5V0HQRqsQ\nqTqiBILOvQqCNlqFSNURJRB07lUQtNEqRKqOKIGgc6+CoI1WIVJ1RAkEnXsVBG20CpGqI0og\n6NyrqAn6gaB1M0RqjiiBoHOvgqCNViFSdUQJBJ17FQRttAqRqiNKIOjcqyBoo1WIVB1RAkHn\nXgVBG61CpOqIEgg69yp6gu7O5IhZJUKk9IEhkmdECQSdexVtQW80Z/eNUPjAEMkzogSCzr0K\ngjZahUjVESUQdO5VELTRKkSqjiiBoHOvoivozVv2230jFD4wRPKMKIGgc6+iKugHgtbLEKk5\nogSCzr0KgjZahUjVESUQdO5VFAX9QNCaGSI1R5RA0LlX0RT0A0ErZojUHFECQedeBUEbrUKk\n6ogSCDr3KuqCdrdn941Q+MAQyTOiBILOvYqyoLd2oe2+EQofGCJ5RpRA0LlXQdBGqxCpOqIE\ngs69CoI2WoVI1RElEHTuVRC00SpEqo4ogaBzr4KgjVYhUnVECQSdexUEbbQKkaojSiDo3Ksg\naKNViFQdUQJB514FQRutQqTqiBIIOvcqCNpoFSJVR5RA0LlXQdBGqxCpOqIEgs69iragZ4Ye\nL/y2+0YofGCI5BlRAkHnXiWmoCdfgWX3jVD4wBDJM6IEgs69SkRBN5M7+Nt9IxQ+METyjCiB\noHOvElXQk/+v6liETFYDQyTPiBIIOvcqCNpoFSJVR5RA0LlXiSfo/vhz/21Y13QsQiargSGS\nZ0QJBJ17lZiCnjyw+0YofGCI5BlRAkHnXgVBG61CpOqIEgg69yoJBP18ZPeNUPjAEMkzogSC\nzr1KBEE360mG3wiFDwyRPCNKIOjcq6gL2vUF31tfJSutEiFS+sAQyTOiBILOvQqCNlqFSNUR\nJRB07lUQtNEqRKqOKIGgc68STdAzKSNoIkQuAEHnXiWWoOc7zSqCPm6EgSFSSEQJBJ17lUiC\nXhzUQNBEiFwAgs69ShxBLw86I2giRC4AQedeJYqg1x8KBhgaQROpN6IEgs69ir6gH43jpA0E\nTYRIehB07lUQdIKEYREQsRlRAkHnXiWOoB3zxXUQNJF6I0og6NyrRBC0y6QImgiR9CDo3KvE\nELQrgqCJEEkOgs69CoJOkDAsAiI2I0og6Nyr5CNo12ePB5GQMjEShkVAxGZECQSdexUEnSBh\nWAREbEaUQNC5V9GJeKhTbGgETaTeiBIIOvcqCDpBwrAIiNiMKIGgc6+CoBMkDIuAiM2IEgg6\n9yoIOkHCsAiI2IwogaD1M72HcvKAjzqlhkbQROqNKIGg9TMI2l0FQROpJ6IEglbPNL2IcvIA\ngtbPEKk5okRlgk6xb9v0h2tz8gCC1s8QqTmiRF2Cvt+be+wqDYLeqIKgidQTUaIuQX/puf3C\nvYhVmkFlOXnAS51CQyNoIvVGlKhK0E0vz4hVmmZQWU4eQND6GSI1R5SoStCP5/GN50GOqIIe\n/s/JAwhaP0Ok5ogSVQn63h6ADrjvj3eVpmRBCw2NoInUG1GiJkG3fn7cm3tMQY8/cvIAgtbP\nEKk5okRNgu71cA/ahfaKNAh6uwqCJlJPRImKBD3qIaagJw9y8gCC1s8QqTmiREWCvneHOCIK\nukHQO1UQNJF6IkrUI+jRz1LR+FcZ2y1T0LINh6CJ1BtRoh5BT+UQciKHR2Da6vN6QmkJvypR\nIghaP0Ok5ogSdQo65GNCL0HPHufkgasEvZiEoIkUElGiGkE3SfagZ49z8sBFgm4W0xA0kUIi\nSlQk6FkihqAbBL1bZS3o7tr7ZjMSUiZGwrAIiNiMKFGNoCcfEb4Efd9cMrjK4q/1iNcr6kcu\nEfRw2WWzFQkpEyNhWAREbEaUqEXQ94Wg7wh6PuUCQc9PStzomLxMjIRhERCxGVGiHkHPE3EO\ncew91aoSJ+J7foVkpY4FvXqEoIkUElGiVkHL9Xm4/PoPeGEFryqRIhcIukHQRMqNKFGLoFdn\nc8UQ9P5znSqRIrEF3X0a2EynNPPZ7o7JysRKGBYBEZsRJRC0WhUEvVOl/SIwl5NnTxA0kUIi\nSlQi6PX1EAh6PsVT0JK1cgm6mU5Y10fQRAqJKFGNoJcJBD2fkkTQ07v9LUYEQRMpKqJEJYJe\nnFWHoJcTjtfOe0lnlbWgnR1A0EQKiShRh6DvCQTtuExOWMGnSqRIakFvbSwETaSQiBK1CHqV\niCDo4ynnq0SKpBe0uwcImkghESXqEPTywu5Ugk6y/mkFLdhwkwWb1SEOdw8QNJFCIkpUIej1\nH9T6gj6+laZGlViR1IJmD5pI4RElKhH0OqEvaMekXATt0dPzgm5GQTs3lqtjwjLREoZFQMRm\nRIkqBL26M1Ljmniqiktx2Qjap6NnBf04EHQ7DUETKSSiRA2CXp7DoSLoleMciXwELYl4rxWC\nJlJvRIk6BO1KnBO0z52REPQ0c5mgHd/jEqEKESIxqFfQ0qPQCFqw9LLKcCeO7fOdYwl6+a1a\ncaoQIRKFCgTdrL89RV3QThcj6GlmKuiUe9DN8i5NCJpIgogSVQjandDdg3YWRtBjZiJoZwuR\nBN10FadLIWgi0SNKVCBox8HmZmuGd5XNe88fTpVUSRRJKOjH5DjHcvEmwvD3Y4CgiaSNKIGg\ng6osDmxuGScPQXv183BtjyLNuC+bTtCOrz08SIRUIUIkFuULen2S3ZAQCXQh6Gl4w3BxvvlQ\nP+LVy9OC7reS40O7yTJ+LW+VcbbpeKhchQiRWNQg6M2EnqA3Egh6EhmPNmxtr0Z7+BsETeSi\niBLlC9p5xkA/S3CUY1vQm8JB0NPI4ZEUdUFvHYZC0ESiR5RA0EFVFoLeSpQpaN/VulrQ8/Mr\nETSRlBElShX0/u4tgp49K1bQG08RNJHoESUKFfTd+en9KnEXGHRT0JviafyPBrirJIoUKujt\niz0RNJHoESWKF7RzH/lw5/eoylzQ2wkEPSYSC3rnK8gQNJHoESXKFPTz2EUnZh1BL92BoGWR\nSwS9OQVBE4keUaJUQQ/vRi1Bz5dE0LIIgiZSWUSJYgXdvR1dl6noCLrPbnunERRY9SxhRCxo\nz7VaCPowpSpo9zco7CVCqhAhEpeiBX3fkoKuoPf6haDHwGEq5Nr4bUFvT0PQRKJHlChZ0K+T\n6PYF7Xua3ULQM98g6ONIekG7JiNoIskiSpQr6Pt95748wyNPQ+8Imj1ojwh70EQqiyhRrqCf\n7t2+CLvnpKA37ysxVBE6p2RBH/ZUUdDu098RNBEEPeFSQT+2L0QJErRjh7DZvRaxXEFLI4Kz\nGeUgaCIGI0qULegt/04E7bkz6BT0wR4fghYtLlhuo8y0IQRN5NKIErUL2vev9fnR7O4v9oOs\n10LbPUsWKVLQe+0jaCLRI0oULujjBIIOEbRX5jpB79xxeiMRUoUIkegUKejj0wUEy/YLrQV9\nlPVaaLtnySLFCXr79lUImgiCHilc0F5VEPQFgt5vH0ETiR5RAkEj6NIEvXNUye90P68qRIgk\nAEEfVry3Z2sgaGnmOkEfFEDQRKJHlKhe0MdnQhcvaK8Wlst4rJZc0EGGRtBEDEaUQND+gh6b\nLUvQIXvQhgW9e3EngiaCoAcQtPfCjp4liohWRhK7TNBH7SNoItEjSiDow6URtLMKgiZCJDoI\nGkFHF7T/xTqxBd3ORNBEokeUQNDHgr7fTwlaZJ1CBe1TQbako0zfBIImcn1ECQR9eBS6+wLa\nUgXtuXubkaAPCyBoItEjSiDoY0E3hQs6sIr/VpYI+vRLBkETsRBRAkEfLt68vtwQQa+mmBT0\n4R2sGgRNJEFECQTtI+j2B4KeTzEq6KMKCJpIgogSRQr68NRmyV/F98IF7Zl3LHXoQt8Fp00i\naCJFRJRA0I8jQd/7TL+Yr0UCBJXTHrRNQR9tDgRNJElECQR9FFgJ+uibrtZVLAvat2+JBB1w\nHgd70EQMRpRA0EcBh6Cl/ULQfjUUBO1x8l+DoInEjyiBoI8CS0ELLosbHiBoTxA0kUIiSiDo\n1/OdsmtBi/tlWdC+cddiR6e/eC43iyBoIkVElEDQ7YQ9Q/eZ9iKHwgR9Yg/aoqCPtwaCJpIi\nogSCbidsJ+5LQQf0K0Dq/pyKyI/XTKf5CVr0O409aCJFRJRA0AeJYQ6CXk/zFbRnjfOC9kgj\naCIpIkog6IPIQtCSE3URtLDGKyI2tFDQQedaGxYBEZsRJRD0QWQl6JB+FSvo3TSCJlJvRAkE\n3U3ZKjwR9OsSFQQ9nYigiRCJCYLuJ218TrgUdFi/znkwYgRBB1QhQiQVCLqf5CfowH6ZFbR3\nuhxBB3wO+TAsAiI2I0og6H6Sh6CFb+0cBG1vD1rsTwRNxGBECQR9EJoJWkYGgj55cBxBEyES\nFQR9EELQe1V28wiaSL0RJRD0buh+1xH0qSuqI0YQdEgVIkRSgaB3Q/fx0DSCdk31ELT02p7o\ngg4yNBEiV1CioD3ux+Ca5rQ2gt6t4ido3xoImkgxESUCBN1GbrcuOjxwgKD9+xRaBUH79ke4\n1HYVIkT8ELhytwVR4tbn5g9cZC/oyaUpCNo5eaeBQEFL/TlZ2jOJoIlEjzyRuHKrCXHNSZnb\n5IETBC3oVGAVBO3Xm2ki5lZuHI/0qxCxHnkIXbnZhnh5BO0JghbWSCfoeC/MSdtm3UEkQUTq\nyu1GIhZF0IJOBVaxLmj5/aVyFnRz6hVDpJzINYK+PRC0NzUIeq+F5miBjUjGgn62jKCJPBG6\ncqcV4dISQQMAFI2OK/eUK1u4sj3ovYykiucZYOIqZyLCS0ic09mDnrU86YXZnTsi8SNiV+62\n47twRzWCnk5D0O7p+oKOftPAaIJuEDSRFrErt9oRV65pDxpBH1fZbsOuoANOtPMU9LQXVt1B\nJEXkRfqzOPpIHReqIOjjKgh6Vb//Ia1BpKDIiwsuVKnqUm8EfVwFQa+WQtBEWq641FtC5oK+\nawraTwr5CXq7kfoEzR40EWUQ9E4KQftU0Rd0UEaQa6S/Ax5+W7mZd9+sO4gkiCiBoHdSCNqn\nCoJelkfQRLRA0D2OG/YjaK8qW6007cz6BN2fzSEsQaSkiBIIemBl6PlVQmYEvVRe+OtNIs9w\nQXuXeDwQNJFSIkog6IGloO/KgvaSQmJBiyPuDu1EELR2FSJZRJRA0Jux+x1B+1XZaMeyoMWG\nRtBErgBBD9wXu9BfIrwbFfRsKQR91BFXAkETiRpRAkEP3O8L8Z3dUUXQwhpjlbCe+R5TR9BE\nokeUKFDQjtMxPKsg6MPIVpe2IsL7Ep0VtH8iqqDbR2bdQSRBRAkEvRlF0N5V9gTtX2JSJahn\n3r8MEDSR6BElEPRmVF3QPlLwEvS8IQQtDSFoItEjSiDo2fRm+timoJ+NIGhHRibo0J7tLYKg\niWiDoLeiyzewEUE3VQg66BrH6wU9f2jWHUQSRJRA0PMZjevhfsa/io6gly0h6PUjj4S6oBsE\nTUQdBL0VNinoxqag3S016+NE3lWy24NefH3o6+8cUQGvKkSyiSiBoBdzNt/uGusiO0N7vfRg\ngXwE7V9hViVA0P6RCIJenVqDoCuPKIGgF3PsCNrxne7DBGuCdjZVl6DXz826g0iCiBIIejlr\n6/2eVtDN6+jAUtHPSauWDAta/PXZCQUtNDSCJnIFCHo5q7l3rSyaUVmXQykM6hz3lScqdu7f\n2xW02M/ZCnq9puPvUr0qRDKKKIGgF/RiXr/jhL06IejZwY0NKxsTtKstuZ+zFfR6NoKuPKIE\ngl7Sxh27RMJeuRNHVmia9aFn11GX0gUt1u3Ve9COKWbdQSRBRAkEveT+2oe+XyXo+/rAgPOw\n+GS/WtyxVIKWdy1TQTt/O9l1B5EEESUQ9JJO0KtGkgi6aZY3PZ2GchO07T1omaERNJErQNBL\nXoZsYgl61wrPnWdn710n1xkTtPuvfCkImkghESUQ9Hrmy5OyjH+VHSs0CDpPQTv/VAj4+8Gw\nbohcBIJez/x6Zzma0FqXzfdte+Kzs/NtZnVOdGjHQg7CHi/rOtNMSq6Cdk406w4iCSJKIGjH\nXOdubGxBNzuCbkNb53aIO5ZG0CGGcv6J4JmJqfTd9XevKIKuO6IEgnbMdVpSbV3cb9ymc/BG\n519XpmUl6LOCyknQ7qlm3UEkQUQJBO2Ye4Ggh5sg7YSMC3rVv7SCFtRLIuiQIzx2dUPkIhC0\nY256QfcldwTtuHNSaMeiCbrZfCavIhe0uAqCJhItogSCds127uP6d+gg4Trfof+dsFklA0HP\nnYygzbqDSIKIEgjaNTvqHrTrdIdhyq4HrAt61kUEbdYdRBJElEDQgVXOJHY+TturshEzJOhp\nm9UIemtFEXTVESUQdGCVU4ntg7UiZxoU9OSzy7OC8s2HCzoos56zMQtBVx1RAkEHVjmVWBwK\nKEnQ4449gk7y8idiM6IEgg6sci6xeSQgf0EfnjHoWyV3QYfsQpvVDZGLQNCBVc4lBnutPvmT\ntG9T0MM1Nwjau/XjKkQyiyiBoAOrnEy4vv1VWsWqoNuVQ9AIuuaIEgg6sMrZxMZxgCIE/Wpa\nQdB+Dbnds74AACAASURBVCBoIgYjSiDowCpnE72gT1QxLejm/P2Q4wtaEELQRK4AQQdWOZ1w\nHwYQVekkL+vVGIko6AeCRtB1R5RA0IFVTiccd9eQVjEsaI1IHoLePhKDoGuOKFGeoH3eFhYE\n7X5rI+jJk0wELZ8jr0Ikt4gSCDqsikbC1VPpoV5xZFIFQccWtHgLmNUNkYtA0GFV4iQQ9PyZ\nVwtnBC2+44djBoImEg8EHVYlTgJBz58haCK5RpRA0GFV4iQQ9PwZgiaSa0QJBB1WJU4CQS+e\n+jSBoIkYjCiBoMOqxEkkFbRMHwh6a8b2HPl5HGZ1Q+QiEHRYlTgJceZ1tUtgFQQtSSFoIleA\noMOqxEkg6OVz/8EMvK+G8JZMjhkImkg8EHRYlTgJBL187j2YkgO+CJpI9IgSCDqsSpwEgl4+\n9x1M0QdyCJpI9IgSCDqsSpyEPHPinkQ5CNqnk81D6GdVQe9URtA1R5RA0GFV4iQQ9GqCj6Bd\nd53yriK7oHw1eSeOoGuOKIGgw6rESSDo1QQPQUv9rCvo3YjU0GZ1Q+QiEHRYlTgJBL2acNiI\nWM+agt6tjaBrjiiBoMOqxEkg6PWUg1ZCvllLU9D7EQRdbUQJBB1WJU4CQa+n7LcSsP+se4hj\nP4Kgq40ogaDDqsRJIGjHpF0HBq2//AXDHjSRa0DQYVXiJAIy4V+eXYCgm7DzwNUEvR9F0DVH\nlEDQYVXiJBC0Y9J2M03gzaLSCVpqaLO6IXIRCDqsSpwEgnZM2hN0WBktQR9sewRdc0QJBB1W\nJU4CQbumbbXTXC/owwiCrjWiBIIOqxIngaBd0zba6df8MkEfbXoEXXNECQQdViVOAkG7prnb\nGVb89MB4bQjZwZchgqBrjSiBoMOqxEkEVSld0O6Ojr+YrhL04a9GBF1zRAkEHVYlTgJBOyfu\n71dfJ2iPCIKuNaIEgg6rEieBoJ0T94//mhZ0PluZiG5ECQQdViVOAkG7py4nzy7wRtBEDEaU\nQNBhVeIkELR76sqo0ynnByboJXP86WxmW5mIakQJBB1WJU4CQW9M3hviywTtFcloKxPRjCiB\noMOqxEkg6I3JcyPnJWhJ/8zqhshFIOiwKnESCHprerP1xLqgZZvZrG6IXASCDqsSJ4GgN2dM\nT9tA0ETMR5RA0GFV4iQQ9PacUdEmBO1x6AJB1xxRAkGHVYmTQNB78zpFL9WoMDAemyJA6gi6\n5ogSCDqsSpwEgj6Y/dXn1a4rgiZiMKIEgg6rEieBoI/mO76D8BJB+5ycgaBrjiiBoMOqxEkg\n6OMlrAjatwyCrjOiBIIOqxInEfbiCexZnoLWyCQVtGg729vKwd+KUHtECQQdViVOAkHLIyoD\n43tKhiCAoOuOKIGgw6rESSBoeQRBx4w0CDowogSCDqsSJ4Gg5ZFrBO1TtQxBt/031zHzESUQ\ndFiVOAkELY/oDIzfrekEyz+KEHSDoEMjSiDosCpxEghaHrlE0JJbSOct6AeCDosogaDDqsRJ\nIGh5RGlgjrZGlYJuEHRwRAkEHVYlTgJByyMIOl6kGV4oxjqWQUQJBB1WJU4CQcsjVwja7ybP\n/TKCDW1rKzcIOjyiBIIOqxInEShocQhBr6fJBC0pk7Gghx+2OpZDRAkEHVYlTgJByyMIOlZk\nen9XUx3LIqIEgg6rEieBoOWRCwTtucXzF/T4wFTHsogogaDDqsRJIGh5RGtgDjbHXNCiMpkK\nukHQZyJKIOiwKnESCFoesS9owZa2tJWbaf8tdSyPiBLFCVr0+XpwlUgJBC2PXCBoX8N5Ni1u\nOEmkQdCnIkog6KAqkRKhgpamELRjInvQ84Wa6UNDHcskosTVgl4ugaADIghaI7G/PSYzfTdc\n1oKe/dYP+CPN0rpcElHiYkErfMEcgkbQKgkEPVtm/ieDnY7lElHiYkHf73dh4qgKgvatgqAX\nUxH0ZBEEfTKixPWCnhsaQQdEggQtDNl9IyDoCJFm8aYy07FsIkpcK+gvPS+GHkEHRBC0RiKe\noP23tZmtvHxbmulYNhElrhb06pVwtgqC9qyCoFeT91pC0BGqlBxR4nJBL/+YOlsFQXtWQdCr\nyQh6WAJBn40ocamg7xEEvfzU8TgRUiVSAkHLIwg6QmR1cpWVjuUTUeJiQT+WRkXQAREErZJA\n0OMSy0UCDG1lXS6KKHHxIY7Xf1OlIuiACIJWScQTdEjEFwRtM6LE9XvQCPpkppG/fRC0e/pO\nU1UJen1aHYK+iCsF3bl0fkr82SoI2rMKgl5PR9DdAqslEPRFXCnobtAR9LkMgtZKIOh+AQR9\nOqKEAUHPBh9Bh0QQtE5iZ5tUL2hxHSPrclVEiQsF3SBolQyCVksg6HY+gj4fUeJSQXcPpk5F\n0CERBK2U2N4odQnaMQlBX8KFgh5VOpEqgg6JIGilRCxB+2ZsbGWXoANvmVhvRAkEHVQlUiL4\nxRNgWwTtnLU1D0Ej6Cu49DS7YbFxOQQdFEHQSgkE7V4AQV+EiT1oBH0mg6AVEwj6wR60SkSJ\n6wQ9Pa6BoE9kELRmYmuzVCRoV18R9EVcJ+jG+ZJH0EERBK2VQNAIWiWiBIIOqhIpgaDlEe2B\n2dguJwXtGTKxlRG0RkQJBB1UJVICQcsjCFo94hR00LmcQkqKKHGZoGdfRjj5vPBsFQQdLyIk\nT0FvbJiKBO3eg0bQl3CdoO/OJwg6KIKg9RLui+bqEfTW6iPoK7Ah6PEZgg6LyN49CHp39sYu\n5Dg/oAyCriyixFWCvkcSdLT3AYIWk6ugW0MvFkLQxbxiEPTA9no18x1dBB2eQdDqiaZlI4Og\nFaoUH1HiIkFvfq07gg6LIGjVRLNS9FlB+6UsbGUErRJR4jJBbzxH0GERBB0hMTU0gi7mFYOg\nBxB0ggyCjpZA0NNIKa8YBD2wuV7Lcy0QdHgmTNDFfCYfdWCcV1AVLmjn7MC9ACElRZS4RtBN\ng6DVMm1E9OZB0N6LOl6YhQt6+zRwBJ0eBB1SJVYCQcsjkQem307VCHrnQkoEnZ6rBL01BUEH\nRoSCFn/HnN03QiaC9ooZ2MoIWieixCWCdn5r8G7CuwqC9ouUc2+y2APTbSkEjaCv4BJBOy7H\nRtDBmRBBy7+k2e4bIfbAtJsKQSPoK0DQIVViJRIKOqyKwUj8gXltLAS9OS+kSvkRJa45Br0W\ndO/s0+9QBO0TQdCSxee35vDedghaSkkRJazsQWsJmj1orwiCFi3fCbrdbAhaoUr5ESWuELRr\nmJUEfffL2/RAYAZBR098ba5muD9HqKB9hsjAVnbfrxdBX0Rpgva5G7RZD4RluojkzSP3s+E3\nQiJBt2auW9CyF5n/oiVGlEDQAVWiJRIJOmAH2vAbIcW6jF4WbDsELaWkiBIXCNr5EkfQwZkQ\nQQdXsRdJMzDjjrNgM6/aEEcCqpyNIGidiBIXCNr5CkDQwRkEnaCKxh8dCLqmiBJ2BH3fTvhX\nQdBeCbOv6hIHZnyOoCuKKHHFMWjnKwBBh2bE752QvUHDbwTrAzM+R9DzBVatGVh9tYgSVvag\nEXRwBkHbrLKOHG93A1sZQetElEgv6I0xRtChGQRts0qJghYdSDtcYPVCNLD6ahElzAh6fdOw\ngCqr+0wfJkKqxEsgaHnE/MBMJmQg6I23UARBO04pv3719SJKlCZoaSKkSrzEqReP/wUUCDpF\nlVwFvRtRFfSquetXXy+iBIKWV4mXQNDyiP2BGScg6EUlBH3EFRequBdF0IEZ+XsHQaeogqCP\nF0DQR7AHLa8SL8EetDxif2AmUw73KjWqnIocCFqyG3A0v1k1d/3q60WUSC7orfvNIejQjFTQ\nr1uzBVcxF7E/MJMpCHpRCEEfkF7QW+dZdAN2qkrlgvYLI2gEvTMPQetElEDQ8irxEudePAja\nVBUEvT+7CEFHN3f6Y9AIWjkjfO80CBpB78xLK+h5e9evvjAS8O3LQtiDlleJl0gj6Ktf1bqR\nDAZmnIKgl4WyFrToC3bCSC3o7dVpzY2gwyMI2lQVV+RoiK7fykeCFpws5DU/Z0FH331+IOiQ\nKvESCFoeyWBgJpMQ9KKdVIKe1FEUtLwlKQhaXiVeAkHLIxkMzGRS/oJWepP1raQT9Pg+UaqS\nYgfalKDvzoSkCoL2WBxBI+ideYkFPWsu3uo3UQQtb0iMVNC3223rgQOJoB8IOiyDoG1WcUYO\nxujyrbzVPwTtanI/JHPlViPyxW/uBy4QdIIMgrZZBUF7zc5Y0PsZoSt3WhEuPZa5TR44EQm6\nvTZf1J9lFQR9vDSCRtA7s8rcg270BX20Ay115V4z8giCNlNFLOiTVaxFMhiY6TQEvWgkkaCn\nvxJUqnhthisEfUPQ0RInXzw+q4+gEbQpQUc5Ac5VTlfQXqdwSFy51YRs8f4wt0lB+1/VU6gH\nELSlKgh6b2ZSQTcxBH0YkrlyqxHZ4n3EV9AAAEWj48qdFqTcwveg73urE/jx1WQP2u8rY4vd\nUWMP2lIVd2R/jC7fyh570L670HtLNan3oKd1NKrse3nE35XbDYg5I+g9hSJoBC2P5DAw04kI\netFEpoL2CyYW9LoWgr66CoI2WgVB+zWRStDj1eUKVY63gdSVu814L332QpWYgr4jaI+3DoJG\n0CcF7XtF1LLlxq3iJGdxTAslEvQVF6qcvtQ7qqB99VyuBxC0oSobkd0xunwrRxP0/AO1WQv6\nd8lYLaQtaK9D0Fdc6i0EQSfIIGibVRD0LDJ1ZLPRXKTVH8zcaFXxPQR9HkuCfs5E0GciCNpQ\nFQQ9TTTTn2kF3SDoLVIK2v8IdLkeQNCGqpQraM+bCjgD8xMqljM9mt3tmHOJ5YGV81V8T7JT\nwJag76cELU2EVImbiC/oBkFfLOjdQbp8K8cRdHOtoBePNAQtbyIQS4J+IOgEgj5fxVYki4GZ\nTTYs6M1dw7OCnj9OKejlLwcEPQFBJ8hIBN0g6GRV8hS0V0Qo6OZaQS8fK1RB0PIqCNpL0ApV\nbEWyGJjZ5AoFvQg3G83lIuhi96APVuw5dOFVEDSCtlQFQbsXz1/QCf1ckKDZg0bQpqpsRnZG\n6fKt7CdorzfbeODiUkGv3x8IeiChoO+SZLEeOHrpIOh0VXIUtOcetFTQy+xVe9BKJ5ki6IAq\ngstUCvbA4QZG0MmqaAp658Rbza3sXeWMoB+reyjHFLTj88jzVRC0vAqCfj0+3MAaVUxF8hiY\n2XSxoPfuC29e0Kuur1cm4usyhqDZgw6ogqBfj3fzDYJOWEXBg8Pyje8Zyl4odMzDUaOgV9m8\nBZ3Szwg6YsagoHWqmIrkMTDzGTJBd1d/bqSyFPRGawh6BYKOl0HQKSJ5DMx8hkjQ442GZFW2\nuVjQLmUfdOy4yuZ8BL1DQkELbpVUsAd2N3CDoFNWURL08l5s3lW2uVrQm3WjC/r1FEEPiAUd\ntO6NR9OOREiVyIm4L55xnlnbljswsxkyQe+nrhL0YV0ErYEpQfsM+1YVBN0+QdBWquxEBKdk\nTG+W6Uxd9JsDQaehFEHLkuV6YO8ULgSdtIqKoGff4+eMIWivpidPEfRASkFLEyFVYiciC1qt\niqVIJgMznyMQ9FGsBEH3yyDoFQg6XgZBp4hkMjDzOTYFLbleEUGnAUHHy9gS9MaX3gdVsRTJ\nZGAWszbmXS1o/0gmgnZdVX6ySsLvu3og6JgZa4LWq2IpksnALGZ5CXp1hbcrdpGgj99xRgS9\nnnBa0PJ8OLYEfRfdk25aBUF3TxC0lSqnBb2+AweC3qyy3/BsAoIeQNAJMn6CbhB04ipnBe34\nc7JYQXcLqb9inFeVI+gBBJ0gc7yTtZpu1rYFD8x81qGgnRdtIejNKnvNLqYg6IHFqtwRdIQM\ngrZZZTeyMUzNON/9iaDDOOJ+6Qj6+AMlz+UmC2m/Ytx/hCDogaWgj+6XESzoQ/WvEiFVoifi\nCbpB0KmrHLljN7I9jKu/SqXdqkfQzl9yQSciIOh+gVBBS26VVLQHNt/ZqlUMRXIZmOXMLUE3\nHb45BH3Q6Goigh6QC1om2qEKgh6eIWgjVQIOj96b5sDP69xlgj4y1eWC9j5KJKtSrqCb41uC\nIuizEQRtpYpU0Idqduf0Orb7a8GjJ+6InxAjCHrr5lIIesTLHVMQ9NkIgrZSRSjo9nutPFqN\nKGhhZL+7TbeMVwejCHpjMoIeEAs6aOUR9PQZgjZS5SiyGJLX07wEvf92bZXr+472XX1nFXeD\nCPoQBJ0gs9oTcy6EoFNXOYxMxqQ/tOF/NMC7iqsFr4Y9IgeC9jliM2tKV9Bb089VSevntB8S\nIugYGQRts4pA0IPIMhP07seKfofUZy0pbuTtb0FH0CPsQSfI+Ah6edOd01XsRLIZGMcCTf+z\nX7QYQYvkPLSkuS7iXWvPKuUK2uccZwR9OrIhaOUqdiLZDIxriZdHpF91E+uXbYCg9y6okXVM\nV9CS8xSFVQoWtIdEEfTpCII2UsXvE7+5ygoRdPt7R9gpeeQVc1bfXRUEPYCgE2SWEdc2RNDp\nq/jadjo2ZQi6uVTQR3vvCHoEQSfIIGibVTwjAfeBnY3mtYJ2hQRnpCwa0liXw2MrCHpkvi4I\nOkoGQdusEtPpUaooCVpyRsqiIZVX/2EjAZKpQtAhl0n5VkHQk+cI2kSVSgU9GPISQft8Nomg\nB2YvJJ8VCz1JEUFPniNoE1XqEPTqpSU6ZXDRzul18fIHgh4QCzr0ABGCnjxH0Caq1Cro48hm\nO2fXRXJjptAqCDqkivSkS3mRfDyAoG1UqVLQ0nO65w2dXBfRjZlCq9Qu6IDb2SHo+XMEbaJK\nxIj8xA+fKmGCXvy6GJ8kF7Tv8VEEPdBsPtki5H6jCHoxYd0Igk5fJTdB75887NubSwXtXShB\nFSWs7UEj6POR7TOfNKtYiWQ0MGoRs4IWXxU5b+bUugjubCquU4GgPb9tEEGfj7gErV/FSiSj\ngVGLmBX040THTgpacuM8BN0xFbSfeRH0+QiCNlElZuTEjupmJFjQ0zM3rhS0fyEE3YGgE2QQ\ntM0qNQm6cTSRVtAC6yLogRBByw2NoBcTELSFKvUIussublKUWtCCQgi6Qy7oBkGfjzguv41Q\nxUgkp4FRi1gU9PImcnILBl4EIc0i6AEEnSCDoG1WqUnQD8c9PhMLWhIRF0LQQwJBn48gaAtV\nELSQE4IWRRH0QICgw84iR9CzKQjaQJWokfArqjcjpwTt+uDDv0N9EycELYog6I7peiHoSJlj\nQZ/fuTEcyWlg9CLmBK0QCRe08PtpEXSPfL3CBC0NFe6BtaBjVDESyWlg9CIlCjrIfY08iKAH\nEHSCDIK2WQVBixOBVYTSQNAD47r432kKQZ+PLLYHgr6iCoIWJ4IFLYwg6I6poD0TCFohgqAN\nVIkbGcYUQSPoYBB0ggyCtlkFQYsTYVWkzkDQAwg6QQZB26yCoMWJoCpiZSDoAQSdIOOKLC66\nRdAXVEHQ4kSgoFNExofVC1q8ARD0ehqCvrwKghYnUgn6jDBqF3TABkDQ62kI+vIqCFoeCZE6\ngg4HQSfIIGibVSJH+kFF0OIIgu4JErT4bkkIej0NQV9eBUHLIwh6iT1By29nh6DX0xD05VUQ\ntDyS5Lg1gh5A0AkyzshsiyDoK6pkJuj99xCCPlHwDIkEfUfQ0TII2mYVBC2PIOglqQTta10E\nrRNB0JdXQdDyiPyMDAR9BgSdIIOgbVaJHelGtXZBB1RB0D0IOkHm8O22ftnbs014JLOB0Ysg\n6McFgg76VoETWBS01NAI2jW1cT3UrmIgktvAqEUQdGiVh9jQe2+muNgTtHwXGkG7piLoi6sg\naHkk1R40gm5B0AkyR4J2vOrt2SY8ktvAqEUQdOABBwQ9ECTogGvrEbRjcr9ZEPQ1VaJHuuFV\nqoKgDyJ9RXnBMyDoeJmrBd3dRwZBX1MFQcsj9gUd9OI5AYKOl7nYA72hEfQ1VRB0QEQqTvag\nT4GgE2S2Bd2+gB0bx6BtgiP5DYxWBEGfEbRsU48F5fVOgaDjZSx4oD/OEbfKlZFMB0YhgqDD\nBS0shKAnCQStGUHQV1VB0AERoTcR9Dnk64WgiUTPFDIwCPp5AC+kygNBtyDoBJnKIxUPTHuS\njlIVG4KWvZUR9EkQdIJM5ZGKBwZBI+hzIOgEmcojFQ8MgkbQ5wgUtFi3CLreSMUDg6AR9DmC\nBC3XbcDdO+TU6wHbkYoHBkEj6HMg6ASZyiM1D8zrWiSlKgj6KCKPKZBG0P6rhaCJRM8UMzAI\nGkGfAkEnyFQeqXlgEHRiQSf3M4KOmCnGA6YjNQ9MgYIWvftPCFr4m2AsmBgEHS9TjAdMR2oe\nGASNoE+BoBNkKo/UPDAIGkGfAkEnyFQeqXlgFAV98KZD0PKQCgg6XqYYD5iOVD0wZwy1akka\nCajiEUHQMxB0vEw5HrAcqXpgEDSCPgOCTpCpPFL1wCDoE4KWFBoLJsaioO/CzSC/azceKCdS\n9cAg6PCOIehQQUs3A4KuOVL1wBQoaMm7H0GfBEEnyFQeqXtgwr/zadWQOBJQxSeCoKcg6HiZ\ngjxgOFL3wCDowCoI+gmCTpCpPFL3wCDowCoI+gmCTpCpPFL3wCDowCpBgg7a2ucoQdDis+zw\nQEGRugcGQQdWCRS0vNpJ0gjaX6EImkj0TEEDU6CgJbtzCPocwYIWbQgEXXOk7oFB0KFVRJsO\nQc8TCJpIxExJA4OgA6vIjqWIE1og6HiZkjxgN1L5wCDowCoI+jGs110saJFyEXTNkcoHBkEH\nVkHQj4mghQmZchF0zZHKBwZBB1ZB0A8EnaJK7ZHKBwZBB1ZB0A8EnaJK7ZHKBwZBB1ZB0A8E\nnaJK7ZHKB6ZAQfuvE4I+CYJOkKk8Uv3A6Kw/gvaIIGgETSR+pqyBQdBhVRA0gk5RpfZI9QOD\noMOqiA52SwNqIOh4mbI8YDVS/cAg6LAqCPqEoEVbAkHXHKl+YBB0WBUEHS5o2ZZA0DVHqh8Y\nuTWsC9r77Y+gT4KgE2Qqj1Q/MAg6sAqCTiRonVeofqYsD1iNVD8wCDqwCoJG0CkylUeqHxgE\nHVgFQQesF4ImEj1T1sAg6MAqCDqRoDkGXXOk+oFB0IFVEDSCTpGpPMLAiLWBoGVlEPSZBIKu\nO8LAaAj6qI0iBS044Vq4vB5mBS3YFAi65ggDU56gfVcJQZ8kWNCSiOALtZZV4mYK84DRCAOD\noAOrIOhEghb0aF4lbqYwDxiNMDAIOrAKgj4haH/rIuiqIwyM2BsIWlYGQa8SEusi6KojDEy1\ngn4ulVLQF/g5iaAl64WgiUTPlDYwCDqsCoJG0AkylUcYmAIF7bdKCPosCDpBpvIIA4Ogo1YZ\nIwgaQVv2gM0IA4Ogo1YZIwgaQVv2gM0IA4OgQ6v4X7IoWloTBB0vU5oHbEYYGAQdWgVBt/8j\n6JiZyiMMjNgcCFpU5oGglwnR9dsIuuoIA4OgQ6sg6Pb/AEELtIugq44wMAg6tAqCbv9H0DEz\nlUcYmBIF7bVKCPos7QpJDIqgiUTPFDcwCDpilTGCoBG0aQ+YjDAwD6k6ELSozANBLxOiEIKu\nOsLAPBB0aBUE/QRBR81UHmFgHucFfZhH0P4Lq5K/oO93q+/Q8jxgMcLAPBB0aBUE/QRBR81U\nHmFgHgg6tAqCfoKgo2YqjzAwDwQdWgVBPwkUtGcKQdcdYWAeJQraZ5UQ9FlOCNo7haDrjjAw\nDwQds8oYQdAI2rYHLEYYmNf/kiSCllQZIwg6RNBNwGbDA+VEGJjX/wg6sIpXA0ULOvC1g6CJ\nRMoUODAIOqyK54ZD0LPECwRNJFKmwIFB0GFVJIIOMc1pEHS8TIEeMBhhYF7/Fydoj1VKLmh5\nrdMg6HiZAj1gMMLAvP5H0LGqDBEE/UDQxj1gMMLAtD8EUQQtqdJHLjnCgaAjZkr0gL0IA9P+\nQNCRqvSRS/yMoCNmSvSAvQgD0/5A0JGqdJFrdqDFgr590T94zB84QNAJMpVHGJj2B4KOVKWL\nyDUjc+VWI/LFb+4HLs4J2s/QCLruCAPT/kDQQVX8NlyIoIWu3GlFuvTN9cDJGUH77kJzqXfd\nEQam/YGgg6r4Clq8Hyh15W4z4gyCNlOl9ggD0/30z+YhaI9OJRW0vNADQT8QdO0RBqb7iaDj\nVGkXykjQ45EUBG2gSu0RBqb7iaDjVGkXCvazbUGf4cu8iosBAERAx5U7LQQk2IM2U6X2CAPT\n/axuD/o13/QetMSVe03IA3YE/bWU1XdokR4wF2Fgup8IOkoV/4VWiFy524Z4eQRtpkrtEQam\n++n/PstE0Ae9atIJOmwHWubK/UbEi8e/UAVBE4mUKXNg6hO0ShU/QcurSF151Irf0i3dw2Ha\n5vIIOkGm8ggD0z+oS9CNkqC9LogJqCJ15VYz8soCEHSCTOURBqZ/UJugU1Tplgg6BK0Cgo6X\nKdMD1iIMTP+gKkE36QR9cCpdXEwL2sfQCLryCAPTP6hM0Cmq9Asg6FmiA0ETiZIpdGC832i5\nCHqvW+kE3SDoZaIDQROJkil0YGoSdJNS0GGvGB0QdLxMoR4wFmFghkdVCVqtCoJG0FEzlUcY\nmOFRPYJuELQKCDpBpvIIAzM8qknQelUOztFoEPQq0YGgiUTJFDow4YL2OBXYv0OKkSSC9rlg\nEUHPEh0ImkiUTKkD4/tOy0bQmx1D0Dog6ASZyiMMzPgQQYdUqV3Qnvd1niQ6vK5UQdCVRxiY\n8WE1gn4gaBXOCdoriaArjzAw48NqBM0etA4IOkGm8ggDM3nsl89e0JPJCPoMCDpBpvIIAzN5\njKADqiBoaaIHQROJkSl2YBB0SBWP204j6FmiB0ETiZEpdmCKE/RGz3QF7XHXPAQ9S/QgaCIx\nMsUODIIOqoKghYkeBE0kRqbYgUHQQVUQtDDRg6CJxMiUOzB+hkbQPkWm8xD0LNGDoInEyJQ7\nMAg6pAqCFiZ6EDSRGJlyBwZBh1RB0MJEj0fyuYjVd2i5HrAUYWCmTxB0QJW6BS3y81LQh1kE\nZMyXFgAAC4NJREFUXXuEgZk+KU3Qzq5NpyHoM5wUtEcWQdceYWCmTxB0QJW6BS262yiCJhI/\nU/DAeL3b8hL0unMIWgsEnSBTeYSBmT0rUdDLydqC3lt/BL1OjCBoIhEyBQ9McYJ+9m3ZPQSt\nxWu9OAYdN1N5hIGZP/VoIitBP9bdQ9BaPNeLDwkjZyqPMDDzpwGCPvhea1fEA8XIon8IWotG\n6mcETSR+puSBCRO0OOKBZmTeQwStBYJOkKk8wsDMnyJoeRUELUlMOL5S5bn9rL5DS/aAnQgD\nM39agaAfCFqJs4I+TiPo2iMMzPxpgG3tC3reRfU96J0tgKBXiSkImoh+puiBKV/Qc1mrVKlX\n0B6301gkZiBoIvqZogemTEHP7o+EoLVopDvQCJpI/EzRA4OgA6pUK2jpDjSCJhI/U/TAIOiA\nKhULWpqYP0XQRPQzZQ+M/KqTHAQ96SSCVgNBJ8hUHmFglhMQtLhKrYIWH+FA0ETiZ8oemEIF\nPfYSQash9jOCJhI/U/bAiAUdcuq0B7EE3SBoNRB0gkzlEQZmOUEu6IAq6SOjoCNU2dwGCHrO\nStAHLSDo2iMMzHKCUNA+O9AmVr/rJ4LW47SgD1vgZkm1RxiY5YRD4c4NF3aP/wsinSsRtB4I\nOkGm8ggDs5oiFHRYlQsirz+Xt++bdKZKnYKWn8SBoInEzxQ+MCULerW/j6BPIPczgiYSP1P4\nwIgE7XeEw8jqu75AVqnK1mZA0HMQNJHomcIHRijowCqXRBC0KvEF/Zpt9R1auAeMRBiY9aSj\nQwXTRbMSdLwIgvYDQROJnil9YESCDq5SVgRB+4GgiUTPlD4wCFoeqVLQGu+dfUMj6OojDMx6\nkregfY9wZLX6YZFNQwdWUcK8oPdfQQi6+ggDs57kK2hvP2e1+mGRjU3BHvRBAkET0c6UPjAC\nQZ+oUlrEuTH6y8vlVZRA0PEypXvARoSBcUzbb6cZFkPQ0+nbv+sQ9GYCQRPRzhQ/MN6CPlWl\ntAiCDknsvoiaBkHXHmFgHNO8BC3Ygc5r9UMjju2BoA8TO6dxNAiaCAPjmOYp6HNViou4riRH\n0EeJbUHfmzuCrj7CwDim+QhasgOd1+qHR9b3YkLQR4lNQQ/3yrP6Di3eAyYiDIxjmp+gT1Yp\nMYKg5Yn9a3y0qkTIFO8BExEGxjVxt6GmXQRBu2Y282cI+jCxeQY5giYSlCl/YI4FLfNzZqt/\nIjL/RtrhexDlVZTIVdD3Zjz0YfUdWr4HLEQYGNdED0Gfr1JkZJRy0yBor4TjKHT38aBmFf1M\n+R6wEGFgnFN3D7QeLeBbpcRIZ+WpnhH0fmIQ9HhQo5k62+o7tAIPGIgwMM6pB4IWHuHIbfVP\nRZpmbuewKkpkIuivPebm9e85f3p4Q6+KfqYCDxiIMDDOqYeC1qhSamR1QjSC3kvc768znrtf\na3eFrYcHyokwMM6p+4KW7kDntvoGIkrkIOjugsFWzU9RR6min6nAAwYiDIxz6pGgVaoQiU4O\ngh5eTvfXvnSsKuqZCjxgIMLAOKci6IsjSmQh6IH73XVdodV3aAUeMBBhYNyTd5pC0AkiSuQm\n6BRVtDI1eOD6CAPjnrwnaPEh6OxW//qIEnkJ+oGgiZzP1DAwB4JWqkIkNpkJmipEzmdqGBgE\nfXFECQSde5XaIwyMe/J2UwF+zm71r48ogaBzr1J7hIHZmL7ZVoCf81v9yyNKIOjcq9QeYWA2\npiPoSyNKIOjcq9QeYWA2piPoSyNKIOjcq9QeYWA2piPoSyNKIOjcq9QeYWA2piPoSyNKIOjc\nq9QeYWC2ZsjVHVCFSFwQdO5Vao8wMJtzNmYh6BQRJRB07lVqjzAwm3MQ9IURJRB07lVqjzAw\nm3MQ9IURJRB07lVqjzAwm3Pcs0IuJLx8XfKLKIGgc69Se4SB2ZyDoC+MKIGgc69Se4SB2Z7l\nnIegk0SUQNC5V6k9wsBsz0LQ10WUQNC5V6k9wsBsz0LQ10WUQNC5V6k9wsDszHPNRNBJIkog\n6Nyr1B5hYHbmIejLIkog6Nyr1B5hYHbmIejLIkog6Nyr1B5hYPZmruY2Ad8Ye1SFSDwQdO5V\nao8wMHszlz5+PTXQsfIjSiDo3KvUHmFgducuFP16aKFjxUeUQNC5V6k9wsAcLjEqukHQCHqC\n1fdOSVVqjzAwPgs9xTyI2lDHyo0ogaBzr1J7hIHxWmp6rMNSx4qNKIGgc69Se4SB8VtsciTa\nVMdKjSgRV9AAABAMggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIG\nADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqC\nBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADBKXEHfAACyJ6omdxVqrPWQ\n/tRdpfYIA0PEYkQJBJ17ldojDAwRixElEHTuVWqPMDBELEaUQNC5V6k9wsAQsRhRAkHnXqX2\nCANDxGJECQSde5XaIwwMEYsRJRB07lVqjzAwRCxGlEDQuVepPcLAELEYUeK6ygAAsAuCBgAw\nCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYA\nMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIG\nADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwShRB32634cFtOvW2mr14cLqy8+GswG029bYx\ne7dHalX2t7/mymzXyW6T7WwzrSq3Wzmb7La/MlmtS+QqrrpXE6Mjt77d4YF7qns5zV6saj+3\n/W4/+xHy7dGpKrPl4pVJszKpNpnnNju9Ln7kscmKGf6oVeL6KJAI/Rg25ezFvp7qXk65G/Pa\nz/9uq6nr2f49OlNltly8MmlW5rZoIE6Vh/c2O1XF/7Vof2C8i2SwLlGrRPZRIAHd6Pb/b4+N\nPwgW67ecF3WD3IaRcA/RbVXn5pp90COlKo7lopRJszK7RfSq7G4z1eHfJk0ZxYHZqZTVOyZu\nlY0p1yLvxq3/d5s+WTXpfnFMV3/94DxHQ7Sus6yv8ULwrLJbQ7dMopXZXhu1Kus3mX6V/SPQ\niq+yoyOqCV5lWlWWFohSJfr70jXlWsTdGHo/3xRbG2Re4PZYNqC8QcRDdHPO1n4huKvs1tAt\no/ne2aqia5uNKsoe2Bn++CtzS7Iy+6uS1/BHf186plyMuBtD7+ebYmuDzAvMVn/+jtP53DSX\nF8JtHopeJokHElS5rReMUMW1ZIQyyV5lKV5kt9WC0apE3GKOKRcj7sZw1s6wKZan8WxvkPnL\ncrLcM6+yQaRDtH6jxHjrbFTZrZGsjGKV6Jvs6K2TZl2ye5UleJGlWpddU6RZl7SIuzF/z98c\nLdxW/7tnLJZQ2SDCIXJ0NcLLbavKbg3VMmmq7BXSqXI7Pqk3wbpk9ypLMPzp3jExqzjqXo24\nG0Pv55vCvUFu6+SiAWcL4ciGaNmjRb+VXgibVXZraJbZ3bTZbbKd1UmzLrltsnKGP3IVV92r\nkXfjNvk3f7JcYj11PXv+4DTd0NzmQzQr4Hijr2fv90irym4R3ZWJXsXvEy+NTZagysELMrNX\n2e7457UuUas4615NQD+G86CH/5bXX/VLTP8enTyJfKl3d5hqtpWdV1k7ejS+qI8uKVWpsr/9\nlcp4XOub1Sbbf4cmWJfcNtnueyu3dYlXxV33asx0RIc0q5Noo5W0MiVVKWplqGKbwtapqBdC\nSStTUpWiVoYqtilqndL8YZLoz5+SVqakKkWtDFWsU+RKAQCUAIIGADAKggYAMAqCBgAwCoIG\nADAKggYAMAqCBgAwCoIGADAKggYAMAqCBgAwCoIGADAKggYAMMr/PC8Z6glgYEYAAAAASUVO\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(resdiaria02_xts, screens = factor(1, 1),\n",
    "     col=c('dark grey','red'),\n",
    "     main = ' Embalses Tranco de Beas. Reserva diaria (hm3).\\n Datos interpolados')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Creación de la serie de datos mensuales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta serie es generada a partir de la serie de datos diarios de manera que los datos del último día de cada mes será el valor de reserva mensual asignado. Este proceso puede hacerse por diferentes vías, pero el disponer nuestros datos en un objeto xts nos facilita una via rápida."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            reserva\n",
       "2009-01-31 229.2731\n",
       "2009-02-28 279.5406\n",
       "2009-03-31 319.4200\n",
       "2009-04-30 336.4608\n",
       "2009-05-31 324.6378\n",
       "2009-06-30 275.8100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "           reserva\n",
       "2020-10-31  158.43\n",
       "2020-11-30  159.97\n",
       "2020-12-31  198.19\n",
       "2021-01-31  241.31\n",
       "2021-02-28  296.35\n",
       "2021-03-31  306.54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_xts <- apply.monthly(resdiaria03_xts, last)\n",
    "res_xts <- res_xts[1:(nrow(res_xts)-1),]\n",
    "head(res_xts)\n",
    "tail(res_xts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Análisis exploratorio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este apartado se analizan las carácterísticas básicas de la serie de datos y su distribución."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Index               reserva     \n",
       " Min.   :2009-01-31   Min.   :140.1  \n",
       " 1st Qu.:2012-02-14   1st Qu.:272.4  \n",
       " Median :2015-02-28   Median :323.9  \n",
       " Mean   :2015-03-01   Mean   :323.1  \n",
       " 3rd Qu.:2018-03-15   3rd Qu.:391.1  \n",
       " Max.   :2021-03-31   Max.   :483.4  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAABNTU1oaGh8fHyM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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Embalses de Tranco de Beas. Histograma de datos mensuales.\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(res_xts)\n",
    "hist(res_xts, n=20, col='orange',\n",
    "     main ='Embalses de Tranco de Beas. Histograma de datos mensuales.',\n",
    "     xlab='Reserva (hm3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para los siguiente análisis, las librerías empleadas requieren volver a trasformar la serie anterior a una estructura de serie temporal tipo ts que permite también mostrar una serie de datos mensuales de forma más compacta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A Time Series: 13 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Jan</th><th scope=col>Feb</th><th scope=col>Mar</th><th scope=col>Apr</th><th scope=col>May</th><th scope=col>Jun</th><th scope=col>Jul</th><th scope=col>Aug</th><th scope=col>Sep</th><th scope=col>Oct</th><th scope=col>Nov</th><th scope=col>Dec</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2009</th><td>229.2731</td><td>279.5406</td><td>319.4200</td><td>336.4608</td><td>324.6378</td><td>275.8100</td><td>220.6600</td><td>166.8867</td><td>157.7400</td><td>156.9386</td><td>160.1100</td><td>211.5118</td></tr>\n",
       "\t<tr><th scope=row>2010</th><td>296.4729</td><td>383.0905</td><td>426.7000</td><td>436.6800</td><td>452.3600</td><td>449.4300</td><td>431.1043</td><td>411.2700</td><td>388.1300</td><td>382.2663</td><td>404.0000</td><td>424.7400</td></tr>\n",
       "\t<tr><th scope=row>2011</th><td>411.0000</td><td>434.6200</td><td>426.8100</td><td>440.3700</td><td>454.4500</td><td>440.6000</td><td>416.9800</td><td>390.1200</td><td>376.8900</td><td>372.2700</td><td>378.3300</td><td>382.6700</td></tr>\n",
       "\t<tr><th scope=row>2012</th><td>385.0800</td><td>385.6100</td><td>379.0800</td><td>386.8900</td><td>381.7700</td><td>356.9100</td><td>312.7100</td><td>272.4100</td><td>250.9100</td><td>253.1200</td><td>287.7100</td><td>314.5300</td></tr>\n",
       "\t<tr><th scope=row>2013</th><td>396.0500</td><td>438.0700</td><td>483.4400</td><td>463.1100</td><td>473.3700</td><td>465.5300</td><td>442.9000</td><td>419.9600</td><td>397.7500</td><td>381.7700</td><td>375.6600</td><td>380.8700</td></tr>\n",
       "\t<tr><th scope=row>2014</th><td>407.0900</td><td>426.9100</td><td>433.2900</td><td>445.5800</td><td>445.2400</td><td>423.0200</td><td>392.0500</td><td>365.4300</td><td>353.1000</td><td>350.3100</td><td>356.8200</td><td>362.7500</td></tr>\n",
       "\t<tr><th scope=row>2015</th><td>370.3400</td><td>416.3200</td><td>429.6500</td><td>436.2900</td><td>429.3900</td><td>405.9800</td><td>379.9000</td><td>352.7800</td><td>332.5300</td><td>323.8500</td><td>321.6100</td><td>314.7400</td></tr>\n",
       "\t<tr><th scope=row>2016</th><td>316.1600</td><td>350.0000</td><td>365.0200</td><td>387.1500</td><td>401.6300</td><td>379.6900</td><td>347.8500</td><td>315.7300</td><td>290.1300</td><td>275.9400</td><td>275.9500</td><td>278.8707</td></tr>\n",
       "\t<tr><th scope=row>2017</th><td>279.7900</td><td>283.2600</td><td>289.8800</td><td>281.0000</td><td>263.0200</td><td>234.4600</td><td>203.1300</td><td>175.7500</td><td>151.1100</td><td>142.1400</td><td>140.1300</td><td>152.2500</td></tr>\n",
       "\t<tr><th scope=row>2018</th><td>163.3900</td><td>172.4000</td><td>283.1300</td><td>332.2400</td><td>345.5300</td><td>338.0000</td><td>313.6800</td><td>288.2600</td><td>273.6800</td><td>264.8500</td><td>272.0900</td><td>275.9600</td></tr>\n",
       "\t<tr><th scope=row>2019</th><td>279.3100</td><td>297.3900</td><td>291.0100</td><td>311.9900</td><td>294.4900</td><td>262.6400</td><td>227.5300</td><td>192.8300</td><td>178.8800</td><td>168.5500</td><td>192.5800</td><td>254.8000</td></tr>\n",
       "\t<tr><th scope=row>2020</th><td>266.6600</td><td>272.4500</td><td>277.2000</td><td>292.3500</td><td>300.6500</td><td>274.6400</td><td>232.0100</td><td>187.8200</td><td>162.7600</td><td>158.4300</td><td>159.9700</td><td>198.1900</td></tr>\n",
       "\t<tr><th scope=row>2021</th><td>241.3100</td><td>296.3500</td><td>306.5400</td><td>        </td><td>        </td><td>        </td><td>        </td><td>        </td><td>        </td><td>        </td><td>        </td><td>        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A Time Series: 13 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & Jan & Feb & Mar & Apr & May & Jun & Jul & Aug & Sep & Oct & Nov & Dec\\\\\n",
       "\\hline\n",
       "\t2009 & 229.2731 & 279.5406 & 319.4200 & 336.4608 & 324.6378 & 275.8100 & 220.6600 & 166.8867 & 157.7400 & 156.9386 & 160.1100 & 211.5118\\\\\n",
       "\t2010 & 296.4729 & 383.0905 & 426.7000 & 436.6800 & 452.3600 & 449.4300 & 431.1043 & 411.2700 & 388.1300 & 382.2663 & 404.0000 & 424.7400\\\\\n",
       "\t2011 & 411.0000 & 434.6200 & 426.8100 & 440.3700 & 454.4500 & 440.6000 & 416.9800 & 390.1200 & 376.8900 & 372.2700 & 378.3300 & 382.6700\\\\\n",
       "\t2012 & 385.0800 & 385.6100 & 379.0800 & 386.8900 & 381.7700 & 356.9100 & 312.7100 & 272.4100 & 250.9100 & 253.1200 & 287.7100 & 314.5300\\\\\n",
       "\t2013 & 396.0500 & 438.0700 & 483.4400 & 463.1100 & 473.3700 & 465.5300 & 442.9000 & 419.9600 & 397.7500 & 381.7700 & 375.6600 & 380.8700\\\\\n",
       "\t2014 & 407.0900 & 426.9100 & 433.2900 & 445.5800 & 445.2400 & 423.0200 & 392.0500 & 365.4300 & 353.1000 & 350.3100 & 356.8200 & 362.7500\\\\\n",
       "\t2015 & 370.3400 & 416.3200 & 429.6500 & 436.2900 & 429.3900 & 405.9800 & 379.9000 & 352.7800 & 332.5300 & 323.8500 & 321.6100 & 314.7400\\\\\n",
       "\t2016 & 316.1600 & 350.0000 & 365.0200 & 387.1500 & 401.6300 & 379.6900 & 347.8500 & 315.7300 & 290.1300 & 275.9400 & 275.9500 & 278.8707\\\\\n",
       "\t2017 & 279.7900 & 283.2600 & 289.8800 & 281.0000 & 263.0200 & 234.4600 & 203.1300 & 175.7500 & 151.1100 & 142.1400 & 140.1300 & 152.2500\\\\\n",
       "\t2018 & 163.3900 & 172.4000 & 283.1300 & 332.2400 & 345.5300 & 338.0000 & 313.6800 & 288.2600 & 273.6800 & 264.8500 & 272.0900 & 275.9600\\\\\n",
       "\t2019 & 279.3100 & 297.3900 & 291.0100 & 311.9900 & 294.4900 & 262.6400 & 227.5300 & 192.8300 & 178.8800 & 168.5500 & 192.5800 & 254.8000\\\\\n",
       "\t2020 & 266.6600 & 272.4500 & 277.2000 & 292.3500 & 300.6500 & 274.6400 & 232.0100 & 187.8200 & 162.7600 & 158.4300 & 159.9700 & 198.1900\\\\\n",
       "\t2021 & 241.3100 & 296.3500 & 306.5400 &          &          &          &          &          &          &          &          &         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A Time Series: 13 × 12\n",
       "\n",
       "| <!--/--> | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2009 | 229.2731 | 279.5406 | 319.4200 | 336.4608 | 324.6378 | 275.8100 | 220.6600 | 166.8867 | 157.7400 | 156.9386 | 160.1100 | 211.5118 |\n",
       "| 2010 | 296.4729 | 383.0905 | 426.7000 | 436.6800 | 452.3600 | 449.4300 | 431.1043 | 411.2700 | 388.1300 | 382.2663 | 404.0000 | 424.7400 |\n",
       "| 2011 | 411.0000 | 434.6200 | 426.8100 | 440.3700 | 454.4500 | 440.6000 | 416.9800 | 390.1200 | 376.8900 | 372.2700 | 378.3300 | 382.6700 |\n",
       "| 2012 | 385.0800 | 385.6100 | 379.0800 | 386.8900 | 381.7700 | 356.9100 | 312.7100 | 272.4100 | 250.9100 | 253.1200 | 287.7100 | 314.5300 |\n",
       "| 2013 | 396.0500 | 438.0700 | 483.4400 | 463.1100 | 473.3700 | 465.5300 | 442.9000 | 419.9600 | 397.7500 | 381.7700 | 375.6600 | 380.8700 |\n",
       "| 2014 | 407.0900 | 426.9100 | 433.2900 | 445.5800 | 445.2400 | 423.0200 | 392.0500 | 365.4300 | 353.1000 | 350.3100 | 356.8200 | 362.7500 |\n",
       "| 2015 | 370.3400 | 416.3200 | 429.6500 | 436.2900 | 429.3900 | 405.9800 | 379.9000 | 352.7800 | 332.5300 | 323.8500 | 321.6100 | 314.7400 |\n",
       "| 2016 | 316.1600 | 350.0000 | 365.0200 | 387.1500 | 401.6300 | 379.6900 | 347.8500 | 315.7300 | 290.1300 | 275.9400 | 275.9500 | 278.8707 |\n",
       "| 2017 | 279.7900 | 283.2600 | 289.8800 | 281.0000 | 263.0200 | 234.4600 | 203.1300 | 175.7500 | 151.1100 | 142.1400 | 140.1300 | 152.2500 |\n",
       "| 2018 | 163.3900 | 172.4000 | 283.1300 | 332.2400 | 345.5300 | 338.0000 | 313.6800 | 288.2600 | 273.6800 | 264.8500 | 272.0900 | 275.9600 |\n",
       "| 2019 | 279.3100 | 297.3900 | 291.0100 | 311.9900 | 294.4900 | 262.6400 | 227.5300 | 192.8300 | 178.8800 | 168.5500 | 192.5800 | 254.8000 |\n",
       "| 2020 | 266.6600 | 272.4500 | 277.2000 | 292.3500 | 300.6500 | 274.6400 | 232.0100 | 187.8200 | 162.7600 | 158.4300 | 159.9700 | 198.1900 |\n",
       "| 2021 | 241.3100 | 296.3500 | 306.5400 | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> | <!----> |\n",
       "\n"
      ],
      "text/plain": [
       "     Jan      Feb      Mar      Apr      May      Jun      Jul      Aug     \n",
       "2009 229.2731 279.5406 319.4200 336.4608 324.6378 275.8100 220.6600 166.8867\n",
       "2010 296.4729 383.0905 426.7000 436.6800 452.3600 449.4300 431.1043 411.2700\n",
       "2011 411.0000 434.6200 426.8100 440.3700 454.4500 440.6000 416.9800 390.1200\n",
       "2012 385.0800 385.6100 379.0800 386.8900 381.7700 356.9100 312.7100 272.4100\n",
       "2013 396.0500 438.0700 483.4400 463.1100 473.3700 465.5300 442.9000 419.9600\n",
       "2014 407.0900 426.9100 433.2900 445.5800 445.2400 423.0200 392.0500 365.4300\n",
       "2015 370.3400 416.3200 429.6500 436.2900 429.3900 405.9800 379.9000 352.7800\n",
       "2016 316.1600 350.0000 365.0200 387.1500 401.6300 379.6900 347.8500 315.7300\n",
       "2017 279.7900 283.2600 289.8800 281.0000 263.0200 234.4600 203.1300 175.7500\n",
       "2018 163.3900 172.4000 283.1300 332.2400 345.5300 338.0000 313.6800 288.2600\n",
       "2019 279.3100 297.3900 291.0100 311.9900 294.4900 262.6400 227.5300 192.8300\n",
       "2020 266.6600 272.4500 277.2000 292.3500 300.6500 274.6400 232.0100 187.8200\n",
       "2021 241.3100 296.3500 306.5400                                             \n",
       "     Sep      Oct      Nov      Dec     \n",
       "2009 157.7400 156.9386 160.1100 211.5118\n",
       "2010 388.1300 382.2663 404.0000 424.7400\n",
       "2011 376.8900 372.2700 378.3300 382.6700\n",
       "2012 250.9100 253.1200 287.7100 314.5300\n",
       "2013 397.7500 381.7700 375.6600 380.8700\n",
       "2014 353.1000 350.3100 356.8200 362.7500\n",
       "2015 332.5300 323.8500 321.6100 314.7400\n",
       "2016 290.1300 275.9400 275.9500 278.8707\n",
       "2017 151.1100 142.1400 140.1300 152.2500\n",
       "2018 273.6800 264.8500 272.0900 275.9600\n",
       "2019 178.8800 168.5500 192.5800 254.8000\n",
       "2020 162.7600 158.4300 159.9700 198.1900\n",
       "2021                                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nSvkPrHO7PEMkOnYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rests00 <- ts(res_xts, start = c(2009,1),frequency = 12)\n",
    "rests00\n",
    "rests00.decomp <- decompose(rests00)\n",
    "plot(rests00.decomp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que la componente estacional tienen un escaso peso en relación a la componente de tendencia. Se observa que hay una clara tendencia decreciente desde el año 2013.\n",
    "\n",
    "Por otro lado, para hacer posteriomente predicciones, es relevante conocer el grado de autocorrelación de la serie ya permite determinar si la serie es estacionaria o no. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\nQmCC",
      "text/plain": [
       "Plot with title \"reserva\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vemos si existe autocorrelaciones en los datos\n",
    "acf(rests00,lag.max=75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los lags en el eje x de la gráfica se refieren a unidades relativas de tiempo no a número de observaciones. En este caso los datos son mensuales por lo que, como se observa, en un año habrá 12 mediciones. A efectos esto se traduce en que el los lags están expresados en años.\n",
    "\n",
    "Se observa que existe una correlación marcada. Por tanto la serie a priori parece no ser estacionaria. Esta cuestión se analizará con mas detalle posteriormente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Modelos de predicción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Datos de entrenamiento y datos test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comprobar la efectividad de los modelos de predicción que se van a desarrollar se a tomar como serie de entrenamiento la comprendida entre 2009 y 2019, dejando el año 2020 como serie de comprobación del ajuste de los modelos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Serie de entrenamiento\n",
    "rests <- window(rests00, c(2009,1),c(2019,12))\n",
    "\n",
    "# Serie para evaluar el ajuste \n",
    "rests.check <- window(rests00, c(2020,1),c(2020,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 - Análisis mediante suavizado exponencial.\n",
    "\n",
    "https://en.wikipedia.org/wiki/Exponential_smoothing\n",
    "\n",
    "En el siguiente ejemplo se aplica el método de Holt-Winters que consiste concretamente en un triple alisado exponencial:\n",
    "\n",
    "* Primer alisado: Incluye el efecto del dato previo de la serie.\n",
    "* Segundo aliasado: Incluye el efecto de la tendencia de serie.\n",
    "* Tercer alisado: Incluye el efecto de la estacionalidad de la serie.\n",
    "\n",
    "Esto deriva en que es necesario determinar tres parámetros a partir de los datos de la series (alfa, beta y gamma)\n",
    "\n",
    "#### 5.2.1 Análisis paso a paso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Holt-Winters exponential smoothing with trend and additive seasonal component.\n",
       "\n",
       "Call:\n",
       "HoltWinters(x = rests)\n",
       "\n",
       "Smoothing parameters:\n",
       " alpha: 0.818106\n",
       " beta : 1\n",
       " gamma: 0.9666872\n",
       "\n",
       "Coefficients:\n",
       "           [,1]\n",
       "a    339.375742\n",
       "b     22.136813\n",
       "s1   -34.675332\n",
       "s2    46.887677\n",
       "s3   133.456477\n",
       "s4   162.292106\n",
       "s5   123.069810\n",
       "s6    66.123325\n",
       "s7     6.169208\n",
       "s8   -52.356206\n",
       "s9  -102.981348\n",
       "s10 -137.176009\n",
       "s11 -124.565372\n",
       "s12  -84.655116"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Se aplica el alisado exponencial de HoltWinters\n",
    "(rests.hw <- HoltWinters(rests))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A Time Series: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>xhat</th><th scope=col>level</th><th scope=col>trend</th><th scope=col>season</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Jan 2010</th><td>204.9729</td><td>207.0242</td><td>13.98831</td><td>-16.03952</td></tr>\n",
       "\t<tr><th scope=row>Feb 2010</th><td>358.5237</td><td>270.1565</td><td>63.13234</td><td> 25.23491</td></tr>\n",
       "\t<tr><th scope=row>Mar 2010</th><td>441.1228</td><td>303.7748</td><td>33.61831</td><td>103.72972</td></tr>\n",
       "\t<tr><th scope=row>Apr 2010</th><td>429.9396</td><td>320.6407</td><td>16.86595</td><td> 92.43290</td></tr>\n",
       "\t<tr><th scope=row>May 2010</th><td>441.9375</td><td>336.7500</td><td>16.10921</td><td> 89.07831</td></tr>\n",
       "\t<tr><th scope=row>Jun 2010</th><td>450.5726</td><td>355.2682</td><td>18.51825</td><td> 76.78609</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A Time Series: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & xhat & level & trend & season\\\\\n",
       "\\hline\n",
       "\tJan 2010 & 204.9729 & 207.0242 & 13.98831 & -16.03952\\\\\n",
       "\tFeb 2010 & 358.5237 & 270.1565 & 63.13234 &  25.23491\\\\\n",
       "\tMar 2010 & 441.1228 & 303.7748 & 33.61831 & 103.72972\\\\\n",
       "\tApr 2010 & 429.9396 & 320.6407 & 16.86595 &  92.43290\\\\\n",
       "\tMay 2010 & 441.9375 & 336.7500 & 16.10921 &  89.07831\\\\\n",
       "\tJun 2010 & 450.5726 & 355.2682 & 18.51825 &  76.78609\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A Time Series: 6 × 4\n",
       "\n",
       "| <!--/--> | xhat | level | trend | season |\n",
       "|---|---|---|---|---|\n",
       "| Jan 2010 | 204.9729 | 207.0242 | 13.98831 | -16.03952 |\n",
       "| Feb 2010 | 358.5237 | 270.1565 | 63.13234 |  25.23491 |\n",
       "| Mar 2010 | 441.1228 | 303.7748 | 33.61831 | 103.72972 |\n",
       "| Apr 2010 | 429.9396 | 320.6407 | 16.86595 |  92.43290 |\n",
       "| May 2010 | 441.9375 | 336.7500 | 16.10921 |  89.07831 |\n",
       "| Jun 2010 | 450.5726 | 355.2682 | 18.51825 |  76.78609 |\n",
       "\n"
      ],
      "text/plain": [
       "         xhat     level    trend    season   \n",
       "Jan 2010 204.9729 207.0242 13.98831 -16.03952\n",
       "Feb 2010 358.5237 270.1565 63.13234  25.23491\n",
       "Mar 2010 441.1228 303.7748 33.61831 103.72972\n",
       "Apr 2010 429.9396 320.6407 16.86595  92.43290\n",
       "May 2010 441.9375 336.7500 16.10921  89.07831\n",
       "Jun 2010 450.5726 355.2682 18.51825  76.78609"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vemos algunos de los valores ajustados.\n",
    "head(rests.hw$fitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "88470.0774516964"
      ],
      "text/latex": [
       "88470.0774516964"
      ],
      "text/markdown": [
       "88470.0774516964"
      ],
      "text/plain": [
       "[1] 88470.08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Suma total del error cuadratico\n",
    "rests.hw$SSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAANlBMVEUAAAAAAP9NTU1oaGh8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\nBA0AECgIGgAgUBA0AECg/AfCh9W8J/4yrQAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Embalse Tranco de Beas. Reserva mensual 2009-2019\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafica serie inicial vs serie suavizada\n",
    "plot(rests,\n",
    "     xlab = \"Fecha\",\n",
    "     ylab = \"Reserva hm3\",\n",
    "     ylim = c(0.99*min(rests), 1.01*max(rests)),\n",
    "     col = 'blue')\n",
    "lines(rests.hw$fitted[,1], col=\"red\")\n",
    "legend(\"topleft\",\n",
    "       legend=c(\"Serie original \",\"Serie suavizada\"),\n",
    "       col=c(\"blue\",\"red\"),lwd=2)\n",
    "title(main = 'Embalse Tranco de Beas. Reserva mensual 2009-2019',\n",
    "      sub = 'Suavizado exponencial (Holt-Winters)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 12 of type dbl</caption>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>fit</th><td>326.8372</td><td>430.537</td><td>539.2427</td><td>590.2151</td><td>573.1296</td><td>538.3199</td><td>500.5026</td><td>464.114</td><td>435.6257</td><td>423.5679</td><td>458.3153</td><td>520.3624</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 12 of type dbl\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "\tfit & 326.8372 & 430.537 & 539.2427 & 590.2151 & 573.1296 & 538.3199 & 500.5026 & 464.114 & 435.6257 & 423.5679 & 458.3153 & 520.3624\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 12 of type dbl\n",
       "\n",
       "| fit | 326.8372 | 430.537 | 539.2427 | 590.2151 | 573.1296 | 538.3199 | 500.5026 | 464.114 | 435.6257 | 423.5679 | 458.3153 | 520.3624 |\n",
       "\n"
      ],
      "text/plain": [
       "    [,1]     [,2]    [,3]     [,4]     [,5]     [,6]     [,7]     [,8]   \n",
       "fit 326.8372 430.537 539.2427 590.2151 573.1296 538.3199 500.5026 464.114\n",
       "    [,9]     [,10]    [,11]    [,12]   \n",
       "fit 435.6257 423.5679 458.3153 520.3624"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAOVBMVEUAAAAAAP8AZABNTU1o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\nteVrUvkAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Embalse Tranco de Beas. Predicción de reserva 2020 \""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predicción para 2020\n",
    "rests.pred2020 <- predict(rests.hw, n.ahead=12)\n",
    "t(as.data.frame(rests.pred2020))\n",
    "\n",
    "plot(rests,\n",
    "     xlim = c(2009, 2021),\n",
    "     ylim = c(0.99*min(rests), 1.20*max(rests)),\n",
    "     main = \"Embalse Tranco de Beas. Predicción de reserva 2020 \",\n",
    "     xlab = \"Fecha\",\n",
    "     ylab = \"Reserva hm3\",\n",
    "     col = 'blue')\n",
    "lines(rests.pred2020, col = 'red')\n",
    "lines(rests.check, col = 'dark green')\n",
    "legend(\"topleft\",\n",
    "       legend=c(\"serie hasta 2019\",\"prediccion 2020\",'datos reales 2020'),\n",
    "       col=c(\"blue\",\"red\",'dark green'),lwd=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.2 - Análisis con el librería forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         Point Forecast       Lo 80     Hi 80      Lo 95     Hi 95\n",
       "Jan 2020       326.8372  287.986570  365.6879  267.42028  386.2542\n",
       "Feb 2020       430.5370  356.037032  505.0371  316.59911  544.4750\n",
       "Mar 2020       539.2427  418.237581  660.2477  354.18136  724.3040\n",
       "Apr 2020       590.2151  414.699450  765.7307  321.78708  858.6431\n",
       "May 2020       573.1296  336.357078  809.9022  211.01727  935.2420\n",
       "Jun 2020       538.3199  234.298329  842.3416   73.35901 1003.2809\n",
       "Jul 2020       500.5026  123.766784  877.2385  -75.66513 1076.6704\n",
       "Aug 2020       464.1140    9.598952  918.6291 -231.00680 1159.2349\n",
       "Sep 2020       435.6257 -101.414098  972.6655 -385.70580 1256.9572\n",
       "Oct 2020       423.5679 -200.478388 1047.6141 -530.82852 1377.9642\n",
       "Nov 2020       458.3153 -256.996019 1173.6266 -635.65896 1552.2896\n",
       "Dec 2020       520.3624 -290.280531 1331.0053 -719.40897 1760.1337"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAP1BMVEUAAAAAAP8il+ZNTU1o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",
      "text/plain": [
       "Plot with title \"Forecasts from HoltWinters\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# con la libreria forecast\n",
    "library(forecast)\n",
    "(rests.pred2020b <- forecast(rests.hw, h = 12))\n",
    "plot(rests.pred2020b)\n",
    "lines(rests.check, col = 'red')\n",
    "legend(\"topleft\",\n",
    "       legend=c(\"serie hasta 2019\",\"prediccion 2020\",'datos reales 2020'),\n",
    "       col=c(\"black\",\"blue\",'red'), lwd = 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se observa las predicciones, aunque captan algo del comportamiento histórico de la serie, difieren de forma relevante respecto de los valores reales.\n",
    "\n",
    "Por tanto,este modelo no se adapta de forma efectiva a la serie de datos que tratamos de analizar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluación del modelo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\naICiBBqgKIEGKEqgAYoSaICiBBqgqP8H70E5sVqPuC0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"x\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Función de autocorrelación de los residuos de las predicciones\n",
    "acf(rests.pred2020b$residuals, na.action = na.pass, lag.max=75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que la autocorrelación disminuye progresivamente y, aunque con oscilaciones, parecen converger a cero. No  obstante esta lentitud en la convergencia es un indicio de que las predicciones pueden ser mejoradas con otros modelos.\n",
    "\n",
    "A continuación se indican varios índices para la medida del error cometido en la predicción. Todos estos indices apuntan a que las predicciones no son muy buenas. En concreto el [Indice U de Teil](https://www.economicsnetwork.ac.uk/showcase/cook_theil) supera con creces el valor 1 que es indicativo de una desigualdad noteble entre la serie original y la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Test.set</th><th scope=col>description</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ME</th><td>-251.4699611</td><td>Error medio                             </td></tr>\n",
       "\t<tr><th scope=row>RMSE</th><td> 260.7298225</td><td>Raíz del error cuadrático medio         </td></tr>\n",
       "\t<tr><th scope=row>MAE</th><td> 251.4699611</td><td>Error medio absoluto                    </td></tr>\n",
       "\t<tr><th scope=row>MPE</th><td>-117.5450097</td><td>Porcentaje de error medio               </td></tr>\n",
       "\t<tr><th scope=row>MAPE</th><td> 117.5450097</td><td>Porcentaje de error medio absoluto      </td></tr>\n",
       "\t<tr><th scope=row>ACF1</th><td>   0.4218031</td><td>Autocorrelación de errores con retraso 1</td></tr>\n",
       "\t<tr><th scope=row>Theil's U</th><td>  11.2692416</td><td>Índice U de desigualdad de Theils       </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & Test.set & description\\\\\n",
       "  & <dbl> & <chr>\\\\\n",
       "\\hline\n",
       "\tME & -251.4699611 & Error medio                             \\\\\n",
       "\tRMSE &  260.7298225 & Raíz del error cuadrático medio         \\\\\n",
       "\tMAE &  251.4699611 & Error medio absoluto                    \\\\\n",
       "\tMPE & -117.5450097 & Porcentaje de error medio               \\\\\n",
       "\tMAPE &  117.5450097 & Porcentaje de error medio absoluto      \\\\\n",
       "\tACF1 &    0.4218031 & Autocorrelación de errores con retraso 1\\\\\n",
       "\tTheil's U &   11.2692416 & Índice U de desigualdad de Theils       \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 2\n",
       "\n",
       "| <!--/--> | Test.set &lt;dbl&gt; | description &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| ME | -251.4699611 | Error medio                              |\n",
       "| RMSE |  260.7298225 | Raíz del error cuadrático medio          |\n",
       "| MAE |  251.4699611 | Error medio absoluto                     |\n",
       "| MPE | -117.5450097 | Porcentaje de error medio                |\n",
       "| MAPE |  117.5450097 | Porcentaje de error medio absoluto       |\n",
       "| ACF1 |    0.4218031 | Autocorrelación de errores con retraso 1 |\n",
       "| Theil's U |   11.2692416 | Índice U de desigualdad de Theils        |\n",
       "\n"
      ],
      "text/plain": [
       "          Test.set     description                             \n",
       "ME        -251.4699611 Error medio                             \n",
       "RMSE       260.7298225 Raíz del error cuadrático medio         \n",
       "MAE        251.4699611 Error medio absoluto                    \n",
       "MPE       -117.5450097 Porcentaje de error medio               \n",
       "MAPE       117.5450097 Porcentaje de error medio absoluto      \n",
       "ACF1         0.4218031 Autocorrelación de errores con retraso 1\n",
       "Theil's U   11.2692416 Índice U de desigualdad de Theils       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Error en la predicción de 2020\n",
    "\n",
    "errors <- data.frame(t(accuracy(rests.pred2020b$mean , rests.check)))\n",
    "\n",
    "errors$description <- c('Error medio', \n",
    "                        'Raíz del error cuadrático medio',\n",
    "                        'Error medio absoluto',\n",
    "                        'Porcentaje de error medio',\n",
    "                        'Porcentaje de error medio absoluto',\n",
    "                        'Autocorrelación de errores con retraso 1',\n",
    "                        'Índice U de desigualdad de Theils')\n",
    "errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 - Análisis mediante un modelo ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3.1 - Análisis de estacionariedad y transformaciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La estacionariedad de una serie temporal (media y varianza constantes) es una cualidad que facilita la creación de modelos de predicción al mantenerse constante la distribución de los sus datos.\n",
    "\n",
    "El gráfico de autocorrelaciones o correlograma permite estimar visualmente la estacionariedad de la serie. Es de esperar que una serie temporal estacionaria el correlograma caiga de forma más menos rápida a cero y se mantenga en sus proximidades.\n",
    "\n",
    "De forma más rígurosa es necesario hacer un contraste o test de hipótesis. En este caso se va a aplicar el test de la raíz unitaria o [Test Dickey-Fuller aumentado](https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test) para confirmarlo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tAugmented Dickey-Fuller Test\n",
       "\n",
       "data:  rests\n",
       "Dickey-Fuller = -3.0717, Lag order = 5, p-value = 0.1309\n",
       "alternative hypothesis: stationary\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(tseries)\n",
    "adf.test(rests)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-valor > 0.05. Por tanto se desecha la hipoótesis alternativa, que considera que la serie es estacionaria. Por tanto se confirma nuestra primera suposición."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos adicionalmento verificar la condición de varianza constante (homocestadicidad) mediante el cálculo del parámetro lambda en la [transformación de Box-Cox](https://es.wikipedia.org/wiki/Transformaci%C3%B3n_Box-Cox)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.82428986469775"
      ],
      "text/latex": [
       "1.82428986469775"
      ],
      "text/markdown": [
       "1.82428986469775"
      ],
      "text/plain": [
       "[1] 1.82429"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vemos si se cumple la condición de homocestaticidad. (Constancia de la varianza)\n",
    "lambda <- BoxCox.lambda(rests, lower = 0, upper = 2)\n",
    "lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El  valor de lambda es mayor que 1 por tanto es necesario aplicar la transformación de Box-Cox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A Time Series: 11 × 12</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Jan</th><th scope=col>Feb</th><th scope=col>Mar</th><th scope=col>Apr</th><th scope=col>May</th><th scope=col>Jun</th><th scope=col>Jul</th><th scope=col>Aug</th><th scope=col>Sep</th><th scope=col>Oct</th><th scope=col>Nov</th><th scope=col>Dec</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>2009</th><td>11087.992</td><td>15919.002</td><td>20303.777</td><td>22323.205</td><td>20912.923</td><td>15533.558</td><td>10339.853</td><td> 6211.681</td><td> 5604.627</td><td> 5552.785</td><td> 5759.212</td><td> 9571.186</td></tr>\n",
       "\t<tr><th scope=row>2010</th><td>17721.888</td><td>28287.088</td><td>34435.415</td><td>35918.871</td><td>38306.533</td><td>37855.098</td><td>35086.601</td><td>32197.653</td><td>28969.622</td><td>28176.168</td><td>31166.901</td><td>34147.399</td></tr>\n",
       "\t<tr><th scope=row>2011</th><td>32159.101</td><td>35610.352</td><td>34451.612</td><td>36474.514</td><td>38630.023</td><td>36509.275</td><td>33017.835</td><td>29241.164</td><td>27457.418</td><td>26846.492</td><td>27649.105</td><td>28230.471</td></tr>\n",
       "\t<tr><th scope=row>2012</th><td>28555.662</td><td>28627.403</td><td>27749.181</td><td>28800.999</td><td>28109.462</td><td>24860.140</td><td>19532.409</td><td>15185.993</td><td>13070.863</td><td>13281.659</td><td>16777.937</td><td>19740.298</td></tr>\n",
       "\t<tr><th scope=row>2013</th><td>30057.113</td><td>36127.726</td><td>43243.378</td><td>39983.499</td><td>41614.244</td><td>40365.483</td><td>36857.708</td><td>33449.580</td><td>30292.897</td><td>28109.462</td><td>27294.162</td><td>27988.688</td></tr>\n",
       "\t<tr><th scope=row>2014</th><td>31603.154</td><td>34466.339</td><td>35411.802</td><td>37265.594</td><td>37213.735</td><td>33895.551</td><td>29505.612</td><td>25953.427</td><td>24378.129</td><td>24027.867</td><td>24848.705</td><td>25607.238</td></tr>\n",
       "\t<tr><th scope=row>2015</th><td>26593.119</td><td>32922.557</td><td>34870.968</td><td>35860.370</td><td>34832.481</td><td>31446.127</td><td>27858.784</td><td>24337.839</td><td>21849.711</td><td>20820.428</td><td>20558.454</td><td>19764.349</td></tr>\n",
       "\t<tr><th scope=row>2016</th><td>19927.328</td><td>23989.090</td><td>25900.329</td><td>28836.318</td><td>30834.157</td><td>27830.696</td><td>23720.935</td><td>19877.911</td><td>17036.288</td><td>15546.917</td><td>15547.945</td><td>15849.469</td></tr>\n",
       "\t<tr><th scope=row>2017</th><td>15944.922</td><td>16307.533</td><td>17009.516</td><td>16070.947</td><td>14244.606</td><td>11549.893</td><td> 8890.542</td><td> 6826.704</td><td> 5182.302</td><td> 4634.825</td><td> 4515.943</td><td> 5253.854</td></tr>\n",
       "\t<tr><th scope=row>2018</th><td> 5976.283</td><td> 6591.167</td><td>16293.881</td><td>21814.960</td><td>23433.105</td><td>22509.854</td><td>19643.083</td><td>16836.497</td><td>15315.403</td><td>14425.934</td><td>15153.464</td><td>15548.973</td></tr>\n",
       "\t<tr><th scope=row>2019</th><td>15895.052</td><td>17822.023</td><td>17130.676</td><td>19450.442</td><td>17506.242</td><td>14207.083</td><td>10934.681</td><td> 8085.327</td><td> 7050.145</td><td> 6325.097</td><td> 8066.213</td><td>13442.922</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A Time Series: 11 × 12\n",
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & Jan & Feb & Mar & Apr & May & Jun & Jul & Aug & Sep & Oct & Nov & Dec\\\\\n",
       "\\hline\n",
       "\t2009 & 11087.992 & 15919.002 & 20303.777 & 22323.205 & 20912.923 & 15533.558 & 10339.853 &  6211.681 &  5604.627 &  5552.785 &  5759.212 &  9571.186\\\\\n",
       "\t2010 & 17721.888 & 28287.088 & 34435.415 & 35918.871 & 38306.533 & 37855.098 & 35086.601 & 32197.653 & 28969.622 & 28176.168 & 31166.901 & 34147.399\\\\\n",
       "\t2011 & 32159.101 & 35610.352 & 34451.612 & 36474.514 & 38630.023 & 36509.275 & 33017.835 & 29241.164 & 27457.418 & 26846.492 & 27649.105 & 28230.471\\\\\n",
       "\t2012 & 28555.662 & 28627.403 & 27749.181 & 28800.999 & 28109.462 & 24860.140 & 19532.409 & 15185.993 & 13070.863 & 13281.659 & 16777.937 & 19740.298\\\\\n",
       "\t2013 & 30057.113 & 36127.726 & 43243.378 & 39983.499 & 41614.244 & 40365.483 & 36857.708 & 33449.580 & 30292.897 & 28109.462 & 27294.162 & 27988.688\\\\\n",
       "\t2014 & 31603.154 & 34466.339 & 35411.802 & 37265.594 & 37213.735 & 33895.551 & 29505.612 & 25953.427 & 24378.129 & 24027.867 & 24848.705 & 25607.238\\\\\n",
       "\t2015 & 26593.119 & 32922.557 & 34870.968 & 35860.370 & 34832.481 & 31446.127 & 27858.784 & 24337.839 & 21849.711 & 20820.428 & 20558.454 & 19764.349\\\\\n",
       "\t2016 & 19927.328 & 23989.090 & 25900.329 & 28836.318 & 30834.157 & 27830.696 & 23720.935 & 19877.911 & 17036.288 & 15546.917 & 15547.945 & 15849.469\\\\\n",
       "\t2017 & 15944.922 & 16307.533 & 17009.516 & 16070.947 & 14244.606 & 11549.893 &  8890.542 &  6826.704 &  5182.302 &  4634.825 &  4515.943 &  5253.854\\\\\n",
       "\t2018 &  5976.283 &  6591.167 & 16293.881 & 21814.960 & 23433.105 & 22509.854 & 19643.083 & 16836.497 & 15315.403 & 14425.934 & 15153.464 & 15548.973\\\\\n",
       "\t2019 & 15895.052 & 17822.023 & 17130.676 & 19450.442 & 17506.242 & 14207.083 & 10934.681 &  8085.327 &  7050.145 &  6325.097 &  8066.213 & 13442.922\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A Time Series: 11 × 12\n",
       "\n",
       "| <!--/--> | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2009 | 11087.992 | 15919.002 | 20303.777 | 22323.205 | 20912.923 | 15533.558 | 10339.853 |  6211.681 |  5604.627 |  5552.785 |  5759.212 |  9571.186 |\n",
       "| 2010 | 17721.888 | 28287.088 | 34435.415 | 35918.871 | 38306.533 | 37855.098 | 35086.601 | 32197.653 | 28969.622 | 28176.168 | 31166.901 | 34147.399 |\n",
       "| 2011 | 32159.101 | 35610.352 | 34451.612 | 36474.514 | 38630.023 | 36509.275 | 33017.835 | 29241.164 | 27457.418 | 26846.492 | 27649.105 | 28230.471 |\n",
       "| 2012 | 28555.662 | 28627.403 | 27749.181 | 28800.999 | 28109.462 | 24860.140 | 19532.409 | 15185.993 | 13070.863 | 13281.659 | 16777.937 | 19740.298 |\n",
       "| 2013 | 30057.113 | 36127.726 | 43243.378 | 39983.499 | 41614.244 | 40365.483 | 36857.708 | 33449.580 | 30292.897 | 28109.462 | 27294.162 | 27988.688 |\n",
       "| 2014 | 31603.154 | 34466.339 | 35411.802 | 37265.594 | 37213.735 | 33895.551 | 29505.612 | 25953.427 | 24378.129 | 24027.867 | 24848.705 | 25607.238 |\n",
       "| 2015 | 26593.119 | 32922.557 | 34870.968 | 35860.370 | 34832.481 | 31446.127 | 27858.784 | 24337.839 | 21849.711 | 20820.428 | 20558.454 | 19764.349 |\n",
       "| 2016 | 19927.328 | 23989.090 | 25900.329 | 28836.318 | 30834.157 | 27830.696 | 23720.935 | 19877.911 | 17036.288 | 15546.917 | 15547.945 | 15849.469 |\n",
       "| 2017 | 15944.922 | 16307.533 | 17009.516 | 16070.947 | 14244.606 | 11549.893 |  8890.542 |  6826.704 |  5182.302 |  4634.825 |  4515.943 |  5253.854 |\n",
       "| 2018 |  5976.283 |  6591.167 | 16293.881 | 21814.960 | 23433.105 | 22509.854 | 19643.083 | 16836.497 | 15315.403 | 14425.934 | 15153.464 | 15548.973 |\n",
       "| 2019 | 15895.052 | 17822.023 | 17130.676 | 19450.442 | 17506.242 | 14207.083 | 10934.681 |  8085.327 |  7050.145 |  6325.097 |  8066.213 | 13442.922 |\n",
       "\n"
      ],
      "text/plain": [
       "     Jan       Feb       Mar       Apr       May       Jun       Jul      \n",
       "2009 11087.992 15919.002 20303.777 22323.205 20912.923 15533.558 10339.853\n",
       "2010 17721.888 28287.088 34435.415 35918.871 38306.533 37855.098 35086.601\n",
       "2011 32159.101 35610.352 34451.612 36474.514 38630.023 36509.275 33017.835\n",
       "2012 28555.662 28627.403 27749.181 28800.999 28109.462 24860.140 19532.409\n",
       "2013 30057.113 36127.726 43243.378 39983.499 41614.244 40365.483 36857.708\n",
       "2014 31603.154 34466.339 35411.802 37265.594 37213.735 33895.551 29505.612\n",
       "2015 26593.119 32922.557 34870.968 35860.370 34832.481 31446.127 27858.784\n",
       "2016 19927.328 23989.090 25900.329 28836.318 30834.157 27830.696 23720.935\n",
       "2017 15944.922 16307.533 17009.516 16070.947 14244.606 11549.893  8890.542\n",
       "2018  5976.283  6591.167 16293.881 21814.960 23433.105 22509.854 19643.083\n",
       "2019 15895.052 17822.023 17130.676 19450.442 17506.242 14207.083 10934.681\n",
       "     Aug       Sep       Oct       Nov       Dec      \n",
       "2009  6211.681  5604.627  5552.785  5759.212  9571.186\n",
       "2010 32197.653 28969.622 28176.168 31166.901 34147.399\n",
       "2011 29241.164 27457.418 26846.492 27649.105 28230.471\n",
       "2012 15185.993 13070.863 13281.659 16777.937 19740.298\n",
       "2013 33449.580 30292.897 28109.462 27294.162 27988.688\n",
       "2014 25953.427 24378.129 24027.867 24848.705 25607.238\n",
       "2015 24337.839 21849.711 20820.428 20558.454 19764.349\n",
       "2016 19877.911 17036.288 15546.917 15547.945 15849.469\n",
       "2017  6826.704  5182.302  4634.825  4515.943  5253.854\n",
       "2018 16836.497 15315.403 14425.934 15153.464 15548.973\n",
       "2019  8085.327  7050.145  6325.097  8066.213 13442.922"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1.00423537947491"
      ],
      "text/latex": [
       "1.00423537947491"
      ],
      "text/markdown": [
       "1.00423537947491"
      ],
      "text/plain": [
       "[1] 1.004235"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aplicamos la transformación de Box-Cox\n",
    "rests.BC <- BoxCox(rests, lambda)\n",
    "rests.BC\n",
    "BoxCox.lambda(rests.BC, lower = 0, upper = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAMFBMVEUAAABNTU1oaGh8fHyM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",
      "text/plain": [
       "Plot with title \"Reserva del Embalse Tranco de Beas.Transformación Box-Cox\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(rests.BC,\n",
    "     main = \"Reserva del Embalse Tranco de Beas.Transformación Box-Cox\",\n",
    "     xlab = \"Fecha\",\n",
    "     ylab = \"Reserva (Transformación  BoxCox)\",\n",
    "     ylim = c(0.99*min(rests.BC), 1.01*max(rests.BC)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comprobamos si tras la transformación Box-Cox la serie tiene varianza constante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\nUgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"FAS de Box-Cox(Reserva)\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estacionariedad en las medias\n",
    "acf(rests.BC, main = \"FAS de Box-Cox(Reserva)\", lag.max =75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tAugmented Dickey-Fuller Test\n",
       "\n",
       "data:  rests.BC\n",
       "Dickey-Fuller = -3.2374, Lag order = 5, p-value = 0.08474\n",
       "alternative hypothesis: stationary\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Probamos el test de raíz unitaria con los datos transformados\n",
    "adf.test(rests.BC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que la serie sigue sin ser estacionaria.(p-vlue > 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\nKEpBAxSloAGKUtAARSlogKL+D1PCxvT1gmhvAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"FAS de la primera diferencia de Box.Cox(Reserva)\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Probamos con la diferenciación de primer orden\n",
    "rests.d1 <- diff(rests.BC, lag = 1, differences = 1)\n",
    "acf(rests.d1, main=\"FAS de la primera diferencia de Box.Cox(Reserva)\", lag.max = 75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hemos visto anteriormente que la serie tiene estacionalidad, cosa que queda clara en la autocorrelación estacional que muestra la serie. Por tanto este efecto tenemos eliminarlo de la serie de diferencias de orden 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8\nfHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD////UNI3wAAAACXBIWXMAABJ0\nAAASdAHeZh94AAAgAElEQVR4nO3d52KiahRAUS7Wsb//017BBsaCSjnAWj8mmogfKO4wBDE5\nABBS0vUMAPCYQAMEJdAAQQk0QFAC3TdJwflbi+PFxe0W63maJJPF9vFk038fDbeZT5Ikna0q\n3HSbJLPiSLPPRnpvliTb51eP5jUP+Nz5wb88B8vjI/774Ldn9Jeb1DPp7PuBqJFnoW8eBLp4\n+ZTr3PLJZItDdbPLROmmym235ZGmH4xUwfk3wJOrh03a3tpcDvQyW9hBBXr9w0DUyLPQN38D\nvcovX7Zx/91+vHkyWZXN4ZNpYartm9seezm5H6nmbehJeS7urrbZlHKgJ+8fnQ/u9Meb1DHp\nOmnzweQ5z0Lf/H3lHCu6uG2tHmOx2B8Ou2l5+/Iy2X5x7mgVx23i9F92Z1n1Z29uvDiX/6uR\nqlmVN//vrnYQ6NZHbiXQu1ki0EF4Fvrmzytnd6xoluVd+ef7Zw25XlrPs/+Yr8/3cervtLjd\nfdwmTs/3up0sd28mSs/3+3ek/SJN0sVp+v0y2yq/7NMu/uT47d0kWWwuOwvmp1lZZbWYXG+T\nlh+LtHD5UpXzHd1NWx64PIOF7eH18Vbz8+ZwaejLoz1Pk8m/uy3o6yI/WJ6/3y2NsV8cn7rp\nujQ778ct3eVV4bkpDv9i0vu5uU46FegYPAt98+eVs8j2Ni+vG5PH1/vswf7iYjZPVbvsvsgj\n+++0y3pR2pW6+LuP4vlEm0uu/wR6l972uFwunzb4iz/Jbp21qhD6tDDg6Taz8n6b4tVSoPM7\nKk1bHvjuQbl1dlHYnVMe+mRzvpNimW8jP1yeP98tjnH52aIwGxXGLd3lw+fmNvyrSe/m5vqw\nzFr97wjPeRb65s8r5/iC22fby+eNyXwfdDpf7R5Ptpufa3D989/pBX18/a6zfQbFLdTpdbP8\n4sVE/y41L440O89gLrvVPN8Psp+eblz8yblzq6z32UbgOp/P471O9/nvgMuvhOKvjOLVWyYv\nd1Satjzw3YNyy+3Z/PBn6OuDndxGug/0w+V59N3LGLf7W19no8q4pbt8/Nz8Gf7RpHdzc3ne\n1+3uL+I5z0LfJOXXWxaybKNwdnqNH24v1Mn6yWTp/jRZ8u8Y9uU5DtmOkqztd39YLI/9aqLr\nQW+FkfIfnIKzPxUyOTV/n++eLv0k/491NmfbUy1Oezguu27Os/LyOI7kltv8jkrTlgZ+MMX5\nS7rOR07uJz9bnW6xTpNCoK9fHi/Pn+8Wxjj+LN3mvzYm17upMG75Lh8/N5fhX05anpsigY7B\ns9A394GeXTc4L61aT84/XzyebJa9bueXTcnLXo1/fzak/r5IX0103QteGGl9nsH96fuz0/bb\nZS9p+SeHy+2ze9rflfS6t6T83d2z3N7/brob+MEUhQmf7r2//RpcJ48C/Xh5Hn73PMb5/van\nPfyVxy3f5cn9c1MY/vmkj5f4fnS641nom7tAX/dtpOdXXma3muf/k/33cLJ8Ezq53Hx3eSlm\nWU+Lt/37In0z0d1Iy/3dyOn5iOFLKks/ud35Mtu6W12P496tFtPrr6O7WSpdLdzm8kjcpi0N\n/GCKB1fvhr5fyIdfHizPn+8W7uBJkt+NW77L609Kz01h+OeTPnlYH36HLngW+ubulVM47Ln8\nF73sWKkHm5eH7TTftL7dzeVSlrDl5Tt5Hv4c3vtmosKPsqP80j9b1Ifb22iyH5Z/cstTttPm\nsv97NSncpHKgT98oTVsc+MkUd1fvhv67kM8DnZTv7fF3/yxO5XHLd3n/UDwa6MmkTx7Wh9+h\nC56Fvrl75UwKr7isx4UN6Yf1yi+m5Q2ufDNsn21zp8Vtvr9HcbyZqDTS9HLARFqe4f3qdLTB\n9P4ntzk83uDyrpfsTTiT+b/tN4G+m7Yw8JMpylfvh360kOUvj5fn8XefB7rCuHcP6eUnpefm\nSaBfzc3hwU/olmehb8qvnE1StMl3RZ73I++SR/W6XJzd704+/W2xtA96c9ve3OR7SV9NdN+u\nLAWr823u9/2u55d5KPzkNofHRF1Gmpxv8k2g76e9DVyYYn9+EP/ewYPJr3O8ehjox8vz+Lun\nr9MH+6ArjPvgIf3z3NwP/3BSgY7Os9A35VfOovQe78Xpb0Hz7NW+Lp+/5zpZdvTb9O8f/bMY\nb9O7I2uv7yTMbjV7PdG08EfC/OvmvJW9Op3IY5UPO7nuGU3vflJYsH2e/eJ2+WVzclc+imP3\n5yiO/eFPdU7Tlga+SPOHbHN3TEZ567a0JfvvdNTD6vFRHI+X5/F3T18fHcVRYdzyXZ48OIrj\n/aQCHZ1noW/+/K/4skfjdmDAVXl36032Ar6dZuNyQO42e5GX3py9LxxDm29Lv5hofon7dQYv\n23TXO9mcDgrcHS7vqyn+pLhg2XbuKT2nHebXw9o25W388tXsRovCHZWmLQ9cGOesOOfXrdvi\n0Lcx/k5xucnj5Xn43eS6V+Tk30fjlu7y7O65+Tv8g0kTgQ7Os9A3pVfOqlio2Wlr+vpCTdfl\nya5OibrcLr+D5SkRy7tz4O2vdzbZvZnozxtV8n0sl0Our8Ne/laX97f0k8KCZd8//cfgsgsn\nvR5T/eyNKresX+6oPG1p4MNtDpPTm+mKc3D6ejf0oXifs+RhoB8vz8Pvnr9u/r6TsMq4pbu8\nKD03t+FfTZoIdHCehb4pvXKmxT2K60v28mPsZsv93WRns8sU+e3uTqsxu3/z4Onk0rej055O\ntP37Vu/Lm+Hy801chs13A19OSl38SXHBkut/DLbz7H2R2/NYr88IPSvvfb2btjTw4XaL6erx\nHwnvJj/L9xC9OBfHo+V59N3L1+zMGHc/qzBu+SG9KDw3xeFfTCrQ0XkWqMmjQwvq9uJkSTBA\nAk1NFg8OLajZuvyf+rurMDgCTU12tX+Cyh93O2D+7I+BgRFo6vL3MwJr9uYjr2BwBJq6NB7M\ntx8aCwMj0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRA0yfz9zf5ZfLPTxHkpEI0ydpFf2zS\nn9bX95MLNLFYu+iPH2v4fnKBJhZrF/3ReKBj3CdcWLsI4XZq5/J5pfPT7M8vn6KaJJeP7dtN\n8lONrrIPCpksSie1y86Bn56/tV9mnzMyW5UmL0+Vn8B+eneS/cKwp++uj/czP5/6ozi5QNMk\naxchzC9nky6f5PnyMU7ZWZhKgZ7kJze9fiJX4bP5duntW5fL2W1vk5em2v390KnysPl3zx+Y\ntT3cTy7QNMnaRQibwudn3Wp7+wTc2V2gk+xTC/8dy7u/fbTWyaXJ2Rb5PP9sw+yTFf/dJi9P\ndf0Q1cJHYZeGLX5cWHYUSHlygaZJ1i5imJzOvr8rfrB49gGn//aH/bLcz+xr1sjLNKVKnvq5\nP6U5Od1gf7rT881KUx1vnm7zhN9u8WDY7AN456eflgcVaJpk7SKG5Wkvw6L4Qd3zy5VF+eNg\nk7tP1ypWcnb+vNl8AzfbPL594O1dTPOrs9Nd7SfL3fUWD4bNb1OeXqBpnrWLGPannRLp9fO8\nD4UP996Va5jcbrRbLaZJsZK3/RFpXv3k1ujbzW5TPYnus2H/DCrQNMnaRRD5tuz6tM16dqvf\nk1KuJpcaF6cpfG9xifWuMFlxqtdbxQ+HLQ0q0DTJ2kUQ6+xYi2lp50VxUzY9/A306nhhMv+3\nLVayfJTeYb86HXQxvU1WmurdFvTfYcuDCjRNsnYRxTGt69vh0JnZ833Q+dfJOed3+6BL+6cP\n54OabzcrTTV9sA/69bDlQQWaJlm7iOK0O2JZ+M6jozj2hz+hLm1BHzdw003+ZZrH9PwXw/Q2\neWmqikdxFIYrDyrQNMnaRRS7PND74reu7wk57ZnODsrIDvW4VHGaX12npUpeD2zenHab7K5H\niJwnL091vfm/2x3fDVsOdHlygaZJ1i7CyP74Nit/a1oMZX4kcmFncvbultOfAItvblmfv5m/\nNfDyR8JpYfLyVJtX7yQ8DVsOdHlygaZJ1i7CWCWP9h+nhUOZZ+WdwoftsbnpfLsrhT0/ucZs\nfZk+q/K/0uTlqbJTd1xufr3j0rD3e1SKkws0TbJ2Eca/8p8IYfQEmii2aek8SYBAE8Npx+62\n69mASASaGP4cYwcINDFkJ3hedT0TEItAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRA\nAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRA\nUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl\n0ABBCTRAUAINEJRAAwQl0ABBtRDoBIAvattGoJsfAiA6gQYISqABghJogKAEGiAogQYISqAB\nghJogKAEGiCoVgO9Wc7yN8fMFpumhgAYjBYDvZ8U3sA4bWQIgAFpMdCLJF1t80u7dZosmhgC\nYEBaDHSabK+Xt0naxBAAA9JioEsnZnp9liaBBrAFDRBVu/ug17v8kn3QAO+1eZjdtHAUx2Tf\nyBAAw9HucdCL/DjodLZ0HDTAO95JCBCUQAMEJdAAQXUVaMdBA7wRJ9C/fNa4oAMDNIxdHAIN\nDJBAAwQl0ABBCTRAUAINEJRAAwTV6vmgKx9JJ9AAbQb6n0ADfKDNXRzb9PVHxX4/hEADA9Tq\nPujt69P0fz+EQAMD1O4fCf8VPvWqziEEGhggR3EABCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQ\nAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQ\nAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0Q\nlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJ\nNEBQAg0QlEADBCXQAEEJNEBQbQZ6N0/S5eHwb5Kki3qHEGhggFoM9D5Njv4ts3+Taa1DCDQw\nQC0GepEct5sXaTLfH/b55fqGEGhggFoMdJpPmCT7/Eta5xACDQxQi4FOktu/ly81DSHQwAB1\nsAWd/bu3BQ3wRgf7oBf78+X6hhBoYIAcxQEQlOOgAYLyTkKAoAQaICiBBgiqq0A7DhrgjTiB\nToq6mCWAWOziAAhKoAGCEmiAoFoN9GY5y/cwzxabeocQaGCA2nyr96TwV0Bv9QZ4o9WTJaWr\nbX5pt06dLAngjVZPN7q9Xt463SjAG62fsP/RlZ+HEGhggGxBAwTV7j7o9S6/ZB80wHttHmY3\nLRzFMdnXOYRAAwPU7nHQi/w46HS2dBw0wDveSQgQlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJ\nNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEAD\nBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQ\nAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQ\nAEEJNEBQAg0QlEADBNVmoPeL9PjvcpIk01W9Qwg0MEAtBnqXJslhf/wnM611CIEGBqjFQM+T\n2f74z3x3bPU8WdQ5hEADA9RioJNkf/7ncNgnaZ1DCDQwQK0G+vhPmhSu1DaEQAMD1Oouju3h\nsMz+ybagX+6EFmiANgO9TdLF9jBLj4VeT5J1nUMINDBAbR5mtz4fwZFZ1jqEQAMD1O4bVVbz\nSVbn2XJX7xACDQyQdxICBCXQAEEJNEBQXQXacdAAb8QJdFLUxSwBxGIXB0BQAg0QlEADBNVq\noDfLWb6HebbY1DuEQAMD1GKg95PCXwGdsB/gjRYDvUjSVX4qu8NunTphP8AbLQY6PZ1pNLd1\nwn6AN9o+Yf/DKz8PIdDAANmCBgiq3X3Q69NpRu2DBnivzcPspoWjOCb7OocQaGCA2j0OepEf\nB53Olo6DBnjHOwkBghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYI\nSqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAE\nGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqAB\nghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiCo\nTgKdvLsLgQYQaICoWgx0UlbnEAINDFCLgd6kAg1QXZu7OPazZLrL78EuDoC32t0HvUqS1UGg\nAapo+Y+Eu2ky2zcfaMEGBqD1oziWSboWaID32j/Mbjt58xfCL4YQaGCAujgOei7QAO8N863e\nAg0MgEADBNVVoJt9o4pAAwMQJ9CV32ZYYZYEGhgAuzgAghJogKAEGiCoVgO9Wc7yPcyzxabe\nIQQaGKAWA72fFP4KOK11CIEGBqjFQC+SdLXNL+3WabKocwiBBgaoxUCnyfZ6eZukdQ4h0MAA\ntfqRV8+u/DyEQAMDZAsaIKh290Gv80+8sg8aoII2D7ObFo7imOzrHEKggQFq9zjoRX4cdDpb\nOg4a4B3vJAQISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYI\nSqABgvoh0K8/teonAg3we6AbybRAAwg0QFQCDRCUQAMEJdAAQQk0QFA/Bbqky7kSaGCABBog\nKO8kBAhKoAGCEmiAoH4K9G7+L/uyn/yrbX7uh/ju9gINDMAvgd6lySz7uk6SdFffLB0EGuDw\nW6AnyXyfX9hMk0ltc1Qa4svbCzQwAD8Eep0sr9+bJat65qc8xLe3F2hgAH4I9DzZX7+3S6b1\nzE95iG9vL9DAANR1wn5vVAGo2Q+BTvsTaMEGeuinXRzr6/fWp+M56iLQAL8Eens7uG6Xxv4j\noUADPfTLYXaLJF1uj1+3y7TevxEKNMCP7yRcXk9lN69xlg4CDXD49Vwcu8X0WOfZst73EQo0\nwGEsJ0sSaKCHagr0dpH+PCtvhvjk9gINDEAdgd4tJ0ki0AD1+jnQ+9Wxzsl0/eTG3xFogF8D\nvZrmB3H4IyFA7X4J9Hp+bHO62Nb7Nu/SEF/eXqCBAfjpXBzHOm8OdZ+HozjEt7cXaGAAfjqb\n3eJyobbZuRvi29sLNDAAtqABgqphH/RGoAEa4CgOgKBqOg565jhogJp5JyFAUM7FARCUs9kB\nBCXQAEG1Gej9/HZWpdeH5gk0QJuB3qf5IXmnj/8WaIA3Wgz0Ivl3rPS/NP98WYEGeKPFQKen\nCXfpZCfQAG+1GOhLk/fTqUADvNVioCfJ/nJpKtAA77QY6H/J/Hxpl0wFGuCNNg+zW1yrvE4E\nGuCNVt+osp1dLu3mAg3wmncSAgQl0ABBCTRAUF0F2h8JAd6IE+ik6MdZEmhgAOziAAhKoAGC\nEmiAoFoN9GY5O50SerGpdwiBhmEa+Wu3zRP2Twp/BZzWOoRAwzCN/LXb6gn709U2v7Rbp8mi\nziEEGoZp5K/dVk/Yv71e3iZpnUMINAzTyF+7HZyw/++VP/7L+Oqrr76O/OvhY7aggZaM/LXb\n7j7o9S6/ZB80UMnIX7ttHmY3LRzFMdm/uqVAA5mRv3bbPQ56kR8Hnc6WjoMGKhj5a9c7CYG4\nRv7aFWggrpG/dgUaiGvkr12BBrrz7rU58teuQAPdEeiXBBrojkC/JNBAdwT6JYEGuiPQLwk0\n0B2Bfkmgge4I9EsCDXRHoF8SaKA7Av2SQAPdEeiXBBrojkC/JNCMnbWjSwL9kkAzdtaWLgn0\nSwLN2Flb6vTra/fX+xsYgWbsrC118tqtlUAzdtaWOnnt1kqgGTtrS52afu2O7NkRaPru12fX\n2lInga6VQNN3Ah2JQNdKoOm7jgNt5SoR6FoJNH0n0JEIdK0Emr4T6Eg+fTQF+iWBpu8EOpK2\nAz3wh1+g6buO1xYrV4lA10qg6bumk/Dh3Y2cQNdqnIEe+JM6MgIdiUDXSqC/GI5QBDoSga6V\nQH8xHKEEC/TI1yaBrpVAfzEcoQh0JF0HemAPv0B/MRyhCHQkAl0rgf5iOEIR6EgEulYC/cVw\nhCLQkQh0rQT6i+EIpeEk/Hp3A/drMAX6JYH+YjhCEeguCXSjBPqL4QhFoLsk0I0S6Ao3J7SW\nAz2yQrwj0I0S6Ao3JzSB7pJAN0qgK9yc0AS6SwLdKIGucHNCE+guCXSjBLrCzQml6Ze0QH9C\noBsl0BVuTigCHYlAN0qgK9ycUAQ6EoFulEBXuDmhCHQkAt0oga5wc0IR6EgEulECXeHmfKTp\nh0+gIxHoRgl0hZvzEYEeE4FulEBXuPnQ1by8Aj0mAt0oga5w86ET6FfXR16Ie3U/HAL9kkBX\nuPnQCfSr6yMvxD2BbpVAV7j50An0q+sjL8Q9gW6VQFe4+dAJ9KvrIy/Evb4FuudPR6uB3ixn\nSWa22NQ7hED/RKBfXR95Ie4JdKtaDPR+ktxMax1CoH8i0K+uj7wQ9wS6VS0GepGkq21+abdO\nk0WdQwj0TwT61fWRF+KeQLeqxUCnyfZ6eZukdQ4h0C99+hppeLi671+g2yTQrWox0Eny7MrP\nQ4w80L8G+M3D8SmBfnm95wS6VbagK9w8OoFu8vrIC3FPoFvVYqAXSbre5Zfsg65Xza8RgW70\n7ntOoFvVYqAP08JRHJN9nUMI9EfX30wv0I3efc8JdKvaDPRhs8iPg05nS8dB16nhIv06O3UT\n6C4JdKtaDXRjQwh0ndcFutG77zmBbpVAV7h5dALd5PWRF+KeQLdKoCvcPDqBbvL6yAtxT6Bb\n1VWgHQddI4Fu8vrIC3FPoFsVJ9AJACUdBbrzIT7R9i/5YBsF0baReq7rtaXjh7/rZzPY/6/e\nEegKun5JBAv0rz8PvrhN63ptEeg6rwt0AF2/JAR6SLpeWwT6p+vN3t3b+29mkrPGTtjfsK5f\nEh0/HAJdq67XHoH+6Xqzd/f2/puZJNfgCfsb1vVLQqCHpOu1R6B/ut7s3b29/2YmyTV4wv6G\ndf2SEOgh6XrtEeifrjd7d2/vv5lJcg2ebrRhXb8kBHpIul57BPqn683e3dv7b2aS03TJsyu1\nDdGQrl8SAwv0u58HW/y6db32CPRP15u9u7f338wkOVvQdV1vmUDXquu1p2drX90E+pkGT9jf\nsK5fEgI9JF2vPT1b++om0E81d8L+hnX9khh5oAem67WnZ2tf3QT6ucZO2N+ykb1EBLpWXa89\nPVv7mibQXwi+TozsJSLQtep67enZ2tc2gY4xxC9G9hIR6Fp1vfb0bO1rm0DHGOIXI3uJCHSt\nul57erb2tU2gYwzxi5G9RAS6Vl2vPT1b+9rWcKB/nZ1mJgk4xC9G9hIR6Fp1vfb0bO1rm0DH\nGOIXI3uJCHStul57erb2tU2gYwzxi5G9RAS6Vl2vPT1b+9om0DGG+MXIXiICXauu157ga1vX\nfgz0pz9/R6C/MLKXjEDXquu1J/ja1jWBjjHEL7xkSgT6I3U/+yNb25om0DGG+IVAlwj0RwQ6\nNIGOMcQvBLpEoD8i0KEJdIwhfiHQJQL9kbqf7ZGtbU0T6BhD/EKgSwT6IwIdmkDHGOIXAl0i\n0B8R6NBqfjgEugMCXSLQPxHoUII9HAL9BYEuEeifCHQowR4Ogf7CyIN8T6B/ItChBHs4BPoL\nAl0i0D8R6FCCPRwC/QWBLhHonwh0KMEeDoH+gkCXCPRPogV65II9PAL9BYEu+XV2e774vxLo\n0AS6myF+IdAldc9uzxb/VwIdmkB3M8QvBLqkZ7MbjUCHJtDdDPELgS7p2exGI9A8J9BfEOiS\nns1uNALNcwL9BYEu6dnsRiPQPCfQXxDokp7NbjQCzXMC/QWBLunZ7EYj0Dwn0F8Q6JKezW40\nAs1zAv0FgS7p2exGU/ezP7C1a+QE+gsCXdKz2Y1GoHlOoL8g0CU9m91omg70pz8nEoH+gkCX\n9Gx2oxFonhPoLwh0Sc9mNxqB5jmB/oJAl/RsdqMRaJ4T6C8IdEnPZjeatgNNnwj0FwS6pGez\nG41A85xA10Cg+V7dD5+nY0gEugYjDzShWHuGRKBrINDEYe0ZEoGugUATh7VnSAS6BgJNHNae\nIRHoGgg0cVh7hkSgayDQxGHtGRKBroFAE4e1Z0gEugYCTRzWniER6BoINHFYe4ZEoGsg0MRh\n7RkSga6BQBOHtWdIBLoGAk0c1p4haTPQ+3mSTNfnO3l5Lz1bx97NrkDTHmvPkLQY6H2aZGan\nOxHo6tehOmvPkLQY6EXy71jpf+k0vxOBrn4dqrP2DEmLgU5PE+7SyU6gP7oO1Vl7hqTFQF+a\nvJ9OBfqj61CdtWdIWgz0JNlfLk0F+pPrUJ21Z0haDPS/ZH6+tEumAv3BdajO2jMkbR5mt7hW\neZ0I9AfXoTprz5C0+kaV7exyaTd/eS//ZfrzNfnw559e99XX6l+TIPPhay1fDx/zTsJ7tqCJ\nw9ozJN7q3QKBpj3WniER6A4INM2x9gxJV4Ee1B8JPyXQNMfaMyQC3QGBpjnWniGxi6MDAk1z\nrD1DItAdEGiaY+0ZkjiBToqaGSIKgQaqaDXQm+XsdEroxaapIXpBoIEqWgz0flLYRJ42MkRP\nCDRQRYuBXiTpaptf2q3TZNHEED0h0EAVLQY6TbbXy9skbWKInhBooIoWA13605/joD+4DoyT\nLegOCDRQRbv7oNe7/JJ90J9dB8apzcPspoWjOCb7V7cceKEEGqii3eOgF/lx0Ols6TjoT64D\n4xTnnYQtD9ElgQaqEOgOCDRQRReBfn+qjYEXSqCBKgS6AwINVCHQHRBkoAqB7oBAA1UIdAcE\nGr62K2kAAAiWSURBVKhCoDsg0EAVDrPrgEADVQh0BwQaqEKgOyDQQBUC3QGBBqoQ6A4INFCF\nQHdAoIEqBLoDAg1UIdAdEGigCoHugEADVQh0BwQaqEKgOyDQQBUC3QGBBqoQ6A4INFCFQAcw\nssUFKhLoAEa2uEBFAh3AyBYXqEigAxjZ4gIVCXQAI1tcoCKBDmBkiwtUJNABjGxxgYoEOoCR\nLS5QkUAHMLLFBSoS6ABGtrhARQIdwMgWF6hIoAMY2eICFQl0ACNbXKAigQ5gZIsLVCTQAYxs\ncYGKBDqAkS0uUJFABzCyxQUqEugARra4QEUCHcDIFheoSKADGNniAhUJdAAjW1ygIoEOYGSL\nC1Qk0AGMbHGBigQ6gJEtLlCRQAcwssUFKhLoAEa2uEBFAh3AyBYXqEigAxjZ4gIVCXQAI1tc\noCKBDmBkiwtUJNABjGxxgYoEOoCRLS5QkUAHMLLFBSoS6ABGtrhARZ0EOnl3FyMr1sgWF6hI\noAMY2eICFbUY6KSsiSF6amSLC1TUYqA3qUA/NrLFBSpqcxfHfpZMd/k92MVRMrLFBSpqdx/0\nKklWB4G+N7LFBSpq+Y+Eu2ky2wv0nZEtLlBR60dxLJN0LdBlI1tcoKL2D7PbTt78hfD3Ifpm\nZIsLVNTFcdBzgS4b2eICFXmrdwAjW1ygIoEOYGSLC1TUVaC9UaVgZIsLVCTQAYxscYGK7OII\nYGSLC1Qk0AGMbHGBiuIEuvKZlIZnZIsLVNRqoDfLWZ7f2WLT1BC9NLLFBSpqMdD7SWETedrI\nED01ssUFKmox0IskXW3zS7t1miyaGKKnRra4QEUtBjpNttfL2yRtYoieGtniAhW1+pFXz67U\nNkRPjWxxgYpsQQcwssUFKmp3H/Q6/8Qr+6DvjGxxgYraPMxuWjiKY7JvZIh+GtniAhW1exz0\nIj8OOp0tHQddNLLFBSqK807CloeIZGSLC1Qk0AGMbHGBiroI9PtTbSgWgEADRCXQAEEJNEBQ\nAg0QlEADBOUwO4CgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYISaICgBBogKIEGCEqgAYIS\naICgBBogKIEGCEqgAYIKGmgAYgb6UwFn6RNmv1M9n3+z36WAs2+W6mb2O9Xz+Tf7XQo4+2ap\nbma/Uz2ff7PfpYCzb5bqZvY71fP5N/tdCjj7ZqluZr9TPZ9/s9+lgLNvlupm9jvV8/k3+10K\nOPtmqW5mv1M9n3+z36WAs2+W6mb2O9Xz+Tf7XQo4+2apbma/Uz2ff7PfpYCzb5bqZvY71fP5\nN/tdCjj7ZqluZr9TPZ9/s9+lgLNvlupm9jvV8/k3+10KOPsBZwmAjEADBCXQAEEJNEBQAg0Q\nlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQ0QK9SJN0se96Ln7w\nL9oj+ol/k14/+vt5ksy3Xc/FTzb9XX2Sk65n42vbbO3ZdT0X94I9ntP8OZ50PRvf2/Z4DT0s\n8kc/7W2h03z++1zofdrb1Wfb80CvY678sR7PTZJuD9s02XQ9I986znusR/QT22S+z/4LMO96\nRr60yOZ8kcy6no8fzPq7+mx7/cAff7sf07OfJYuu5+NOrPVhkayP/66SZdcz8qV/ybS/r7Bj\nHfIvvV2CNMk2f3o7+4dsze/v3P/r7as2t8rTvE/SrmfkTqz1YZZk+4D6+7v4+CT39xV20fMl\nCPcSq27X59/v/5J/Xc/CL+ZBd43FWh+Snm/DbXs87xf7ZNr1LPxi0eNOTJNdf1efWbKeJ2m0\nPQSVTZLDMs338cUSa33oe6AP/Z733L98N1NPrZJwOxGrWyarHq8+s9PfCPv62z1J8gUI99+v\nWOuDQHdul/Z1/1Lm3yzt7a7QfM9ef1ef5Pjr5bDv7X9gkuz4hP083NoTa30Q6K7t075uAl3M\n+5qISXaIV89Xn8O+r8fIng7P3IWb/VjrQyrQHZtGW0E/Fu8P8dXM811LPV99+rsAUbcNY83P\n6SiOXW+P4jgEfIY/sZtMw72V6mM9fQaSq67n5Cd9nf2ox5jGmp9lvhWx7vEfeuI9wx9Y9/ZP\nPLnTcdDx/pdaTd8DfXn0e7pxdUrPLtwrINba0Pt3EvY60PHWzs/k7yTcz/q6DzrX39VnkW1W\n7Rd9PQbo+Ht9n/2RcNX1jNwJtj5Men2oTqa/r7DDvN+bcJdzcfR57enx6rM/Pfq9/c/vMubK\nE2x92Odns+t6Ln7R31fYoef/xz7k50Kc9Hn7uderz77nj/56GjE9/V0fAAZOoAGCEmiAoAQa\nICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGC\nEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiBBghKoAGCEmiAoAQaICiB\nBghKoBmkxJrNAFiNGSSBZgisxgySQDMEVmMGSaAZAqsxg1QI9HqWJOnidHmRJgvxpjesqgzS\nLcLLJJcXeppdmgs0fWFVZZBuEU6S1eGwyq+vk3R72KYCTV9YVRmk+wjn12fJ+pBl2lpPT1hV\nGaRihHfr5TS/fv6mQNMXVlUGqRDh6Wkn9EGg6R2rKoN0i/A8mfxb7wSaPrKqMkjFPxIe/9nZ\nB00fWVUZpGKgN4ft1FEc9JFVlUFKksue58X50uZw3R1tracnrKoM0i3Qh3mSTDfrZJZ9e5Ee\nLws0fWFVZXSSaddzANUINOORv6lwP0sWXc8IVCPQjMf5tBxp1/MBFQk0I/JvmiQT28/0hkAD\nBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQ\nAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQ/wMUjUWP1xe4\negAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"FAS Box-Cox(Reserva) ) tras una diferencia de orden 1\n",
       "y otra estacional\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rests.d12 <- diff(rests.d1, lag = 12, differences = 1)\n",
    "acf(rests.d12, main = \"FAS Box-Cox(Reserva) ) tras una diferencia de orden 1\n",
    "y otra estacional\", lag.max = 75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con esto hemos disminuido gran parte de las autocorrelaciones al intervalo (-0.2, 0.2) el cual se considera suficiente para afirmar que la serie es estacionaria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAMFBMVEUAAABNTU1oaGh8fHyM\njIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD////QFLu4AAAACXBIWXMAABJ0AAAS\ndAHeZh94AAAgAElEQVR4nO2d2WKqMBBAg1rbel3+/29vAReWACHLZAjnPLSKSSYLHDESNA8A\nAFCJyV0BAACwg6ABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCU\ngqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEA\nlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoPVhnhzPt/eG5v93ZcxXjOJjpTfvqv6sKvLf\n18GY6vTrkPRqzKkb6bQu0jInY679LeFd7Mqza/OOr2xxsA46Xx/mw+W1of73XW/RKWhjzitK\nPL0yVf9c0l77kY4rIjnwfAd486+SOyb6gs40vrLFwTrofH10ZGT+PTfU/w6jcz3f4mOl79bU\n5XS45djJtdSgP30ehpEin0MPulVSSH1BZxpf2eJgHXS+Pp6HxP1SzwOMt2eqztxL9/PToy78\nnRNXP/fH4/ZjBievFs5P83tFcuO3f/qfQdAZIrujs1a7gc7Xx+eQOLSTHIOpyj9LVaZ6TlD/\nbbwdGsMMtj4uf2eqX9dXjr+ijr0Jk8dvPdNwaDPcv+vT2sGk8O2rMoefjhs/5Y9q+n50+ao/\npzeRbi//Hp+fBBr+zomrZyHXw/dtIVP1LHccqVudfvUt3fPvNXfw1Val2/ZH923weZLe69il\njjL9Og77vhfL2rXpx/dTU0thf7VpvkUYVGPwtFvcpxxIDILWx0dGl9YrzYaPOm5Vf/rj0MzL\nDreeO3MIr9fOneLfEw3/Pq/35nf/PTc90/fKH9X0JblXqY1k/06Rvx/NWXBnZvU8nqOYzvTv\npeuRoLvV6Vff2j0f0VeDtjen9O9G9QTdduxiR42N1u37fixr1yYf305NLYXZavMYCrpX3Lsc\nSA2C1sdHRvf28/zgAH5JonomNs00gGVrTaPG12ud8/E/Dx7vjQdPzXnlXwn3Y8+d1aeQxzDq\noKb1KVgjh/fXf61Xj3XE336ev229k8nZTD+vGnUjnYbV6Vff2j3ntu2Xpp69tneCPAaCbnIu\nd9TYaJ2+H8Sydm3y8e23bliYrTaPgaD7xb3LgdQgaH2Yt6Afb1d0trfHyr11Rb2xPnLGW6tL\n45Pn0VVdG628dV/PntzehZr2yb07v/vbFnGpukfoq/xP9V5UdSX+/Gd+/lJ9P11xq6ebzdRZ\n95O5TO9r4DqRmhd61elV394911Zl7QxHr+3D6zg6fmpyLnaUxWidvh/EsnVt+vHt1HRc2LM2\nv4Pa9GvVL+5VDiQHQetjQdB/0rq3T58nM5fJrfc2y+n5rJ3y7RuyeVYfm+0U8Idnpsafo/I/\nmV+c6le/XmeWr1mNn89ZnqV1LXOZXl7oRho3t1d9e/fUJd0HYn3Plkzpttcjkx1lMVqn723N\nHnRt+vHtPBkX9qrN75yg+8UN+waSgaD1sSDozllr++xu3TrOPyz+9ntuZhafV+AO1PNO9i6j\nU/4nzWfz/VOZ2nlt7sM7wzPZWNALmQaRvu+DyNWg+vbuqdP81g76HrZ92D+dja+TxPmOmjKa\npZ/tXZt+fIdd3SusstdmJOhOcZ2+gbQgaH18jqnuHLTlAO4enJNbHxMH8O+hk/78Ol5vw2Rj\nQVtk9rgem8ndz4uvR7XRvrsVHF/tu5Cp89Lt+K5hvzrd6ts7orb/8T3/3Wv7tKDbDUsdtSDo\nQSxL1wqM77imnxcnajN42iuuHxASQkfr47P7d6/ieP+rbIfjxNbpA7j+QHv4+rk+X7v/tl/T\nH0dFPB9UtmOyU3BzMmY6J8PNKfC9nhOoumdt46s4FjL1Ih1fNRxUp1N9e0c0WV+rXgZtnxf0\nYkfNC3oYy9YqgfGdKczpDLpfHIIWg47WR08q4+ugT70JwFdi+9b2/9EyR/m8xLp7rF2+ugfe\nYGryZJt27AnaNIn608ntBRq9Oeh/n9PPf02F5jINVVbb5Hfc3G717R3RtOMVadj2WUEvdtTr\nHeafVXCW7FOzvqnHd6IwS23GzekXh6DFoKP18dr9/50+F1V1/v2297D4bc/iOid6lq3tf9u3\n/M8U7SnR4T03+Zlg/ul/ud8vf1DTdqnD+IKMWsbXqn8Vx2clYZ3qNJ/p2PmSsO2TZxV71elV\n394R7RdqnynYT9s7a2Peke6jLpzpqPpN49zcw8Mm6EEsW9emH9/hePUKe13FYT5n8OPm9ItD\n0GLQ0fowHbpnQZ2zyJZ/nY32rZ9jruXnva2dNH5e6fXnyOOtmfvtrA6buA56sPChX9XPbTZe\n1+de68J7i7PvnYKbc+mZTF+vcO9mvs6Cu9XpV9/ePe01ae2bS6/tjfQ75/j11nMn53JHffU7\nqt/3g1jWrk0+vp3xshQ2qI21Of3iDIKWgo7Wx0B6owP48nyxu25sYutLQeOVZs/lY6Y9VX19\n92VZSXh6ltErf1zT5/aXbBvjfbfG+H5fO9Fyfxv5cFvINFqo0lzq8brk+h22V31797Tbf7st\ne7a9t1Dl4/FXzuWOuj3jGZugB7GsXZt+fD/jZSns+eS9ZNTWnH5xBkFLQUfr46Wv4/n+3tD5\n97zzwql/5wX71tf/+tYLg9eufyaqvq7Pz/fNtOrgrs79WzT0yh/UtLP98lWNbqtxGi4ebBId\nPherTWZ6ryH5NPO1Nq5XnV717d3znlwdtX1wR+hT75tZl46qUxx/J75k62e3dq3A+D5mCmvr\nf3m/am1OrzgELQYdDZqxXjwSmf5S9N1ypx8UgqBBM+f0a9Yu635toDieMz/1pexLN38FcRA0\naOaW/qZpowmYndH5/QTWb6sDQYNqxj8ZGJnhT17tjvctVPf9QUIpCBpUk9yfyd8B1HP/ri8n\nqUY3gQIFIGgAAKUgaAAApSBoAAClIGgAAKUgaAAApSBoAAClIGgAAKUgaMhGwD131mU9+QT6\nWk4SoZT1fcCdivYEYw3ZkBL0xSPQvyrGobFcCoKGORhryIaQoJv7HacMEFIKgoY5GGvIhoig\nb+1PHKYLkL6U9GWCVhhrkOf2VZnDT+9289V5cEu55s74r/tD/D28HZqb+cxkrbddjn+ZevfW\n6PxWyHvL677H/btNdyI+7x7Ui/xbm/4wrOaoFvfvOtzpt1dKP3Nzt/zj4Ib8w/b2mtLNjqD3\nBGMN4vwb/MbS83ZqvZ+Wfd0E8/2DKofmxqNzWU37O02mf/ejuoi+075e99Xs3wm6G7En6Dby\n+66cvWo+hrV43xvu2C2ll/k2/oEqS3u7TellR9B7grEGcSZ+jrb7ex6nd4rGWO3D3/msn5e6\nl07UZ6p9p/3r/KrWx7a9iD1BN5F//pR7//zglrU9dS2+mmrWP7v48ymln/ndhsvjXTVre19N\n6WdH0HuCsQZpfv9Udvn8RHSrn/vX6ydda+rv9X7+Nn5/NFYraj6raV/8GhtssOXQ3qL/1v25\ncUvEV9Ym8jOPTY+9Wpg23b0t+5m6l/kveXVtFP5JYYneaUo/NoLeE4w1SHN6TjE8r347PX/L\ntXtu+vX6oe3z+xdcL4tZn4nui4L+bqcXzt1f87ZEfGUd/k6upT2fWtRvHZ87Kw9SN0+fbbgf\nvm/vFBPtHTQFQe8PxhqkeRumO4nwnqh4vdL+APftnea+mLXj1KmILc+fR63ev/Jtj9jf/vfK\n7/loLHrs1eL7OTMx/E3uT+YJ6U5FH8VG0HuCsQZpJi1rxkkeE8KyZXUWdHsSe+nNVS9G/D2M\navnJ0Hnp/JL1rZO7m3n+rNgavRcbQe8JxhqkGVi2sgine0ZpPTm2ZXUX9KW+yOLYm7xYivj7\n9+Dw9XO16HHQgPtve9HF8ZO7l3npDHocvR8bQe8JxhqkeU0k/5rXRPLo1/BOEzPCs1ndBV07\n9TK6bGRqDrr5f3iGsujR0oDLV0+mvcxHyxz0fPR+bAS9JxhrkOanvUTht3qfH1b/mn/Hd5Kp\naypms64QdDsP8d3ZYot4HxdqO4Pu1eLwni2vPqX0MjtexdGJ2o+NoPcEYw3iTFzM3F0C8l6a\n0c4Tv6U0l3WFoG9Npnt30yBiXfK5k/XYPH1d3zfRnn/t7MntfaHIs5R+5nfyn0/5E+1t//ez\nI+g9wViDOM/lgKenai5PNZ27aY5dX32kNJd1haDrE93hipN+xHqOonta/gxsKstSwl4tXl8S\nHjul9DP/m1tJ2G9v+7+fHUHvCcYa5Ln9ievYvaHGny9Pg3ncy1fVvzfFYtY1gq4nsccTx52I\n9bRwdzb4cf0LXH1db7alhL0GNPPPx59eKf3M9a07hpW2t/f5v5cdQe8Jxhr2yE//K0IAnSBo\n2CHXajCjAqASBA27o53RvS4nBMgMgobd0fj5ezkdQG4QNOyO+gbPv8vJALKDoAEAlIKgAQCU\ngqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEA\nlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlOIv6H/fp+bHN0/nfxHrAwAAT3wFfT+Y\nD8f5EAAA4GFbX0GfTfV7bR7dLpU5pwgBAFAQgoKuzPX9+GqqFCEAAApCUNC9s/X5U3cEDQDA\nGTQAgFZk56Avt+YRc9AAAMsICvpx7Hw3ebgnCQEAUA6Sgn78OzfXQVen74XroBE0AICsoDWF\nAADQDoIGAFCK7BSH61JvBA0AICnoNUu9PUMAABQES70BAJTCQhUAAKWw1BsAQCmcQQMAKIWl\n3gAASmGpNwCAUljqDQCgFFYSAgAoBUEDACiFpd4AAEphqTcAgFJY6g0AoBQWqgAAKEXPUm/T\nxTMEAEBBcAYNLjAiABlgqTe4wIgAZICl3uACIwKQAZZ6gwuMCEAGWEkILjAiABlA0OACIwKQ\nAZZ6gwtc+QiQAZZ6gwsIGiADLPUGFxA0QAZYqAIuIGiADOhZ6h0jBKQCQQNkgDNocMAwJAAZ\nYKk3OICgAXLAUm9wAEED5ICl3uAAggbIASsJwQEEDZADBA0OIGiAHCBocMAwJgAZQNDgAIIG\nyAGCBgcQNEAORFcSOv8uLDJQBoIGyIGgoH8Q9GZB0AA5kJziuFbzNxmNEALSgKABciA6B32d\nX+AdIwQkAUED5ED2S8Kfzv2SEoWAFCBogBxwFQc4gKABcoCgwQEEDZADBA0OIGiAHCBocABB\ngzzscgganDAPBgWkYY9D0OAEggZ52OMQNDiBoEEe9jgEDU4gaJCHn5JH0OCCef8BEANBI2hw\nAUFDBhA0ggYXEDRkAEEjaHABQUMGEDSCBhcQNGQAQSNocAFBQwYQNIIGFxA0ZMCwzyFocMB0\n/gLIwA1gHggaXEDQIA+CfiBocAFBgzwI+oGgwQUEDfIg6AeCBhcQNMiDoB8IGlxA0CAPgn4g\naHABQYM83ELxgaDBBQQN8iDoB4IGFxA0yIOgHwgaXEDQIA+CfiBocAFBgzwI+oGgwQXT+wcg\nAYJ+IGhwAUGDPAj6gaDBBQQN8nAPxQeCBgfM4D+AAAj6gaDBAQQNGUDQDwQNDkwJmmGChCDo\nB4IGBxA0ZABBPxA0OICgIQMI+oGgwYEJQfObnpASBP1A0OAAgoYMsH71gaDBAQQNGUDQDwQN\nDpjRg/YZwwTpYHVUDYKGRRA0yIOgaxA0LGIXtGGYICEIugZBwyIIGuRB0DUIGhZB0CAPgq5B\n0LAIggZ5EHQNgoZFEDTIwy26ahA0LGIVtGGYICUIugZBwyIIGuRB0DUIGhZB0CAPgq5B0LAI\nggZ5EHQNgoZFjOURgoa0IOgaBA2L2ATNvSAhLQi6BkHDEsb2EEFDWibuALMzEDQsgaAhAwi6\nBkHDEggaMoCgaxA0LIGgIQMIugZBwxI2QZvhKwBxQdA1CBqWQNCQAQRdg6BhCQQNGUDQNQga\nlkDQkAHr8qjdgaBhCQQNGUDQNQgalrAI2oxeAYiK9dqh/YGgYQkzfoygITEIugFBwxIIGuRB\n0A0IGpZA0CAPgm5A0LAEggZ5EHQDgoYlEDTIY9nr9giChiXGh4qxvAIQEwTdgKBhCQQN8iDo\nBgQNSyBokAdBNyBoWAJBgzwIugFBwxIIGuRB0A0IGpYYHSrcZwySg6AbEDQsYYZPEDQkZ7TX\n7RMEDQuY0TMEDclB0A0IGhZA0JABBN2AoGEBBA0ZQNANCBoWGAnaTLwEEA8E3YCgYQEEDRlA\n0A0IGhZA0JABBN2AoGEBBA0ZQNANCBoWQNCQgfF307sEQcMCwyPFTL0EEA8E3YCgYQEEDRlA\n0A0IGhYYDIZB0JCe4V6Xpxb5QdCwwIygGShIBIJuQdCwAIIGeRB0C4KGBeYOFQYK0oCgWxA0\nLICgQR4E3YKgYQEEDfIM96y97mkIGhaYGwwGCtKAoFsQNCyAoEEeBN2CoGEBBA3yIOgWBA0L\nIGiQB0G3IGhYAEGDPAi6BUHDPLNjwUBBGhB0i6ig/32fTM3p/C9uiL2OngQIGjKAoFsEBX0/\nmA/HqCH2OnoSIGjIAIJuERT02VS/1+bR7VKZc8wQex09CRA0ZGC0Z+10VxMUdGWu78dXU8UM\nsdPBEwFBQwYQdIugoPu3QYt63O908ERA0JABBN3CGTTMg6AhAwi6RXYO+nJrHkWfg97r6EmA\noCEDCLpF8jK7Y+cqjsM9aoidjp4ECBoygKBbZK+DPjfXQVen78jXQe919CRA0JABBN1SyErC\nnY6eBAgaMoCgWxA0zIOgQR7LjrXPfa2Mpd47HTwREDTIg6CflLHUe6eDJwKCBnkQ9JMylnrv\ndPBEQNAgD4J+UsZClZ0OnggIGuRB0E/KWOq908ETAUGDPAj6CWfQMA+CBnkQ9JNClnrvdPQk\nQNAgD4J+UshS752OngS6BZ29ApAEBP2kkKXeOx09CRA0yGMb112OdSErCfc5eCIgaJAHQT/R\nI2jTZX3uBDWCx1LHZu/27BWAJCDoJ4Us9d7n4EmAoCEDxQp6bSMKWertmKOIIZYFQUMGELRn\ner8sDSmXeiPoVCx0We4ezR0f0oCgPdP7ZWlIuVDFLYfH3PbuQdCQAQTtmd4vS5sv4VJvBJ0K\nBA0ZQNCe6f2yNCg4gy5jjEVB0JAB67iWMNiKBZ19qbcpY4hl0S7o3BWAJJQqaIm7DKlc6u2S\nBUF7gKAhAwjaN4PSpd4IOhEIGjKAoH0zKF3q7ZDF+BW8cxA0ZABB+2ZA0PsCQYM89lEtYKw3\nIOifgzGnS/QQCDoNCBrkQdDeGUKvg35+Uzh7EUc6QZcwxrLoFjQXTpYJgvbOECjosznfH4/b\n2fxI18r4lrxvEDTIg6C9MwQKujLN9XV3c4gcAkGnAUGDPBOjuv3BVi/o15xh7KXeCDoRCBrk\nQdDeGQIF/fUSdOSl3otZTO8fuIKgQR4E7Z0hRNCn75+L+f17eD/HXuqNoBOBoEGeUgW9vgGi\ngn7/nJUxVeyl3o4m2fwQS4OgQR4E7Z/Dv5eu15+f06n5qvA86+cEgjajB+AEggZ5ELR/DqUr\nCRF0EhxnjnKBoMsEQfvnQNB7AkFDBhC0f45NCtpYHoEDCBoyMDWqWx9tBL344taHWBgEDRlA\n0P45EPSeQNCQAQTtn2OLgjbWh7CMekHnrgGkAEH759i4oDc/xrIgaMgAgvbPgaD3BIKGDCBo\n/xwIek8gaMhAoYKWsa1SQc9kMpNPYAEEDRlA0LJZREIg6AQgaMgAgpbNIhICQScAQUMGJgd1\n26ONoB1e2PYQS4OgIQMIWjaLSAjXQd32GAuDoEEexw/DmwNBu7yw7TEWBkGDPAhaOItICASd\nAAQN8iBo4SwiIRB0AhA0yIOghbOIhEDQCVjsrLy9iaCLBEELZxEJgaATgKBBHgQtnEUkBIJO\nAIIGeWbGdMvD7VP3ggQ9lWu0ectjLA2CBnkQdEgeBL0flrsKQUN0EHRIHgS9HxD0KwwIgqBD\n8iDo/YCgX2FAEAQdkgdB7wflgjZCNWCPkQVBh+RRe0Ag6Ogg6E8cEANBh+RRe0Ag6Ogg6DYO\nu4woc9294aFA0I6bNzzGwiDoNg57jCgIOiQPgt4PCLqNwx4jCoIOyYOg9wOCbuOwx4iCoEPy\nIOj9gKDbCOwxopQpaK+alyToiWwI2h8E3UZgjxFltrs3OxYI2prNsnGzQywOgm4jsMeIgqBD\nMiHo/YCgnxHYZSRB0CGZti/o7Y6xNAj6GYE9RhIEHZIJQe8HBP2MoHOP0VmrYBaatdVWI2gE\nHRsE/Yygc4/RWatgEHRQJgS9HxD0M4LKPabUy7MRdFAmBL0fEPQzgso9BkFvCgSNoGODoJ8R\nVO4xpV7+h6CDMiHo/YCgXxEU7jJa3zeCWWrVRluNoJ0FvSLARneGSCDoVwSF+wGC3hYIGkHH\nBkG/IujbD3TWKgZlCtqv1kUJ2n06A0E74dD4/IJOXwWdKtRZqxgg6KBc2xK0vSgEPYOxPHJI\nnAEEnbsSSUDQQbkQdOF8rt5C0O8A6vYDoemdHCy2apPNRtApBF3qpaZzfK7eQtDv8tXtBzpr\nFQUEHZRrX4Le5s4QQud8EUHLRVmLzlpFAUEH5SpB0M4Rdipo98MfQefB/S10eyDooFwIumye\nPnKceEXQedi1oDfZbASNoKPQmYBG0HJR1rHiW4LtgaCDciHosulcw+HS9pz9g6B11SoSCDoo\nF4Iumk57nS5hQdA5QNCbA0GvEbRriF0LmjNowSirMKMHJVGkoD2rXJagLRkR9ErWttfMPk2L\npKBV7QjG8qgctH/14QWCtmcsS9AK+34oaMn+QtCqahULBB2UTaEkpjNOl+QUw+1KBjkU9v3o\nDFquw4TMqU/QxvqwGBB0UDaFkpjOGC5oVfuCwr4fT3GIdRiCVlWraCDooGwKJTGdEUGnjmCZ\ng5bqMQStqlax0P7VtB8I2ppxpiTX/UDTvrANQUtNRO9V0GbicSE4nzptCgRtzYigU0ewX8Uh\n0mmygtazIyDoDbYbQVszIujUEeyCFum1nQrazDwrAQQdlq0UQbt+F6FoVxCYOoglaMndJG0o\nyRY5gaBdE2kCQVszIuiVEUJzmKkXEoCgR89KwK1FW2s3grZmLEzQYh/nfXP0pkfLmHvQJujJ\nd8RSKFLQvtUtTNCjnLMlLYdB0CtzDJ7JqBNBFwWCDsu3I0GbsMpEZ2uCTtx5CHr8tAAQdFi+\nvQla0a6wPUEnPYkWFrSSHQFBr0ilBgRtzTlfEIIeBwjMYikgnaJDBb3WBCp2BNHPKFlwbNG2\nGo6grTkXClqKg6DXZrEWkKrSYYL+e+NY+YsEKnaEVd+DbxIEHZYPQWcDQVvL9Sr/ObZuXxQH\nxInNFgQdViUEHZYPQWdjo4JOVesAQX/E7DjP5RknPhsQdOBuiqDD8hUkaDeB69kTELS12LBv\nNpdOohH0Our+RNBDELQ1J4JeGyAwy0QBaartLejhXjJbAIJeRfgx45pXWcPnySXon8PjcTuY\nwz/P+A4hQrIi6JXlh+bZgqAtPp4rwVgf5mJd3eUxvX8hRURLp4JMgr7UO3tl/ohqaDFBLyRQ\nJujQz44uAQLzbEDQttQIOhLuU/sOZURKpwHvugYK+mh+H1dzePyao28NlkIEZQ0TdISzgahs\nV9Bp6o2gpzblIkpfIeiwjMMvWK7mvPxNi3+IoKxRBK1mT0DQ9kLDBT1ThHFKJYZmQZs4fYWg\nwzIOBX0yl+0K2uncScuesGFBJ6m41/jYEyPocMzsU+9ygtNpIN8Ux/Viqsd2pzh0CNoxQGpB\nexWOoCVZOz8jyKgavvVyzqek4S7kEvSl/n7wuz6BvvjWYClEUN7iBJ20LikFnaLiCHpyWwZi\nCdo9m5KGu5BL0I+fqp6Bfhx+fSuwHCIgr0s5M2nkBO0WAUHbi4wgaMdT/+xK0CvoaHMvCDow\no0C3IGh7sgeCtha5pmwEnQYEPQeCtubdiKBd15/oF7TLhFE8fAQ9lRRBBxKtZtkEre7Q8sxo\nzfLv5FsD5xAeeZ3KWT7p24mgI5zwIOjEWCuQvVaP9VP7a0uKVbpccXHKDhX02bzwrcFiiIC8\nmxG0e023K+j4Vd+foONpMDY5BB254SUK+uNnlVdxhAnaa4rTh00L2r3DVQvazdy5Vbg1QSee\nQo17Vqjv0PLL2c1Smd/H0dxuRzX34uhljiTo1EcAgg4MjKBzM92tqyuHoANz9g7Ivyfff2fP\nVzULVQoWtElcldSCTjdnGGPa0m3uI7MLtyfo9bVD0IE5h4K+mJ/YrduXoF3Fi6CnikPQCUqN\nVsTK4hF0YM5ulpP5fdzM4fEPQfujRNC+JbsLOnLl4wrabRYVQXsUsU4OaxLHHI4iBd3cD/pY\nf0n45V2FhRAhmd3KWT53QtCO+RB0UtzO8teXGtyqpQJSSTficCT9MYxsgn5818++TLPeOx47\nFLRDjO0LOvJnUo9iEbQtd3JBrzk9RdCBOQX20dAdbl0xCNqxAsv5tAt6/VSpcUkkRRJBmwit\ncv38F6ksr7SLRek7tPyyIujIIOjQwHEOfuuLw41ZDZ1K0KGtcsmOoKWyvrI0P69u1K0kRNAh\ntQjMiKCTkkLQMfYqp+yuMfIIOsbniJmyJfPuRdBmMUUczOD/QsJUdRERdNTa707Qk7F3K+iV\nFZ9JXaCgE4KgpxMi6HFZ+gSdZJRSCDrGTuWWe7eCDp6AkownESK+oJO2GUEHB45z7G9X0AHh\nTGgB7plTdN3KxLM9qO7I8szcy3I/V39/q/M9pBLzIfxzb0vQi0GSCjr4EHcrRbOgrS+Ptjl+\nnE8xTAkE7Xx24FKIaDKvxLsT9K1qJp+NqW4htZgLEZAdQXvUIigngn4l2pGgI89dZBG0WV3Y\niqjCubtZjuarPne+n42aG/Y/Vu91CDqoWGN9GD1MYGCXZNEEbRzTrSS+oNe/x4UEd0u4riYr\nUs+NSqmCfl+8oekqjtWnBfZ02gRtUlYlzvHpUErE6q8WtNs74MK2fIKeKXHrgjbeFVEv6PZk\n34sAACAASURBVEgu88xSmXby+Y6gfXHWDIIOCuySypLAW9DxByq+oNd/BgmKPWlHs+JzpHf0\nXQr6bI71nfr/HePejANBTydE0H6BXVJFErRxTbiOpIL2r+5KRY439rsrmaBnHVyqoNs72f0R\n9X79kRoV+NHLe+9dV3sVgg4pdGeCdoiVaKSiC9pMPvEuZXXSz0dvv15be/4+d6ynOLRCywwV\n9OP3VOv5J7AasyF884cJ2n/vRdCJIgUFdkkVR9Ce54KLzJXnFSu/oHszo16W1CdoM/MstLRU\nWaRDZBa0t85nMyLooMArTn3nN7lrPu5QuewcAQX6Vjbk3MX2dG09XNPPHj4RBW3cj2i34iSy\nSIeILuhVNULQqUIFBfZ6+3BS9lSCTQnat7YBgh5mlRD03MEeSdDrP9rNFyeQRTpEZkF7f+rL\nJeigMhH01Ou6BW3VY3gxKxJbezuHoCMeWv03mfASQwf2+6DvbnbbFfRsRgQdFjiWoJfKiXn+\n5Bw2m6BX5lr4vGjWV8Mx/awz4x1ag5KyC/pb4+1G8wt6RXIELRVYSNALCvLHPaxncV619Rf0\nxFGXWtAzH4riCXp47WB4gZ5ZKhP5+o1xCP/8ZQo61kfTaCVsRdBeEg8TdFRD70LQ65ckqxL0\noHkRje+ZJdFPlQeXataVsnwYrhS075vDDgQdT1r7EvRSUeFikxD0/FSDVy3WjmtCQQ/HPr+g\nTybufUYtIfwL2KCg5/JNCjrufhWSG0GvGM+VRBb0+neeKGGXBZ2mBrP7ytSRFViVKGevgYK+\nVc1S79hsWtBmVQbnA3pyzw7eDwLz707QTp9z1gR1ojBBy3XMKNEoQ6w6xf9sG2GKY82XhP++\nT03S03nB6goE7X+grRK0+5gi6MDAbnFdxt3hY87KqA4sFrQq0uo3nihRPxni+XmiqJnzq1SC\njtimoEJ9BX0/dFLP37sjiqBXO3Vu0/rCvHyAoJMF9nvHRNARg3ZypBf0tJP3JOg1nE31e20e\n3S7V/N3v9Al6/em4lKBNaHdF9DuCHr8Uq7nL5YTt8CtLCMsR1WW2wgZHxcz5dOd53uMoWqm+\nFanM9f34aqoUIXolZBa0p60mcxUj6Fh78vQBGBI1TNAOO5In8Vo4k1RC0OE7rEsdzNx+OfU0\nqFpp/Bwu6Mup/jBxcvhJwv6dq8ZmMusmtBeCIWhXonxcWVNSCmVFO7/chaCnUhYj6MFp+o4F\nfWx16vKjsXs7g3bKEkXQQf0Vpa9XFbV5Qa8TXJzmeu1MqxOurquXPyK7bPLondwxjf1Z3sMo\nWrndLD/m2Pza1Y/5Wsx3NtWl1Xj6OeitCHqcBEGvDutUpqe8ihT0qk8AcUL28kgJevBvKkcM\nQafyc6ig698kbPrbpdOPnTmMw+wCF2lBO4jS5/Ojz2kdgl4d1qnMfIKO016nQoIHQEbQHnnW\nlWf6/+cFveZ4da9CLAIF3UxvuAr68e/cXAddnb5TXweNoJ2RF3SKc8pEgp7IFWXzOiIKeiaV\niKCjMy1gs/B6/4l3YxL2QqCgD88z6Ks5RKvSI5I08gvaSxuLZ2cWQed970fQCzFi9nHaVGUI\nenAELhxk4YJO2QmBgn7OQV8i39Uuyh4dJOjlDS5lIehUMYelLBXpNXwrlbv84cef4MkLtzTF\nCXriTC2qoJP2QaCgHyeNv+rdFoGgXUDQPsUiaN8MSbAdEp/HiQWdtgtCBd1cB21Ov5GqYw3h\nWURcQXt9Ro4o6KlSjWMk9wr4l6FX0CsiJhB0hAbH69r5FGtrqlHQg2cLY7XmcF2OHZ1gQSch\nhqBXlZFI0D7eQNA+pSgQ9EyMLIKeWPIVVdA6/Dw/aHZBv/smUNCpeyBQ0KfZy5m9QdBzm+MJ\nOs4VqasFHWevXiFo730hkqDDG7xa0K+hHS7NjfdO5pE8HV6aHb6FaTxbDayT1l9UQdAuRBq8\nHQp67f4R2uAV0mn/DjO8RYSgnQpJlkMgQjfLQekvqqwtQ5GgJ/IsCNpn54q1d6kX9Lpw2xf0\nxLRrLemlgrYq6E5NEHTn8f2k8xdV1paRStA+Z3ZCgo63byHo5SCBLV6RffZWY4tvypsVdMCZ\nyriMdBlWEzzFEe8GdBMhZMoYfST0L28bgo44XtoFHSAdd0HHnToIyB4WCUGnTO8Bgran9he0\nmXnmVKafoFd+Yog5XAjaIYioN8UCKRL0sy5BNSpP0ImQH3cz+3Rik0NJHh+9BQQdt389qhD9\nHXjuY31Auc6CXgyCoJNj3n+CSkiY3gMEbY0YTdAep3YI2qMMaUGvGWa/asTKmzQSgk5NsKDd\nf1HFO4QEDsebY6VkBW2sWxeI3L26Bb0+1NR74VyxpQg6cGYwL0b6g8oGBL3iF1V8Q4igSND2\nzYuCFtajpTjhtwjHuSQRQTsEcT3hXw6WlC0L+hH6Yy0KhyVQ0Gt+UcUzhAxygl4+/BG0RxlT\nJQbt4M4TJy5ROsWuFYlWQSvzc/B33+UJetUvqviFkCGZoMfPP121QtBTE6MbFXSMKigStFOQ\ndzcZ5yyryo+D1nq5gaAHWdb9oopXCBnEBN3prDVOcRC0c6/F7l3NgvaJ4yRoj57vj7pwbyUJ\npU7QgZQnaLW/qBIYMa2gX29qjiUMtyFoSxEbEbTTbYuWPoSlBEEnS+5FoKD1/qJKUERr/JiC\nnp2HXHx3mJjXQNBxwjgK2i1ZP4vTxMj4TkeO5ccAQSdK7UmgoPX+okpQyJiCnnbrZIGWFyIK\nOnrnliboT6757K53iJuqDYLWRoGCVvuLKkEhRQS9ogg3Qa+aCo3IXgX9eLjdIW45znCro8lT\ngKATpfYkQNDn76g1sYUQREbQ3gotTdARahFvVmoym9vbqX9TpiqNoPNQlqDfV3DEB0HPbkLQ\n4xLyCTqEiZHOKGjpz0GqKE3Qt0IFHfRxeZxsyqcripgSscvH/OXCA0HQ/tj3Fo9hjQaCTpHY\nmwBBf5kemWsVM2QyQXsX4SZop/J3JGjfGIKCHoUw460IWoqyBH0/IWinZFkFPTvjEgcvQQfX\nw+G9aQuCtrcDQWehLEE3j5niWE62WtDz0yQrBZ1+PhNBB2Eb2m0IujQ/I2ifEEIkFPR6gyHo\n1fljCtqzRYHBuk8QdBbKE3Qi8gp6InpOQU/Nty5ayrjM1QaiVdD+EUQFbd31ls8XUrFiTmzX\nghZqfICg28vsCpyDDhL0zJmce6t8BW3NmFzQ69vnk3whewpBS+2GlvFE0DlA0OlqFTFmYYJO\nf7Aj6EDGZ+zL5wup6+IQH0GnhymOccyp6E61siYy7vntKbcg6LVFpxZ0QPkIem6DwysbBUHr\nCTETM5GgQ0Y/SNDJj3Y/QQfWxa3pQWXL7YbjHlzeHdPWpfN0sgIIOj1hgr58Heo72Z3/xavQ\nMIQYWxT0opSGB36ajkXQwYz2EASdgRUNkmp7iKBvx/cE9HHrv+o9Z73FFxYTRRa05SPxfL7l\ntgWhUtBBpXu2KCyiGTwfPRKrSf8pgg5LGkSAoO+VOVzufw9uvwdTxaxUcYIeHX5rS3EV9ERC\nBO1VOIJ+IOjwpEEECPrcuUv/0US99yiCnn++VtAzGWLgq7MIDnV/7lG46G44taJI/FhA0JGT\nBhEg6IP5zGvc4v6kSoGCXtek2IJOaxwEHQMEnZ2yBN17yy/oOuiCBD38nLwjQceYP8kqIP2C\nLs7PCHptCEmWD4gAQa/un3ndrBd0+1CZoIPqg6ATB34/Q9AhKQNB0MOYiQTtWZv5p5ZSzcQT\n41EJVxB0Ckzvn3jczrNI+/QGcG4SgpZn8YDYtKDnToVCUSjowKYi6O4zBO2fMJQgQZd0w/7E\ngl7LvG4QtCXvZMu9S9cg6Ax1GHUkgvZPGAqCHgbdkKBtsczE4/qZOkGH1GicdbrpnqUjaAQd\nmjAU4dMePSGmgs6E3rqgk/WqEkF3NgU3VYGgs9UBQUdLFw6CHgTduqC7GydmrqOjRdCfbVEE\nnVtACFocBK0mxFRQJYKen1H1E3QyAj6M+1dxtvEIOjSuQx1y908CELSaEFNBEfR69Ah6tmfW\nlp5bQGoEPd/XJYGg1YSYClqQoMV6UZGgY1lNg6DrCuSoA4KOli4cBN0POhfZpVbRar4TQftX\ncipjHLUi6O7jpY9qxeDYJsGmI+h+0G0I2u24kRW0Z7Togo6ktUxy1FAHBB0pWQwQdD/q9gX9\n2bpTQce5nhBBdx4jaK9kMUDQ/ahqBG05TEYb5gUtPE+mR9BRWq5B0I90a4vmow4fI2ivZDFA\n0P2oWxL0RKxtCVo8n3vx+Q2UR9DjWTIE7ZUsBgi6F3U2MIKeAEGnqkS+M5XeIwTtkSoOCLoX\nVaegx4UiaASdMOzoka0e+bsnPghaTQh7VEWC7hyd6wQ9e9qThCBBi09eOxevwEAIWhYErSaE\nPep8YIdqRa25mVStg6Cl58mEBZ28eZnmf4eVyBsVQfunigOC7kXVJeiXonco6MXS9iHoPCDo\nGKnigKB7UbUJulU0gvZIEAqCfiBo/0SxQNDdqAtxcwi6VnTZgvY7+hF0OsZffiDotYligaC7\nUVUKevUZdKgwVxNf0MuXL6QXdIkCcsNJ0EV2D4JWE8IaVqmgJ8LMClp8ngxBF4PFyxPnCKWB\noNWEsIZF0F4g6MIYTz0j6FVp4oGgu2E3JejpUNKCDg1oOfoRdE4QdGCaeCDoFWERdJqAtqM/\nwlgEUqSB3EDQgWnigaBXhNUj6DoOgk5LkQZyA0EHJYkJgl4RVtHwzQrawW9xiS3o+dbZ80Sn\nSAO5YXr/Bg8ntxSAoiPcP1y5gl5C0fAhaP9wjijdCSVA0EFJYoKgV7BcLbGKqxN0SECdgt4x\nCDooSUwQ9AoQ9GS8sIDDzAg6L2NBW99Ey0PREe4fD0GHpIgEgg6JB/Mg6IAUcUHQK1A0fAsK\nk/8chqALwkHQZfa/oiPcPx6CDkkRiaIF7fKdo9JdpBBM5+9j9Nj2vBDU7XYIegWqBD0bCkFD\nCOb9p7dl5nkhqNvtEPQKtiNoYUIFPciNoHODoD1fjw6CXgGCngBBFwaC9no5AQh6BQh6ggSC\n1neo7AnLCFimoQpE3V6HoFeAoCeIK+jxN1RLOSAyCNrr5QQg6BUg6AkQdGEgaK+XE4CgV4Cg\nJwgWdC87gs4OgvZ4NQkIegUIegIEXRiWr2kRNIKWDOGDIkHr+sEPBF0YCNrj1SQg6DUs1gtB\n+5cweIygczIeUQSdpc0Ieg2aBC0WyYGogjajLeM0ytpfHkuCLrX7h+3Kvs8h6DUgaDsIujQs\nI7qL7h81evzRThYEvQYEbSdc0J38CFoBCPr1fLRjyoKg14Cg7US4pgRBqwJBv5+bqddEQNBr\nQNB25AWtqvkFYhnRvQr6vQ1BS4bwYqlegvVW1UUigu4FUdX8AkHQneeTpwwCIOg1KBK0KmIK\nevITJYIWBEF3nxvbSzIg6DUgaDsx1jWOPkgi6Iwg6N5zY3lJBgS9BgRtB0GXhm1E9/AdwNQ3\noxGuU/IDQa8BQdtB0MVhWam6Z0FnW7mLoNeAoO0g6OJA0IPnCFo2hBcI2g6CLg5LB+9Q0Pmb\niaDXgKDtRBT09CkLgpZkXtDFdj+C1hLCCwRtJ8rdqTtXNA0evZ9n/8C5HxC05WkGEPQaEPQE\nMb5CQdCaQNCWpxlA0GtA0BMICNrMvQgCIOgMIOg1IOgJEPQe2EH3I2gtIbxA0BNEE/T0WRqC\nzs77QhtVP+cTFQStJYQXCHqCKEdsX8HD3kTQ2TGNm8u18wNB6wnhBYKeAEHvgbLd3ICgtYTw\nAkFPgKChCBC0lhBeIOgJhAT92bTbnoakIGgtIbxA0BNEaTiChuxYdrqsIOg1IOgJ4jR8sCAR\nQYM4CFpJCC8Q9AQIGsoAQSsJ4cd8xdRWOznpBW36m/bb1ZCUXQv63/epuY7ydP6XKkRiELQd\nBA1lsGNB3w/mwzFJiOQgaDsIGspgx4I+m+r32jy6XSpzThEiOQjaDoKGMtixoCtzfT++mipF\niOQgaDuRWj5zdCBokGDHgu4tZphf2ZC/XyZA0HaSCNp2UbSxpwSIw44FzRl0uSBoKIOZCz2z\nIDsHfbk1j5iDBhcQNEizY0E/jp2rOA73JCFSg6AlQdAgzZ4F/fh3bq6Drk7fXAcNyyBokGbX\ngtYUwg8ELYkZP0TQkBQErSSEHwhakmlB09OQhl0LmqXesAYEDdLsWNAs9YZ1WAQ9XLACEJUd\nC5ql3rAOBA3S7FjQLFSBdSBokGbHgmapN6wDQYM0OxY0Z9CwDgQN0uxY0Cz1hpWY8SMEDSnZ\nsaBZ6g0rQdAgzJ4FXfpSb7W13ixm9ABBQ1J2LWhNIfxA0KJYBN0+pKshDQhaSQg/ELQoCBqE\n2bWgC1/qrbbWmwVBgzA7FnTxS73V1nqzIGgQZseCXljqbbp4hkgOghYFQYMwOxZ0CQtVZmum\nt9ZbBUGDMGbicS5Y6r0OBC0JggZhdixozqBhJQgahNmxoEtY6o2gRTGD/+8ndDUkYngukBmW\neq8DQYtiev8+T+hqSMSOBV3AUm8ELQuCBmH2LGhNITxB0KIgaBAGQesI4QmCFgVBgzC7F/TP\nwZjTJWmIhCBoURA0CLNfQbeXPj+/KZy9iENFz9hB0KIgaBBm54I+m/P98bidzU+KEOlB0KIg\naBBm54KuTHN93d0cUoRID4IWBUGDMDsX9GuJN0u9wQGboOtndDUkYueC/nodciz1hmVM5293\nI10NidizoE/fPxfz+/fwfi5wqbfeSm8XBA3C7FnQ73s9G1OVt9Rbb6W3C4IGYfYr6Mf1+vNz\nOjVfFZ5n/ayiZ+wgaFEQNAizY0GrCuEJgpbFvP90t9HVkAoErSOEJwhaFgQNsiDohUvsooRI\nBoKWBUGDLAgaQYMzNkE77EAAnhjLo4wg6HUgaFkQNMiCoBE0OIOgQRYEjaDBGfOwdCyChmQg\naAQNziBokAVB6wjhCYKWBUGDLAhaRwhPELQsCBpkQdA6QvgyXTXFld4uCBpkQdA6QviCoEWx\nCpquhmQgaB0hfEHQshgEDZIgaB0hfEHQsiBoEAVB6wjhC4KWBUGDKAhaRwhfELQsCBpEQdA6\nQviCoGXh1nUgCoLWEcKXyaoprvOWQdAgCoLWEcIXBC0LggZRELSOEL4gaFkQNMhiRg9ygqBX\ngqBlQdAgC4JWEcIXBC0LggZZELSKEL4gaFkQNMiCoFWE8AVBy4KgQRYz+J8XBL0SBC0Mt64D\nURC0ihC+IGhhEDSIgqBVhPAFQQtDv4IoCFpFCF8QtDD0K4iCoFWE8AVBC0O/gigIWkUIXxC0\nMPQriIKgVYTwBUELQ7+CKAhaRQhfEDRAySBoFSF8QdAAJYOgVYTwBUEDlAyCVhHCFwQNUDII\nWkUIXxA0QMkgaBUhvJmqm+Y6A4AjCFpFCG8m6qa5ygDgCoJWEcIbBA1QMAhaRQhvEDRAwSBo\nFSG8QdAABYOgVYTwBkEDFAyCVhHCG2N/ornKAOAKglYRwpu+oI2u4QSAMHQd0Qh6LcMz6Kei\nNVcZAFwxvX+5QdBrGU9xNIrWXGUAcAVBqwjhjW0O+k/RmqsMAK4gaBUhvDHWx/z2NEARIGgV\nIbyxCxoAigBBqwjhDYIGKBgErSKENwgaoGAQtIoQ3iBogIJB0CpCeIOgAUpG1cIGBL0WBA1Q\nMghaQwhvEDRAySBoDSG8MdaHAFAGCFpDCG8QNEDJIGgNIbxB0AAlg6A1hPAGQQOUDILWEMIb\nBA1QMghaQwhvEDRAySBoDSG8QdAAJYOgNYTwx1geAUAhIGgNIfxB0AAFg6A1hPDHjB4AQDGY\n9x8FIOjVIGiAgkHQGkL4g6ABCgZBawjhD4IGKBgErSGEPwgaoGAQtIYQ/iBogIJB0BpC+IOg\nAQoGQWsI4Q+CBigYBK0hhD9m8B8ACgJBawjhD4IGKBgErSGEPwgaoGAQtIYQ/iBogIJB0BpC\n+IOgAQoGQWsI4Q+CBigYBK0hhD8IGqBgELSGEP4gaICCQdAaQviDoAEKBkFrCOGP6f0DgKJA\n0BpC+IOgAQoGQWsI4Q+CBigYBK0hhD8IGqBgzEPR0Y2gV4OgAUrGKDq6EfRqEDRAySBoBSH8\nQdAAJYOgFYQIwHT+AkBhIGgFIQJQ9S0vAMQFQSsIEQCCBigYBK0gRAAIGqBgELSCEAEgaICC\n2a2g/32fTM3p/C9VCAkQNEDB7FTQ94P5cEwSQgYEDVAwOxX02VS/1+bR7VKZc4oQMiBogILZ\nqaArc30/vpoqRQgZEDRAwexU0MZMPYkWQgYEDVAwOxV0UWfQumsIAN7sVNBnU11uzaMC5qB1\n1xAAvNmpoB/HzlUch3uSECIgaICC2augH//OzXXQ1el789dB664hAHizW0FrChEAggYoGASt\nIEQACBqgYHYr6IKWeuuuIQB4YxQd3iz1Xg+CBiiYnQq6pKXeuisIAP7sVNAlLVTRXUEA8Gen\ngi5pqbfuCgKAPzsVNGfQAKCfnQq6pKXeuisIAP7sVNAlLfXWXUEA8Gevgi5oqbfuCgKAP7sV\ntKYQASBogIJB0ApChKBpAAEgLpqOb5Z6ezB/jSAAbJmdCrqYpd4IGqBgdiroYpZ6I2iAgtmp\noItZqIKgAUpG0QHOUm8PFI0fAMRG0QHOGbQHisYPAGKj6ABnqbcHisYPAGKj6ABnqbcHisYP\nAGKj6ADXs9TbdPEOIYLy6gFACIoExEpCD5RXDwBCUHSAI2gPlFcPAEJQdICz1NsD5dUDgBAU\nHeAs9fZAefUAIARFBzhLvQEAuijyDwtVAAC6KPIPS70BALoo8g9n0AAAXRT5h6XeAABdFPmH\npd4AAF0U+UfPUu84IQAAwlDkH1YSAgAoBUEDACgFQQMAKAVBAwAoBUEDAChFdCWh8z35ETQA\ngKSgfxA0AMAKJKc4rtX8TUYjhAAAKAfROejr/ALvGCEAAIpB9kvCn879khKFAAAoBa7iAABQ\nCoIGAFAKggYAUEoOQc9fYhclBADA9kHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpRepkdAADo\nFLQbaioiB03eAzR5DyRrspq+VFMROWjyHqDJewBBFwhN3gM0eQ8g6AKhyXuAJu8BBF0gNHkP\n0OQ9gKALhCbvAZq8BxB0gdDkPUCT9wCCLhCavAdo8h5A0AVCk/cATd4DCLpAaPIeoMl7AEEX\nCE3eAzR5DyDoAqHJe4Am74HyBQ0AAH0QNACAUhA0AIBSEDQAgFIQNACAUhA0AIBSEDQAgFIQ\nNACAUhA0AIBSEDQAgFIQNACAUhA0AIBSEDQAgFIQNACAUhA0AIBSEDQAgFLyCPrnYKrzvXl4\nrt4P/7a/qtPbWgSLTe4kKITFJv/xr6wzhMUmX7+M+bplqVsilpp838uxPNURYWQ5Ps6mpqpb\ncGweHtrtV/OsTm9rESw2uZOgEBab/Me9KkrQi02+7G6Ub1WboKA3JXuTpzoikBzHx9V83et3\n2K/6BKq6Pq6V+ddsr56D2ttaBItN7iQohMUm15xMSYJebnL1t/V+MuectYzKYpO/msaei9+x\npzoilBzHx6kNWo/g2Vz+Hv2a70fduONzULtby2CxyZ0EhbDY5GZTQQ12aPJvY6u7qbJVMTaL\nTTY72bEnOiKYjP1Wt+Vk6o8+V3Oqn58fb1t9thbFdJM7Ccpirsm3rqzLYbrJX+aatWbJmG7y\ncw6roPekJ8Mmz20NCROjEC/u5th/f70O326LO3hnmtxJUBSzTT6aW3FjPNvkg3l8V81n4bKY\nafL3c4qjnE/DLaMmz2wNId/x8VN/EBi0pXBBzzS5k6Ao5pr8bX7LG+OFHfvUfJOUq2qpmBvl\nn/pbwuonU82SYWvy1NYQsh0ft6r5KLQnQc81uZOgJOaa3HwGLG2Ml3bs+kvCr9JOJ2d37O/m\nkobCWmxv8sTWIHIdH/eq+TC/J0HPNrmToCBmm3yoL0kqbIwXd+x6DvpW1AWkC03+qac4/t6T\nyjqFtjfZvjWMXMfHsd1HK+ugVkUKerbJnQQFMdfkr2Y6p7AxXhjlMs88Zpt8MPWM+72w9yR7\nk+1bw8izq9wOx/bC9fYLz9vrC8/eVRy3oq7iWGhyJ0ExzDfZvMlVvwQs7tjdZ2Ww0OQS35Ps\nTZ7qiDCy9NvlfbHCd3MadXlduf/+5re3tQSWmtxJUAoLTS5R0G479q2koV5qcns6WdKl3xNN\nnuyIMHIcHZ0ddLDo5jmo5a0kXGxyUQdtw2KTR483j8MoH+71hOxvluqlYLHJZ1PflOJc0MmW\nvcnTHRFGjuPjq3PqdGgevNr2Olz7WwtgsclfxZ1OLo/y8PHmWW7y9+527OeNKYpv8nRHhJHj\n+Oh+tm3vdfV5pf3f31oAi00u7/P+8igPH28ehyZfjjvbsZ+3dstTuyTYmzzdEYHR4hQDAACx\nQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAA\nSkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAA\nAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQAABKQdAAAEpB0AAASkHQ\nUBDmxeTrotUBCIQdFgoCQUNZsMNCQSwJGEHDtmCHhYJA0FAW7LBQED0B/xxM9dM+PFfmeGtf\nP5vqu950ORlTneWrCLACBA0F0RX0qZmMPtYPj/Wj6l6/3mz90/Z3O1eNoUE1CBoKovMd4cUc\n74/70Vwej9/64VctY1M/+jGH+tFv/QL7P6iGHRQKoiPok/k7Y37czal++K9+VNWv/3t0z7MR\nNOiGHRQKoiPczhV3QyG3f2+X7yOCBt2wg0JBrBD0cfaCaQAVsINCQfQEbXn4EfSXOfxcbgga\ndMMOCgXREe6p/nqw5diZg36lah4haFAOOygUREe4v6a6Ph4/9ZeEP/W1G+f2Ko5XGHRfhQAA\nAOFJREFUqvrrwitz0KAcdlAoiK5w20nm6vboXQf9SnV+zlD/y1RTABcQNBTEcCWh+bo1D/90\nfLo9el8Sfhlz/HepT7AB1IKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqAB\nAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKg\nAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSCoAEAlIKgAQCUgqABAJSC\noAEAlIKgAQCU8h8FvCkBB2CtCAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"Diferencias de Box-Cox(Reserva) tras una diferencia regular\n",
       "             de orden 1 y otra  estacional\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(rests.d12,\n",
    "     main = \"Diferencias de Box-Cox(Reserva) tras una diferencia regular\n",
    "             de orden 1 y otra  estacional\",\n",
    "     xlab = \"Fecha\",\n",
    "     ylab = \"Diferencias\",\n",
    "     ylim = c(0.99*min(rests.d12), 1.01*max(rests.d12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tAugmented Dickey-Fuller Test\n",
       "\n",
       "data:  rests.d12\n",
       "Dickey-Fuller = -3.8801, Lag order = 4, p-value = 0.01748\n",
       "alternative hypothesis: stationary\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Probamos el test de raíz unitaria\n",
    "adf.test(rests.d12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En sentido estricto, dado que p-valor > 0.01 la serie no es estacionaria. No obstante, aún siendo un valor de referencia, éste no deja de ser es valor arbitrario. En est caso el p-valor es ligeramente mayor que 0.01 por lo que va a aceptar que la serie es estacionaria. No obstante hay que tener presente que es posible que está aproximación puede disminuir la eficiencia de los modelos de predicción que se van a aplicar a continuación. Dependiendo del error final del modelo, habrá que evaluar si es necesario aplicar alguna transformación adicional a losd datos hasta que el p-valor sea inferior a 0.01."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3.2 - Modelos ARIMA y SARIMA "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez transformada la serie original a una serie estacionaria podemos aplicar un modelo de predicción tipo [ARIMA](\n",
    "https://es.wikipedia.org/wiki/Modelo_autorregresivo_integrado_de_media_m%C3%B3vil).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series: rests.d12 \n",
       "ARIMA(1,0,0)(2,0,1)[12] with zero mean \n",
       "\n",
       "Coefficients:\n",
       "         ar1     sar1     sar2     sma1\n",
       "      0.4404  -0.1303  -0.3088  -0.7999\n",
       "s.e.  0.0872   0.1299   0.1198   0.1673\n",
       "\n",
       "sigma^2 estimated as 4013187:  log likelihood=-1082.11\n",
       "AIC=2174.22   AICc=2174.75   BIC=2188.12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Probamos con la función auto.arima.\n",
    "auto.arima(rests.d12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El modelo obtenido es un ARIMA (1,0,0) SARIMA(2,0,1). Se comprueba que el modelo  AR(1) para diferencias de orden 1 junto con un modelo ARMA(2,1) para la estacionalidad (lag =12) es el modelo que menor [AIC](https://es.wikipedia.org/wiki/Criterio_de_informaci%C3%B3n_de_Akaike) proporciona."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3.3 - Diagnóstico del modelo obtenido"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analizamos los residuos para detectar cualquier indicio de no aleatoriedad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAAAAAP9NTU1oaGh8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\nA4QSaIBQAg0QSqABQgk0QCiBBgj1f6dgjcYG2RPKAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"p values for Ljung-Box statistic\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# La función arima empleada pertenece al package stats.\n",
    "fit <- arima(rests.d12, order=c(1,0,0), seasonal = list(order = c(2, 0, 1), period = 12))\n",
    "tsdiag(fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La función de autocorrelación se mantiene en el intervalo (-0.2,0.2) lo cual indica muy poca correlación entre los residuos. Por otro lado los p-valores del [test de Box-Ljung](https://es.wikipedia.org/wiki/Prueba_de_Ljung-Box) (H0: residuos independientes, H1: residuos dependientes), se mantienen con valores superiores a 0.05 por lo que adoptamos la hipótesis nula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5.4 - Inversión de las tranformaciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAM1BMVEUAAABNTU1oaGh8fHyM\njIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD/AAD///89ODILAAAACXBIWXMAABJ0\nAAASdAHeZh94AAAgAElEQVR4nO3dDVubyhaAUaIe21rr9f//2ltja/PBxzBsYANrPc85jZph\nmJC8jTHY5h2AlJq1dwCAdgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl\n0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgIN\nkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJ\nCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRA\nAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRA\nUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl\n0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkJRAAyQl0ABJCTRAUgINkFR9oH9+e2o+PD3/DNwf\nAP6oDfTbQ/PPY+guAfChNtDPzenH6/nSr5dT8xy3QwB8qg30qXn9uvzanHqnAKCitrWBvpqr\nf2I/hwSoSeESz6ArpwDYkQUD/dycXn6dLw2+Bi3QAEsG+v3x4pWVh7dZpgDYjyUD/f7z+fw+\n6NPTt4H3QQs0wLKBzjQFQHYCDZDUsi9xlJ7qLdAASwZ6xKneAg2w8NvsSk/1FmgAJ6oAZOVU\nb4CkPIMGSMqp3gBJOdUbICmnegMk5UxCgKQEGiApp3oDJOVUb4CknOoNkJQTVQCSynOq98R/\na5w0HD0I4hk00Rw9COJUb6I5ehDEqd5Ec/QgiFO9ieboQRBnEhLN0YMgAk00Rw+CONWbaN4l\nCUGc6k00gYYgTvUmmkBDECeqEE2gIUieU70jpiADgYYgnkETTaAhiFO9iSbQEMSp3gRrHD4I\n4lRvggk0RHEmIcEEGqIINMEEGqIINMEEGqIINMEaxw+CCDTBBBqiLHomYfG/C+sBvmECDVEW\nDPR3gT4EgYYoS77E8Xrq/yWjAVOwPoGGKIu+Bv3af4J3xBSsTqAhyrI/JPx+8fuSZpqCtQk0\nRPEuDoIJNEQRaIIJNEQRaIIJNO4AUQSaYAKNO0AUgSaYQOMOEEWgCSbQuANEEWiCNe8O4NE5\n/lEEmmACjX+WMopAE0ygEegoAk0wgUagowg0wQQagY4i0AQTaAQ6ikATTKAR6CgCTTCBRqCj\nCDTBBBqBjiLQxGq+/sdhCXQUgSaWQPP7+LsHxBBoYgk0Ah1GoInVXPyfY/L7ssIINLEEGoEO\nI9DEEmgEOoxAE0ugEegwAk0sgUagwwg0sQQagQ4j0MQSaAQ6jEATS6Bxtn8YgSaWQCPQYQSa\nWAKNQIcRaGIJNAIdRqCJJdAIdBiBJlZLoB3OgxHoMAJNrObqj7vLHIBAhxFoYgk0Ah1GoIkl\n0Ah0GIEm1n2g/ftHR+MfbQgj0MQSaAQ6jEATS6AR6DACTSyBRqDDCDSxBBqBDiPQxBJoBDqM\nQBNLoBHoMAJNqObmz49LDufBCHQYgSaUQOMXZsURaEI1LRcczoMR6DACTSiBRqDjCDShBBqB\njiPQhBJoBDqOQBNKoBHoOAJNqLtANw7n4Qh0GIEmlEBzf64StQSaUAKNQMcRaEIJNPcnK1FL\noAnVFmjH81gEOo5AE0qgEeg4Ak0ogUag4wg0oZqbS36x2fEIdByBJpRAI9BxBJpQAo1AxxFo\nQgk0Ah1HoAkl0Ah0HIEmlEBz/wuzqCXQhBJoBDqOQBPqJtB+r9kBCXQcgSaUQCPQcQSaUAKN\nQMcRaEIJNAIdR6AJJdAIdByBJpRAI9BxBJpIzc1lgT4ggY4j0ES6CXRz/1l27/a98NQTaCIJ\nNAIdSKCJJNAIdCCBJpJAI9CBBJpIAo1ABxJoIgk0Ah1IoIkk0Ah0IIEm0nWgm5bPsnsCHUeg\niSTQCHQggSaSQHN5uB35iQSaSALN7en+TCDQRLo6co1AH5FABxJoIgk0Ah1IoIkk0Ah0IIEm\nUtP6geN5JAIdSKCJJNAIdCCBJpJAI9CBBJpIAo1ABxJoIrUfOcfzSAQ6kEATSaBp/y6KKgJN\nJIFGoAMJNJEEGoEOJNBEEmgEOpBAE0mgEehAiwb657en5sPT88+5pmBdAo1AB1ow0G8PzT+P\ns0yxhMz7tj6BRqADLRjo5+b04/V86dfLqXmeY4olZN631XXdOG60AxHoQAsG+tS8fl1+bU5z\nTLGEzPu2OoFGoCMtGOim6fogbIolZN631Qk0NwfboZ/EM+ixMu/b6gQagY607GvQL7/Ol7wG\nvVcCjUBHWvJtdo8X7+J4eJtligVk3rfVCTQCHWnZ90E/n98HfXr6tuX3QafeuZUJNAIdyZmE\no6XeuZUJNAIdSaBHS71zKxNoBDqSU71HS71zKxNoBDqSU71HS71zKxNoBDqSU71HS71zKxNo\nBDqSE1VGS71zKxNobg+2Yz+FU71HS71zKxNoBDqSZ9Cjpd65lQk0Ah3Jqd6jpd65lQk0t8fa\nsZ/Cqd6jpd65la0faEdnbQIdyaneo6XeuZUJNAIdyZmEo6XeuZUJNAIdKU+gm0vzTBEj9c6t\nTKAR6EhO9R4v996tSqDZXqAz76FTvccb2LvcOz8vgebuCKQ/JJl30Kne4wl0J4FGoCM5UWW8\n/r3L/QL6zAQagY7kVO/xBLqTQCPQkTyDHk+gOwk0Ah3Jqd7jDQQ6+d7PSqAR6EhO9R5v6NWZ\n3Hs/p86VC/RxbC7QqffPqd7jCXSXDIE+7q2fhEBHynMm4cJTTCDQXQQagQ4l0OMJdBeBpuVY\nJz8kqXdv+UB/f2iap5dZp5hZ3941A1/fN4FGoEMt/j7oPz8p7H0TR+6bTKA7CTQCHWrpQD83\nz2/v77+em+9zTLEMge4i0Ah0qKUDfWrO7697ax7mmGIhPbsn0OO+EL8Hx731kxDoSEsH+u8T\nnC2f6t2ze83A13dOoBHoUEsH+r+/gd7wqd4C3UWgEehQiwb66dv3l+bH74tvz1s+1Vuguwg0\nAh1q0UB//XNWTXPa8KneAt1FoBHoUEu+D/r19fv3p6fzjwqfe/uc+yYb7lDy3Z+PQCPQoZY/\nUSXJFFMIdAeBpu0A5D4mqfdOoCsIdAeBZnOBTr1zAl1DoDsINAIdSqArdO1eM/D13RNoBDqU\nQFcYCnT2/Z/N0NtbltiDo972aQh0JIGuINAdBBqBDiXQFQS6g0DTdvunPiapd06ga3TsXjN0\nhd0TaAQ6lEDXaN8/gRZoBDqUQNcQ6HYCTevtn/mgZN43ga4j0O0EGoEOJdA1hu6E2fd/LgKN\nQIcS6BoC3S5DoA974ych0KEEusbgnTD7AubRs2qBPgqBDiXQNQS6lUAj0LEEuoZAtxJoNhfo\nxLv2QaBrCHQrgUagYwl0DYFuJdAIdCyBriHQrQQagY4l0DUEupVAM3gSVzaXe/a/1faik0DX\naNu/ZugK+yfQCHQsga4xGOj0K5iFQLPpQCcstEBXadlBgd5roA95LKsJdCiBriLQbQQagY4l\n0FUEuo1AI9CxBLqKQLcRaLYd6HyFFugqAt1GoOm6tdLeiAJdIe3R/Eug2+w10Ic8mLUEOpRA\nVxHoNgKNQMcS6CoC3Wb9QDdzTCXQY2ws0Lf7la3QAl1lMND5lzADgUagYwl0FYFuI9AIdCyB\nriLQbfYZaP8O7ShdN1bSG/Fut5IVWqCrCHQbgUagYwl0lfsdvP1M+iXMQKAR6FgCXUWg2wh0\nuO3djQQ6lEDXudtDgc4S6Oi55nhr9ZjJN2brgU5WaIGuI9AtBDra9t5A0rnDSVci0DWSHswL\nAt1CoKPtKNBJHxECXSPnsbw0GOgNrCGcQEcT6Lm17FWqQgt0HYG+17diga6ce2t3I4GOJdB1\nBPqeQEdb9eeTdQQ6lkDXiQh0/lWOI9DRBHp2Al0j57G8JND3BDqaQM+tdacyFVqg6wj0P83N\nn33XmdlMgV7rUO0q0CmXItBVMh7KawL9z9+3Ggh0sBX/aqgm0LEEuo5A//P3rQYCHUygZyfQ\nVTIeymu3e3i/x4cJ9FdGetezzGIFem17CHSmQgt0neFADy9ie2chtPqKokAHm2M1cxPoWAJd\nJyDQ2zsJoZ1Az2T4haOEBDqWQFdqej/s+NTN1zewzAJfHRHo+Ik38Vi4lOAHEWN07FKeQgt0\nJYH+6+s9HPsM9HqvMwj0/AS6SsIjeUug//q7iv6X1AW6auJNPBYuCXQsga4k0H80LZd6rzan\nuQK9xqES6AUIdJWER/KWQP9RuAiBrpt4a3eSfQQ6T6EFutJwoIdWsU6gw+esCPR86xbotQl0\nLIGuJNDjNngV6NkWvqNAN3cXNiHB61xjCHSVfAfyjkCP2+D1M+i5Vi7Qa9tWoDt3SKDXnmIq\ngR63wZuXOGZaukCvbSeBTlNoga5UEOiBZcyXqYFZV9ne7WvQ8yxdoNcm0LEEupJAj9ve3Q8J\nZ1n7DDVdKdBNy6UtEOhYAl2p6fmo/7MXXzxyoGdZvECvbS+BzlJoga4k0OO2dx/oOVY/W6AX\nP1S7DHS6tQh0nWzHsUVJoHvXsU6gw9/hNiHQMyxfoNcm0LEEulbT+cHgp7++dvBAx69/N4Fu\nvbm2QKBjCXQtgT5vr+aKF88OY3dnjpgK9Ci7CXSSQgt0rY0GOnjOiYHeQEwFehSBjiXQtUoC\n3beQ4wb65uX7wB0S6LUN7Gu2pQh0nWzHsY1Af25v/BVvx8QluiqmZU0R6CJD+5psLb27k6LQ\nAl1LoD+3N/6K92Oi9qkmpkO3yCqBLrpzZSTQwQS6lkB/bm/8FTMF+vzsve8pfHPz5zJyBHqG\nOCR7ZAt0nWSHsVXZY6h7JU3/l2cSHOjyjfUGOmqnRgd6OL/N3YUlpAh0zUtP2wp0/94I9JpT\nTCbQ75sPdNNyqesqhwt0UzPzngKdotACXWtioJv+L89FoC+u3XR9cL/JNQO9zsOh7iU4gQ4m\n0LWajsvd12r5gkCP3lDBJGUbu71W+6ijBrry/jk4INVDW6ArpTqKXQaSM/Alga7cUMEkRRu7\nu5JA388p0GsT6GoCLdAzaXo/XHIPxk69q0BnKLRAV9tioKt+8NO7uYqrto4K2a1JgR7Yr0MF\nunrVAh1MoKsJtEDPZO1Al921B0ZWXmFJowO9fLEFulrhvbjja/MEemh7At0z38B+LXinXDnQ\nTcflkUPrrrAkga6U6ih2EejIQIfsl0DHTz9ucoEOJtDVBFqgZ3I71bKPh6bno3Fj666xnPGB\nXrzQAl0tItDRKx06O1ege6YT6NbZRk0u0MEEul7hY7e3R5sO9JhNDf91FrBj0wI98Lnl7pR3\nMwl03PhR27rtsUAvNkWAfIEefJfzMQJdsimBLp6+8jBXX2Pe8aO2JdCrTRFAoOuumyHQAy9n\ntH3uKIGeNPumAj28qf/dfijQS00RYEqgRzzXG2Gwv03olLGBDtgxgZ5j9jHTzx7oGf6FtB4C\nvdoUAQS67roCPSBboEfMv/dAL15oga7X3F3ov1rbJwW6bnP9W6jNRH+bFrtXrhro4r+6ykdX\nbqt9tEDPMCThFAGOHuhRGyoJ3eQ9iw900/vVmUx6kWHCkJ5xpVsT6GACXS9loIdfblkn0CW3\nlkB3zbN6oEvDKNDBBHqCsh4I9PW1MwS6q0K9n9lUoKt/r+y0728EOphAT1DYg7YvzxLoZmiL\ng1cYP934a/cMm7prAn0xom53Jx6duQMd+vvMjxTon09T92RwinwCAh26VIEu3Y5Aj5h8zF5s\nKdAFW7oP9NKFnhro5+avqD26myKvlIEeDKBAd1/h7vNbDXRTu7sCfWnzgf7X55ewXXoX6ErL\nBnrkdgS6VFCgq/a3f8zwFoNeBukZu2igb3K8vUCfmh/vj82vX4/Nz7BdehfoSgJdup2qQC91\ntxTo7isFBrpoQ1sP9McrG99+P3t+bR7Ddul9sUfCVAJdc/WJ30SXzDG0mc6vTwt0zC3bupWa\nG1ugp+/GHgL90nx/D333ywEC3fR9sVoztEmBHvi6QE/4aumUZVfquhlWDPT/7j81u4mBfmp+\nvP9qHt5/HjLQw0Xs+vIOAj12M5sI9N1XxgQ66NldWKArdmfqX0Fhge64LQMDXbadrQf65SPM\njx8/JPwvbJfeY6s1p1yBHs7TzgNd/LrR3gNddr+smEagNxbo928fH/3XNM9B+9MyRWICXXH9\nHQc65LYV6HeB/jI10PPYVqAr7rWzBrp7mwI99PXeQPdvtymYuMT0QBffDuOnab/CyAkLrtVR\n4qjbuHgrWw/0W9iOdE6R2YEDXf/YH6xctdJO9Hz52IEevn5rNf893Y0KdNdtKdAjhzSPoSeo\ntE2RWVmgW67Q9H1x2t70bVKgh748MdABN+7Ac9RRm1gk0JfrnjnQy99/WwK9bKEnBvqhaZrn\n0HNU7qbIbKlAj7zfC3Th9Ya/Vh7owvvCoI4tjNhw/bdnZa89tH1ia4Eu3cjWA/3+69vvRj98\nC36pQ6CHNtB/ra7rR0WkZiPHCPT0Gzcy0CP3puKu+PX22jF3rcIn6h0Pm+pbuOm43Gfzgf7t\n1/OpCX6pYyuBLrzHTA900Q0yGOiohlRtQ6DLbCrQt1MFBbqr9pMC3VTdMP+7v7y1QP/2/Zi/\nzS5XoIe3eZhAD2ymPNC9uW790vqBrnmiWH7tpveD3IEu/hv8wg4C/fkqx4+Q3emYIq2lAl1y\nrV0EetLOFQZ6xBdvr9o9tOKxX7IDBTP3XTM+0D13s6Z4vqGrdd1RpwT6auykQC9a6KmBPtf5\n9Pwran9apsgsaaA7ri7QY3dg64EetTejn/7e3zrzBrrrifWIOUc/BDYe6I93cfx33Hdx1AZ6\n7JOcdIGecrfZeaAn3rydo0s32/8tQNXUHddquVvHBLrree6UQF9tc8QmNh7o5jH0pY22KVJb\nLNAFV6sL9LS7e82QXQZ65LdEhdMXfaH3evGB7juQpT+JWjHQ5wt1gf5fy+dmNzHQT7G/g6Nt\nitQEevyQvQd62r03ONBjdmZkXduuHhvo9rVU3bzXf4EeJ9Cxb95onSK1rIFuv3proKfe38cO\nKX1wVth1oEe/AjFm1JirFh7ICTPNG+hxd/mNB/qhmeW3cWwm0BXpvPtEwWKLvi1bMNBTxgyN\nDXnc921kzN8Q/UeudNAY+QM96ZWGkpk6D2P9xE3PR/3aAr1koScG+u3pccSPCH9+ezr/A7NP\nQ2eH7yzQ/Y8agZ687buhEwLd/2S4JNAztatoq6U7XD3B1/WC/xpq2i623qxL3WE/bTzQzT+D\n494eLq7d/08YCnTL9Ue9NiDQ1RPsK9AjdmZUoKc9RFt2s/XgtT1QJiZrpAMF+rk5/Xg9X/r1\ncur/Df8C3XL9mQK91B1+z4Ge8LrCiJElW91qoJv2gyfQC6721Lx+XX5tTnNMsbxFAl30PWVd\noMv2v3eqkYMEunTyMV/rvkrxvowq+cRHaNvt2nYHiQn0pJ1ty/I+A331JPv+GXfTjHk6nsWC\ngR64YtP7Ydd2BLr36wLdcc2FAt12MAR61JCXp4+cPhWc6+0ZdPvHiwW64ynKwQI9uP3xgZ7y\nusKYgcX3lKp9GRPoqQ/Q1gdE2908ItDTdrY1y8sVemqgHz+f7zan4UI/N6eXz2t5Dbrvi53D\nZwr0Qvf40kCHPP2cLdCFCVwp0IV/fVRu/eKqswT66v/3Vyy+Aw3MNdK2A/29eXz7OFrfm/+G\nBz5evIbx0Pv+6e0EuuzZRG88y58XjXn0zhjouqOzmUC3RaF/+PECXX7Vsi1cpLnzwVEZ6Kn7\nuu1An5q3z79Oi/5O/fl8fh/06enbbt4HLdBjR+0v0GWfKjPqu6SyL88Q6OnaHwIt98b7YyrQ\nI4acX94oDnTVFMktGegx3RHo6s0HBLp2EVUFHvjyhgL9cSE40JNX1X6C92KFnhjohz/PoF+b\nh7Bdel/4vjLNBgN9V6BFnpMkCfS4oyXQM1kq0NMXte1A/3kN+uXUfA/bpfeF7yvTTA/0iGSl\nCPTE9uwu0K1bnCfQ/V/v+GLKQN99G/f3Yvf9uCbQAWvadqDfn4pO3Z40RW5lbznqfc48S6D7\nSpI70PVHvyDQ4w5W0XPjac9bx406XqDv7zcj9jNiSR2/w26pQk8N9Pl90M1T8K/t31Kgi/Y1\nKNBjnhkKdO22NxHolvO5ugYX7srCD7qO71Pub94/6xToGYcknCLKHgK9yHeNew30xOet40Z9\n3Yh/jttlpietufxqUToC3X7dy7+NyvczZEUCvc4UYQ4W6Mn1XDXQIw/WhEDXraLwrnBzdxo4\niDkDXfWUeNTVgxbU8S8RLlToqYH+/vD+/uuheYj9h2MFuuMK0YFuCvfg6/r1R2bRQE96Xrte\noAtvm7aXAcbcNSZdLU75PaJ1WNwV+2060C8fj9jTxzcgoYUW6I4rjHiWNEegJx2X4rnWDnT/\nc7vSgzdboNv/kuz5q3Pkohcj0AUmBvqx+XF+D/SP2LdxCHTXFcqfr40J9CKP4fK/DGrnGbyd\nxj6XzBfomu9hsgZ6/Otr464/b6D7Cx2W74mB/jxJ5XnS974DU2Q3PtCl3yS3XWHNQE88KrMH\nuum4PHrDvYHu/8t2/GQjh8yz1boNTzVvoKMWtPlAPzUvRw50kRUC3TNNXaCnHpT1Az36oZ0w\n0BXSBvpzzq0Gui/C/wsr9OSXOF5fPn6184Ff4igi0GOeL20o0D2bFOiiSUfPK9Ajhrx8/Hzw\n28cT6JegHbqbYhf6H9RDy50/0AW3+ORjcrRAVyxDoAsHBV5t0P+u/rj/QvuYJIF+//75q/cf\nYk8lFOjOr3dcV6CHb6bizYYHeuGkVG12ncdczT/OkiXQPRHOE+h57C7Q/fGoD3RfSrYb6Mq5\nVgh07yYvNrNqI1MHer4ArRjo//V9cRyBXsZcge45i0yga7c6cKsUvOJ09dWmdO4DBnrGAAn0\nWfk/Gls9xR7ME+ivh/7kQI/ahzprB3rERpubPzs3VBDopvMA9W041KqTx1v4L5zuQHdGOFGg\nR/yjsbVT7MJ8gf78V20GRl1/LNCFWxoK9MAmL586D7Z+eHPVBHqS/138v+0rHZ+PKfTEQI/6\nR2PrptiHsEC3benuF0+2bvbggR6zzYGqlr4w1AzewAJdIX2guweMNzHQ4/7R2Kop9qH32+La\nQI969Lddu/ibdYG+/vrQL5D7ul7/Dtw8agS6SJ5Ad0Q4UaD9o7GFZg708LDWgQP9KP5qkRGB\nrptuuUC/vw/8ArnW8W37VH4EJhDoSfoC/eV+QEyhJwbaPxpbaEqgOx/mAt05aPTLSG1XHrp9\nx+7l7fWbMUdgAoGepOQJcdZA+0djC80R6JGvSgj02C1F3w0zB3pDD7mMgb78cue54TUmBto/\nGltIoAW67YgI9HhLf0Ow6UAf/h+NLRQX6H8fLhjoiAMi0DdbbN4FukbKQF98veNfMawzOdCz\n2NC9pVDv8966QA/eSJ2vXbdvom97mw/0uA3OFuj7fez9m3uGSadcJ4lVAj0Y25YsJwj003PA\nPvRPsRN9z3tTBro43qXGBLpqwrhAj9vX8Ru+uCzQo+UMdMsrGwkCHfvuutYpdmKFQHc3VqCL\nNjXH3fD2W6neb63C55x2nSQ2E+iIQk8M9Mfb7GawoXtLqfBAF9xG0wId/dxOoK+3uVygS/6y\n3dBDLmmgW95dt36g354eQ/8575YpdiIu0OX1mBLoq3MoNh/osZubL9C3t71Aj7Z0oEt/99H9\nDxPXD3Tzz/R9aZ9iJ7YX6Oh2LBro+9VUbGqJQPfeMaLn7JlnQw+5xV+xqQ50QKEFeiHRgR59\nLx0M9N3V1wz01Dtm3kDfPnUW6LGyBrrltZDVAz2TDd1bSrU3sftzPV8OC3Tf6wDNe/8+j7di\noEdvbM5A3xw/gR5LoMOHJJxiaQIt0JcbXy3QHb/jaUMPubSB/rjS/24/MZFAL6Q3dv3rvftq\nU3gLJQx08aZ2HOir2zb4Ru6e8N9lgR6pPtDTCy3QC1kj0G1RvrzcE+im53qVxgW6YtLAQI/d\n2dEbb64+mnOy220L9Gjl/4LV3b/mLdBbseFABx2N9QJdeyef7W7Y9h7GdQO9pUecQIcPSTjF\n0mIDXXgDTQ10aKcE+t/Whw9G7HxXlw8Q6NjljPg3YO+uNrXQAr2UvsdhjkB3Pa0T6HACPYFA\nhw9JOMXi+l4uGBno4l+Bcl/cqw8Eun9by9wNBXqkNQJd3VmB3orQQI+as2VI+860XL2JOxhj\nmzd63q5A1yygqRxXOdXMswn0JAK9whSL21Sgm4sLAj2zBZ6tX98aLXNt6RG3eKDvf/Y3Zug0\nAr2UyECPm7NlI8WB7njCVb0ziwW6dTHjtrXLQHcsbEuPOIEOH5JwisWtEejOSh0p0FX7L9B5\nCXT4kIRTLC5foO+223rtsGMxd6C7/roR6MMFOno1EwI9tdACvZS+h2GSQLc+61wt0GNn7vr7\npm4BgS++F0wl0CMIdPiQhFMsbquBDrN4oHu/aSnY2mL3wvmfrgv0JAK9/BSL21KgZ7n9Vwp0\n5VoEOi2BDh+ScIrFVQd6yo1x7EBPKd/CgZ55LoGeRKCXn2JxAj12y9MDPSF9ew5034tbWzC4\nswI9s03dXUr1PJ0T6O4R065d/S7BnQa662X5TT3iBDp6SMIplrdGoG8fmVcfdX+nO8/tP/oF\nh4hA1y5lyUDX/zVSPsHVhbvZtvWAG9rb8NUI9PJTLK/nIS/QnQOmXlugPye4uiDQ4/xvQmUF\neiu2E+iZbv5VAl1LoPNaPNBTKivQW7FqoJuWT6cP9Jw5L9jcjgJ9cz8Q6JGmVHZaoQV6MQI9\netOHCfTsUwn0JAK9+BTLWyXQHa8qC3TB5ha8Fwr0GAIdPSThFMtbJ9B/np21BLptu3MHeuSW\nBU+X8U8AABSpSURBVDp6AoGuItCLT7G8lQLdWuPOJ7NdT60jRAS6ZwMCXTBB+zdUG3vADezt\nDIsR6MWnWF7PI37eQLeUsTfQc9364wM96qledKB3dSe8CbNAL0egN2K9QP+OTcsjUqD7p97V\nnVCgVyPQG7FioFsfkQLdP/Wu7oQCvRqB3oruh/z8gW7Z6BYD3VdNge4h0KsR6K0QaIFey4EC\nnW0tAr0VAr2lQO/rTijQ65lUaIFeTveq8gS675Xy2ebsH3P1oUDX6Q/0xpYq0NFDEk6xAoGe\nHujaG7HCru6EN4G+WdzGlirQ0UMSTrGCymd/An35oUDXaS7+/77nQOdbi0BvhEBPDXTvFgS6\nT/P1v/frS/cfpSfQ0UMSTpGLQHePKd3C4e40owj0agR6+1YIdMdmBXqfBHo1Ar19eQI9420v\n0Cs6SqATLkWgt0+gewYVbuFwd5pRBHo1Ar19At0zqHALh7vTjCLQqxHo7RPonkFXlwW6zk2g\n7/7i25Ke3U25kimFFugUEgV6PgK9IoFej0Bv3uJ3OYE+GIFej0BvnkD3DLq6LNB1bm87gV6O\nQG+eQPeNurwo0HWOEeicCxHozRPovlGXFzu3cLj7zEiNQK9FoDdv+W/aVvh99AK9pp5Ab+2m\nE+joIQmnyOUgga6Zs7m+JNCVBHo1Ar15At0z6vpS1zYOd58Z6QiBTroQgd68FQI9z2b7pxTo\n9dy+wNT1lrsNEOjoIQmnyEWge0ZdX2p6v0qX3QY6/0IEevMEum/Y1QWBrnN76+fvWrfrv2ma\n1k9nMqHQAp2CQPcNu7og0HX2G+ivD9OuQ6C3TqD7hl39KdB1BHo9Ar11At037OpPga6z40C3\n3zUSEeitE+i+YVd/CnSdPQf68xN5lyHQW7d8oFcwS6Dz/4QoiV0H+l+lUxLorRPovmFXf3a8\n/rij22ked7f+hr/5uA/0xQsdCQn01gl077iWP+42uZ/baSa3544K9GIEeusEunfcv/8LdLV9\nB3qN311QTKC37hCBrn0MCXSI2xtow7dcW6AzL0Ogt06g+4Zd/F+gq+090JnVF1qgUxDovmEX\n/xfoagK9HoHeOIHuG3bx//frG0Sgy+000JvYe4HeOIHuG3bx/3eBriXQ6xHorete8Y5ui9of\ntDdf/7u68C7Qk2zgp2sdBDp4SMIpkhHovnHvXW8Ka7acmbVt95YT6OAhCadIRqD7xr0L9Az+\nvHSU+Q3EHQQ6eEjCKZIR6L5x7wI9g+Yjzpu82QQ6eEjCKZLpXPGeborwQF9+YU831DK2GecP\nAh08JOEUyRwj0BPGCTRfBDp4SMIpkhHogYECzZfNBbq+0AKdg0D3D7yp8sVFgT4egQ4eknCK\nZAS6f6BA849ABw9JOEUyAt0/UKD5R6CDhyScIhmB7h8o0Pwj0MFD/vj57enjnZfN0/PPuabY\nKoHuH9gd6L8f7+mGop9ABw85e3to/nmcZYrtEuj+gQLNPwIdPOTsuTn9eD1f+vVyap7nmGK7\nBLp4pEAfnkAHDzk7Na9fl1+b0xxTbJdAl4+8eUu0QB+OQAcP+RzXdH0QNsV2CXT5SIE+OoEO\nHnLmGXQPgS4f2VxfaO6vwb5tL9DVhV72NeiXX+dLXoO+c4hABxHooxPo4CGfHi/exfHwNssU\nmyXQ5QT66AQ6eMgfP5/P74M+PX3zPugbAl1OoI9OoIOHJJwiGYEuJ9BHJ9DBQxJOkYxAlxPo\noxPo4CF/ONW7k0CXuwn01ZvtOIINBrrWgoF2qncPgS4n0IfXtFzap2XfZudU7y4CXU6gD0+g\nY4ecOVGlh0CXE+jDE+jYIZ/jnOrdTaDLCfThCXTskDPPoHsIdDmBPjyBjh1y5lTvHgJdrmn7\n0+10JAIdO+STU727CfQIzdUfAn08Ah075A+nencS6BEE+ugEOnZIwimSEegRBProBDp2SMIp\nkula8fFuiQI3gT5fcEMdiUDHDvnDqd7dOpZ8wFtimEAfnUDHDjlzqncfgS4n0Ecn0LFDzgZO\n9W4uVU6xYQJdTqCPTqBjh5w5UaWPQJcT6KMT6Nghn+Oc6t1DoMsJ9NEJdOyQM8+g+wh0OYE+\nOoGOHXLmVO8+Al1OoI9OoGOHfHKqdw+BLifQRyfQsUP+cKp3N4Eu11z8/+8lN9SRCHTskIRT\nZCPQ5QT66AQ6dkjCKbIR6HK3gf646IY6EoGOHXLp+0PTPL3MOsUWCXQ5gT46gY4d8jnuPPDP\nTwp738Sx+1u9hUCP0Hz97+tjN9SRCHTskM9xHwOfm+e39/dfz833OabYMIEeQaAPTqBjh3yO\n+xh4as7vr3trHuaYYsMEegSBPjiBjh3yOa75+7+LP2On2DCBHkGgD06gY4d8jvsY+N/fQDvV\n+5pAjyDQB9fcXdirRQP99O37S/Pj98W3Z6d63xDoEQT64AQ6dsjnuH+/67lpTk71vibQI9wH\n2u10KAIdO+TT6+v3709P5x8VPvf2ef83+z2BHkGgD06gY4cknCIbgR7h7tzuI/4bPEcm0LFD\nEk6RjUCPINAHJ9CxQ242MLiF3d/s9wR6BIE+OIGOHXKzAYG+J9AjCPTBCXTskJsNCPQ9gR5B\noA9OoGOH3GxAoO8J9AgCfXACHTvkZgMCfa99yQe8IUoI9MEJdOyQmw0I9D2BHuP21EGBPpjm\n5s/d8ja7JAR6DIE+OIEOHZJwimwEegyBPjiBDh2ScIpsBHqM20C7oQ5GoEOHJJwiG4EeQ6AP\nTqBDhyScIhuBHuPutyO5oY5FoEOHJJwindY1H/GGKCHQByfQoUMSTpGOQI8g0Acn0KFDEk6R\njkCPINAHJ9ChQxJOkY5Aj+A39B+cQIcOSThFOgI9gkAfnECHDkk4RToCPYJAH5xAhw5JOEU6\nAj2CQB+cQIcOSThFOgI9gkAfnECHDkk4RToCPYJAH1xz9ceOCXQWAj2CQB+cQIcOSThFOgI9\nhl9fd2wCHTok4RTpCPQYbphjE+jQIQmnSEegx3DDHJtAhw5JOEU6Aj2GG+bYBDp0SMIp0hHo\nMdwwxybQoUMSTpGOQI/hhjk2gQ4dknCKdAR6DDfMsQl06JCEU6Qj0GO4YY5NoEOHJJwiHYGG\nUgIdOiThFOkINJQS6NAhCadIR6ChlECHDkk4RTptaz7i7QDDBDp0SMIp0hFoKCXQoUMSTpGO\nQEMpgQ4dknCKdAQaSgl06JCEU6Qj0FBKoEOHJJwiHYGGUgIdOiThFOkINJQS6NAhCadIR6Ch\nlECHDkk4RTpNy8Uj3g4wTKBDhyScIp3LQDeHuf9Blebi/3sm0FlcP4P+TPQRbwcoINCRQxJO\nkc7tSxwfiT7i7QAFBDpySMIp8mluL3290gFcO8p3mAKdxl2gD3o7wDCBjhyScIp8ZBlKCXTk\nkIRT5CPQUEqgI4cknCIfgYZSAh05JOEU+Qg0lBLoyCEJp8hHoKGUQEcOSThFPgINpQQ6ckjC\nKfIRaCgl0JFDEk6Rj0BDKYGOHJJwinwEGkoJdOSQhFPkI9BQSqAjhyScIp/m5k+gi0BHDkk4\nRT4CDaUEOnJIwinyEWgoJdCRQxJOkY9AQymBjhyScIp8BBpKCXTkkIRT5CPQUEqgI4cknCIf\ngYZSAh05JOEU+Qg0lBLoyCEJp8hHoKGUQEcOSThFPgINpQQ6ckjCKfIRaCgl0JFDEk6RT3P1\nB9BNoCOHJJwiH4GGUgIdOSThFPkINJRq3o/xUBHoNAQaSgl05JCEU+Qj0FBKoCOHJJwiH4GG\nUgIdOSThFPkINJQS6MghCafIR6ChlEBHDkk4RT4CDaUEOnJIwikSai7+D/QQ6MghCadISKCh\nkEBHDkk4RUJHOTkKpmuO8VgR6DwEGkoJdOCQhFMkJNBQSqADhyScIiGBhlICHTgk4RQJCTSU\nEujAIQmnSEigoZRABw5JOEVCAg2lBDpwSMIpEhJoKCXQgUMSTpGQQEMpgQ4cknCKhAQaSgl0\n4JCEUyR0lLNXYTqBDhyScIqEBBpKCXTgkIRTJCTQUEqgA4f88fPbU/Ph6fnnXFNsmUBDKYEO\nHHL29tD88zjLFNsm0FBKoAOHnD03px+v50u/Xk7N8xxTbJtAQymBDhxydmpevy6/Nqc5ptg2\ngYZSAh045HNc0/VB2BTbJtBQqjnGY8Uz6DwEGkoJdOCQs+fm9PLrfMlr0K0EGkoJdOCQT48X\n7+J4eJtlik0TaCgl0IFD/vj5fH4f9Onpm/dBtzjIjz0ggEAHDkk4RUICDaUEOnBIwikSEmgo\nJdCBQ/5wqncvgYZSAh045Myp3gMEGkoJdOCQM6d6DxBoKCXQgUPOnKgyQKChlEAHDvkc51Tv\nfgINpQQ6cMiZZ9ADBBpKCXTgkDOneg8QaCgl0IFDPjnVe8BB7nMw3UEeLE71TqT/hXngi0AH\nDkk4RUYCDYUEOnBIwikyEmgoJNCBQ/5wqnc/gYZCAh045Myp3kMEGkod49HiVO9EjnGXgwjH\neLQ4USWRY9zlIMIxHi1O9U7kGHc5iHCMR4tn0Ikc4y4HEY7xaHGqdyLHuMtBhGM8WpzqnchB\nlw0VjvFoyXOqd3OpeopNO+iyocIxHi3OJEzkoMuGCsd4tAh0IgddNlQ4xqPFqd6JHHTZUOEY\njxaneidy0GVDhWM8WpzqDWzQMSLhRBVgg44RCad6Axt0jEh4Bg1s0DEi4VRvYIOOEQmnegMb\ndIxI5DnVO2YK4BCOEQlnEgIkJdAASQk0QFICDZCUQAMkteiZhMW/k1+gAZYM9HeBBhhhyZc4\nXk/9v2Q0YAqA/Vj0NejX/hO8I6YA2I1lf0j4/eL3Jc00BcBeeBcHQFICDZCUQAMktUag+99i\nFzIFwPYJNEBSAg2QlEADJCXQAEkJNEBS3mYHkJRAAyQl0ABJJQ00ADkDndBeVr2TdVhGJjtZ\nxk7WsY9VjLWXVe9kHZaRyU6WsZN17GMVY+1l1TtZh2VkspNl7GQd+1jFWHtZ9U7WYRmZ7GQZ\nO1nHPlYx1l5WvZN1WEYmO1nGTtaxj1WMtZdV72QdlpHJTpaxk3XsYxVj7WXVO1mHZWSyk2Xs\nZB37WMVYe1n1TtZhGZnsZBk7Wcc+VjHWXla9k3VYRiY7WcZO1rGPVYy1l1XvZB2WkclOlrGT\ndexjFWPtZdU7WYdlZLKTZexkHftYxVh7WfVO1mEZmexkGTtZxz5WAbBDAg2QlEADJCXQAEkJ\nNEBSAg2QlEADJCXQAEkJNEBSAg2QlEADJCXQAEkJNEBSAg2QlEADJCXQAEkdINDfH5rT89v5\n4vPp6+Lvz/9d+9Vn8xpcx8UVEhtcxm8/898tB5fx+l/T/PdrlX0bYWgZb5t+cHStbkPyPxKm\nem4+nD6OzeP54sPn51+bP2u/+mxeg+u4uEJig8v47e2U/m45uIyXXRyNX6fPK2T/i6Z9HV2r\n25L0j4SpXpv/3j6eEPz38bzs9Pr+emp+nj9/+nMfvPpsXoPruLhCYoPL+PDUZL9bDi/j9Puz\nb0/N85p7OWhwGf+dF/Cc/E7VsY6u1W1K9kfCZE+fK/y4wz03L78v/Wi+vX8ctsc/98HLzyY2\nuI6LKyQ2uIzzp5IvomAZP85le2tOq+1iicFlNJu4U3Wso2N125L8lg/zcZSemo/v1F6bp4+P\nn9+/wvbvs/l1r+PiCvn1LePXZaxz617Gf83rqns2Svcy/rzWlPzvmb9u19H32a3YyCNhqrfm\n8frpwOvts4NtNKFnHRdXSK93GY/Nr20cjL5lPDTv307nb7Hz61nGtz8vcWzimefdOno+uxnb\n2+Mq3z++xbk5SlsMdM86Lq6QXt8yvjU/NnIw+u9VT+cfUK21a2P0HY3vHz8lPH1fac/GaVtH\n12c3Y3t7XOPX6fyd2+YD3beOiytk17eM87ehmzgYA/eqjx8S/reFp569d6pv53c/bGAVHevo\n+Ox2bG+PK7ydzt/3bz7Qveu4uEJyvct4+HhX1BYOxtC96uM16F8beGNX7zK+f7zE8fvvmQ08\nhW5fR/tnN2R7e1zh8fNhcmq9D562c+x613FxheT6lvHf+SWaLRyM/qOxnSL0LuOh+XgV/W0D\nf890rKP9sxuyvT0e7dfD4+f77D9/lPvr749yr97F8Sv/D3gH1nFxhdT6l9F8WWv/Cg3eqy4/\nymtgGZv5e6Z9HV2r25D0t/xkL1/va/h2fnb28vfkga8fVF99Nq2hdVxcIbOBZWwl0GX3ql/Z\nD8nQMj6feWZ/O3fXOjpXtyHJHwfTXTxGbk4n+nMf3MhJRoPrSB+Ds8Fl3F1OqeBoPLx9vHj7\nY5XdKzW4jOfm4/dXPGcPW/s6ule3IdkfCZP9d/GM7OF84e9R+1uB689mNbiO/zbx1HP4cNxe\nTml4Gd+2cK8aXsbjFpbRsY7u1W1I9kfCZJffMn/+aq5/X/n88/qzWQ2uYxuvDQwfjtvLKRUs\n4+Ux/72qYBkbfnB0r25Dsj8SAA5LoAGSEmiApAQaICmBBkhKoAGSEmiApAQaICmBBkhKoAGS\nEmiApAQaICmBBkhKoAGSEmiApAQaICmBBkhKoAGSEmiApAQaICmBBkhKoAGSEmiApAQaICmB\nBkhKoAGSEmiApAQaICmBBkhKoAGSEmiApAQaICmBBkhKoNmX5sLvD9beHZjCHZh9EWh2xB2Y\nHRJm9sEdmR0SaPbBHZkd+hvojz9///etOX17f39umufzZ78/NKfvK+4dlBJodug60N8+Xo9+\nefz4/0ehn86vTz+uuoNQRKDZoetAP769f//z/9P7+8vHpbfH5mXdXYQCAs0OXQf65/nSrz8f\nPzVvvy+9NU8r7h+UEWh26OY16PfL//97Ex5k517KDgk0++Beyg71B3q9/YJx3FnZob5AP/nx\nIJsh0OxQX6B/NKfX9/fvfkjIBgg0O9QX6PfzG6Kb06/V9g5KCTQ71BvojzMJm//0mQ0QaICk\nBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqg\nAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBogKYEGSEqgAZISaICkBBog\nKYEGSOr/ECbgvm+1ZXYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prediccíón de la serie de diferencias con el modelo ARIMA seleccionado\n",
    "plot(rests.d12, xlim = c(2009,2021))\n",
    "rests.d12_pred <-predict(fit, n.ahead = 12)\n",
    "lines(rests.d12_pred$pred, col=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combinamos la serie temporal de differencias y la predicción\n",
    "# en una misma serie\n",
    "\n",
    "rests.all <- c(rests.d12, rests.d12_pred$pred)\n",
    "rests.all <- ts(rests.all, start=c(2009,1), freq=12)\n",
    "plot(rests.all, type=\"l\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# primero deshacemos la diferencia estacional\n",
    "# xi es la lista de valores iniciales necesarios para invertir las diferencias.\n",
    "# en este caso se toman el valor mensual del primer año con datos.\n",
    "rests.inv1 <- diffinv(rests.all, lag = 12, differences = 1,\n",
    "                     xi = c(rests.d1[1] ,rests.d1[2], rests.d1[3],\n",
    "                            rests.d1[4], rests.d1[5], rests.d1[6],\n",
    "                            rests.d1[7], rests.d1[8], rests.d1[9],\n",
    "                            rests.d1[10], rests.d1[11], rests.d1[12]))\n",
    "\n",
    "#segundo deshacemos la diferencia de orden 1\n",
    "rests.inv2 <- diffinv(rests.inv1, lag = 1, differences = 1,\n",
    "                     xi = rests.BC[1])\n",
    "\n",
    "#y tercero la transformación Box-Cox\n",
    "rests.inv3 <- InvBoxCox(rests.inv2,lambda)\n",
    "rests.global <- ts(rests.inv3, start = 2009, freq = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3.5 Predicciones del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAOVBMVEUAAAAAAP8AZABNTU1o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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hacemos un plot de las serie original con la predicción\n",
    "plot(rests.global,\n",
    "     type = \"l\",\n",
    "     xlim = c(2009,2021),\n",
    "     ylim = c(0.99*min(rests.global), 1.20*max(rests.global)),\n",
    "     col = 'red')\n",
    "lines(rests, col=\"blue\")\n",
    "lines(rests.check, col = 'dark green')\n",
    "legend(\"topleft\",\n",
    "       legend=c(\"serie hasta 2019\",\"prediccion 2020\",'datos reales 2020'),\n",
    "       col=c(\"blue\",\"red\",'dark green'),lwd=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6.2 Modelo de predicción con datos sin transformar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series: rests \n",
       "ARIMA(1,1,0)(2,1,1)[12] \n",
       "\n",
       "Coefficients:\n",
       "         ar1     sar1     sar2     sma1\n",
       "      0.4869  -0.1356  -0.2948  -0.7711\n",
       "s.e.  0.0860   0.1489   0.1383   0.1775\n",
       "\n",
       "sigma^2 estimated as 323:  log likelihood=-520.22\n",
       "AIC=1050.44   AICc=1050.97   BIC=1064.34"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAANICAMAAAAGubgvAAAAPFBMVEUAAAAil+ZNTU1oaGh8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\nBBoARBFoABBFoAFAFIEGAFH/ByblhXmeXn67AAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Forecasts from ARIMA(1,1,0)(2,1,1)[12]\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 720
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Con la libreria forecast y la serie original (sin transformar)\n",
    "\n",
    "ajuste <- auto.arima(rests)\n",
    "ajuste\n",
    "rests.fcast <- forecast(ajuste, h=12)\n",
    "plot(rests.fcast)\n",
    "lines(rests.check, col = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se observa que con los datos sin transformar la solución es similar a la obtenida con los datos transformados, salvo que en este caso el parámetro de integración vale 1 en lugar de 0. Por tanto en el modelo ARIMA como en el SARIMA. Es decir internamente se han realizado las mismas diferenciaciones de orden 1 que hemos realizado nostros de forma manual\n",
    "\n",
    "Dado que el AIC obtenido es menor que el modelo obtenido con los datos transformados, pareciera en principio, por ahorrarnos pasos sería recomendable usar los datos sin transformar. No obstante en la seccion de evaluación de modelos se verá cual proporciona mejor resultado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 - Evaluación de los modelos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Test.set</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ME</th><td>-34.201673</td></tr>\n",
       "\t<tr><th scope=row>RMSE</th><td> 38.334508</td></tr>\n",
       "\t<tr><th scope=row>MAE</th><td> 34.978077</td></tr>\n",
       "\t<tr><th scope=row>MPE</th><td>-14.604987</td></tr>\n",
       "\t<tr><th scope=row>MAPE</th><td> 14.996735</td></tr>\n",
       "\t<tr><th scope=row>ACF1</th><td>  0.447668</td></tr>\n",
       "\t<tr><th scope=row>Theil's U</th><td>  1.353393</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & Test.set\\\\\n",
       "  & <dbl>\\\\\n",
       "\\hline\n",
       "\tME & -34.201673\\\\\n",
       "\tRMSE &  38.334508\\\\\n",
       "\tMAE &  34.978077\\\\\n",
       "\tMPE & -14.604987\\\\\n",
       "\tMAPE &  14.996735\\\\\n",
       "\tACF1 &   0.447668\\\\\n",
       "\tTheil's U &   1.353393\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 1\n",
       "\n",
       "| <!--/--> | Test.set &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| ME | -34.201673 |\n",
       "| RMSE |  38.334508 |\n",
       "| MAE |  34.978077 |\n",
       "| MPE | -14.604987 |\n",
       "| MAPE |  14.996735 |\n",
       "| ACF1 |   0.447668 |\n",
       "| Theil's U |   1.353393 |\n",
       "\n"
      ],
      "text/plain": [
       "          Test.set  \n",
       "ME        -34.201673\n",
       "RMSE       38.334508\n",
       "MAE        34.978077\n",
       "MPE       -14.604987\n",
       "MAPE       14.996735\n",
       "ACF1        0.447668\n",
       "Theil's U   1.353393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Errores con el modelo ARIMA con transformacion de datos\n",
    "(errors_steps <-  data.frame(t(accuracy(window(rests.global,2019), rests.check))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 1</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Test.set</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ME</th><td>-66.3047496</td></tr>\n",
       "\t<tr><th scope=row>RMSE</th><td> 67.9836559</td></tr>\n",
       "\t<tr><th scope=row>MAE</th><td> 66.3047496</td></tr>\n",
       "\t<tr><th scope=row>MPE</th><td>-31.5142673</td></tr>\n",
       "\t<tr><th scope=row>MAPE</th><td> 31.5142673</td></tr>\n",
       "\t<tr><th scope=row>ACF1</th><td>  0.3197843</td></tr>\n",
       "\t<tr><th scope=row>Theil's U</th><td>  2.9649770</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 1\n",
       "\\begin{tabular}{r|l}\n",
       "  & Test.set\\\\\n",
       "  & <dbl>\\\\\n",
       "\\hline\n",
       "\tME & -66.3047496\\\\\n",
       "\tRMSE &  67.9836559\\\\\n",
       "\tMAE &  66.3047496\\\\\n",
       "\tMPE & -31.5142673\\\\\n",
       "\tMAPE &  31.5142673\\\\\n",
       "\tACF1 &   0.3197843\\\\\n",
       "\tTheil's U &   2.9649770\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 1\n",
       "\n",
       "| <!--/--> | Test.set &lt;dbl&gt; |\n",
       "|---|---|\n",
       "| ME | -66.3047496 |\n",
       "| RMSE |  67.9836559 |\n",
       "| MAE |  66.3047496 |\n",
       "| MPE | -31.5142673 |\n",
       "| MAPE |  31.5142673 |\n",
       "| ACF1 |   0.3197843 |\n",
       "| Theil's U |   2.9649770 |\n",
       "\n"
      ],
      "text/plain": [
       "          Test.set   \n",
       "ME        -66.3047496\n",
       "RMSE       67.9836559\n",
       "MAE        66.3047496\n",
       "MPE       -31.5142673\n",
       "MAPE       31.5142673\n",
       "ACF1        0.3197843\n",
       "Theil's U   2.9649770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Errores con el modelo ARIMA obtenido a partir de la serie de partida\n",
    "# y sin transformaciones previas\n",
    "(errors_forecast <- data.frame(t(accuracy(rests.fcast$mean , rests.check))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se presentan en una misma tabla los errores calculados para los tres modelos (suavizado exponencia y los dos modelos ARIMA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 7 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>description</th><th scope=col>arima_trans</th><th scope=col>arima_notrans</th><th scope=col>suav_exp</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ME</th><td>Error medio                             </td><td>-34.201673</td><td>-66.3047496</td><td>-251.4699611</td></tr>\n",
       "\t<tr><th scope=row>RMSE</th><td>Raíz del error cuadrático medio         </td><td> 38.334508</td><td> 67.9836559</td><td> 260.7298225</td></tr>\n",
       "\t<tr><th scope=row>MAE</th><td>Error medio absoluto                    </td><td> 34.978077</td><td> 66.3047496</td><td> 251.4699611</td></tr>\n",
       "\t<tr><th scope=row>MPE</th><td>Porcentaje de error medio               </td><td>-14.604987</td><td>-31.5142673</td><td>-117.5450097</td></tr>\n",
       "\t<tr><th scope=row>MAPE</th><td>Porcentaje de error medio absoluto      </td><td> 14.996735</td><td> 31.5142673</td><td> 117.5450097</td></tr>\n",
       "\t<tr><th scope=row>ACF1</th><td>Autocorrelación de errores con retraso 1</td><td>  0.447668</td><td>  0.3197843</td><td>   0.4218031</td></tr>\n",
       "\t<tr><th scope=row>Theil's U</th><td>Índice U de desigualdad de Theils       </td><td>  1.353393</td><td>  2.9649770</td><td>  11.2692416</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & description & arima\\_trans & arima\\_notrans & suav\\_exp\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tME & Error medio                              & -34.201673 & -66.3047496 & -251.4699611\\\\\n",
       "\tRMSE & Raíz del error cuadrático medio          &  38.334508 &  67.9836559 &  260.7298225\\\\\n",
       "\tMAE & Error medio absoluto                     &  34.978077 &  66.3047496 &  251.4699611\\\\\n",
       "\tMPE & Porcentaje de error medio                & -14.604987 & -31.5142673 & -117.5450097\\\\\n",
       "\tMAPE & Porcentaje de error medio absoluto       &  14.996735 &  31.5142673 &  117.5450097\\\\\n",
       "\tACF1 & Autocorrelación de errores con retraso 1 &   0.447668 &   0.3197843 &    0.4218031\\\\\n",
       "\tTheil's U & Índice U de desigualdad de Theils        &   1.353393 &   2.9649770 &   11.2692416\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 4\n",
       "\n",
       "| <!--/--> | description &lt;chr&gt; | arima_trans &lt;dbl&gt; | arima_notrans &lt;dbl&gt; | suav_exp &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| ME | Error medio                              | -34.201673 | -66.3047496 | -251.4699611 |\n",
       "| RMSE | Raíz del error cuadrático medio          |  38.334508 |  67.9836559 |  260.7298225 |\n",
       "| MAE | Error medio absoluto                     |  34.978077 |  66.3047496 |  251.4699611 |\n",
       "| MPE | Porcentaje de error medio                | -14.604987 | -31.5142673 | -117.5450097 |\n",
       "| MAPE | Porcentaje de error medio absoluto       |  14.996735 |  31.5142673 |  117.5450097 |\n",
       "| ACF1 | Autocorrelación de errores con retraso 1 |   0.447668 |   0.3197843 |    0.4218031 |\n",
       "| Theil's U | Índice U de desigualdad de Theils        |   1.353393 |   2.9649770 |   11.2692416 |\n",
       "\n"
      ],
      "text/plain": [
       "          description                              arima_trans arima_notrans\n",
       "ME        Error medio                              -34.201673  -66.3047496  \n",
       "RMSE      Raíz del error cuadrático medio           38.334508   67.9836559  \n",
       "MAE       Error medio absoluto                      34.978077   66.3047496  \n",
       "MPE       Porcentaje de error medio                -14.604987  -31.5142673  \n",
       "MAPE      Porcentaje de error medio absoluto        14.996735   31.5142673  \n",
       "ACF1      Autocorrelación de errores con retraso 1   0.447668    0.3197843  \n",
       "Theil's U Índice U de desigualdad de Theils          1.353393    2.9649770  \n",
       "          suav_exp    \n",
       "ME        -251.4699611\n",
       "RMSE       260.7298225\n",
       "MAE        251.4699611\n",
       "MPE       -117.5450097\n",
       "MAPE       117.5450097\n",
       "ACF1         0.4218031\n",
       "Theil's U   11.2692416"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errores <- cbind(errors_steps, errors_forecast, errors)\n",
    "errores <- errores[, c(4,1,2,3)]\n",
    "colnames(errores)[2:4] <- c('arima_trans','arima_notrans','suav_exp')\n",
    "errores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según se muestra en la tabla el modelo ARIMA(1,0,0) SARIMA(2,0,1){12} con datos transformados, proporciona los mejores índices de error de los tres modelos. No obstante se pone de manifiesto que los modelos ARIMA/SARIMA muestran mucha más precisión en que el modelo de suavizado exponencial. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Conclusiones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se ha visto que es posible hacer predicciones a partir de los datos de una serie temporal. No obstante la calidad de estas predicciones depende de la cantidad de datos disponibles, de manera que sea posible extraer la estructura de la serie. Además depende del modelo de predicción aplicado e incluso de la librería empleada aunque, en este último caso, es necesario documentarse sobre la parámetros de la función que calcula el modelo y entender que consideraciones están incluidas en los parámetros por defecto.\n",
    "\n",
    "En relación a los métricas de error, indicar que la más intuitiva es MAE (Mean Absolute Error) o Error Medio Absoluto. Este es un error promedio entre las 12 datos predichos. En manos de quien tenga que tomar decisiones al respecto está el considerar si el error se considera o no admisible para el contexto de aplicación considerado. En caso de que el error cometido no sea aceptable deberán analizarse otro modelos de predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir40"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}