| 1 | { |
| 2 | "cells": [ |
| 3 | { |
| 4 | "cell_type": "markdown", |
| 5 | "metadata": {}, |
| 6 | "source": [ |
| 7 | "## Package R \"vortex\"\n", |
| 8 | "\n", |
| 9 | "using Vectorial exOgenous variables to foRecast Time-sErieX.\n", |
| 10 | "\n", |
| 11 | "Ce package permet de prévoir des courbes de PM10 (par exemple), en se basant sur l'historique des valeurs mais aussi des variables exogènes (par exemple la météo).\n", |
| 12 | "\n", |
| 13 | "Fonctions principales :\n", |
| 14 | "\n", |
| 15 | " * <code>getData</code> : charge un jeu de données en mémoire\n", |
| 16 | " * <code>getForecast</code> : prédit les lendemains aux indices demandés\n", |
| 17 | "\n", |
| 18 | "Diverses méthodes permettent ensuite d'analyser les performances : <code>getError</code>, <code>plotXYZ</code> : voir la section \"see also\" dans <code>?plotError</code>." |
| 19 | ] |
| 20 | }, |
| 21 | { |
| 22 | "cell_type": "code", |
| 23 | "execution_count": null, |
| 24 | "metadata": { |
| 25 | "collapsed": false |
| 26 | }, |
| 27 | "outputs": [], |
| 28 | "source": [ |
| 29 | "#Chargement de la librairie (après compilation, \"R CMD INSTALL ppmfun/\")\n", |
| 30 | "library(vortex)" |
| 31 | ] |
| 32 | }, |
| 33 | { |
| 34 | "cell_type": "markdown", |
| 35 | "metadata": {}, |
| 36 | "source": [ |
| 37 | "Note : sur la base de nos dernières expériences, on considère que \n", |
| 38 | "\n", |
| 39 | " * on ne touche pas à la fenêtre obtenue par la fonction <code>optimize</code> ;\n", |
| 40 | " * on oublie la méthode consistant à prédire forme et niveau de manière complètement déconnectée : il faut relier les deux.\n", |
| 41 | "\n", |
| 42 | "### Acquisition des données\n", |
| 43 | "\n", |
| 44 | "Compte-tenu de la nature hétérogène des données utilisées $-$ fonctionnelles pour les PM10, vectorielles pour les variables exogènes $-$, celles-ci sont encapsulées (comme des listes) dans un objet de type *Data*. En interne, la $i^{eme}$ cellule correspondant aux données disponibles au $i^{eme}$ jour à l'heure $H$ de prédiction choisie (1h00, 8h00 ou 14h00) : c'est-à-dire les valeurs des PM10 de $H-24h$ à $H-1h$, ainsi que les variables météo prédites pour la période de 1h à 0h du jour courant (sauf si on prédit à 0h : on prend alors les valeurs mesurées de la veille).\n", |
| 45 | "\n", |
| 46 | "Méthodes d'un objet de classe \"Data\" : elles prennent comme argument \"index\", qui est un index entier ; mais une fonction de conversion existe : <code>dateIndexToInteger</code>.\n", |
| 47 | "\n", |
| 48 | " * <code>getTime</code> : suite des date+heure\n", |
| 49 | " * <code>getCenteredSerie</code> : série centrée\n", |
| 50 | " * <code>getLevel</code> : niveau\n", |
| 51 | " * <code>getSerie</code> : série *non* centrée\n", |
| 52 | " * <code>getExoHat</code> : variables exogènes prévues\n", |
| 53 | " * <code>getExoDm1</code> : variables exogènes mesurées la veille\n", |
| 54 | "\n", |
| 55 | "Exemple :" |
| 56 | ] |
| 57 | }, |
| 58 | { |
| 59 | "cell_type": "code", |
| 60 | "execution_count": null, |
| 61 | "metadata": { |
| 62 | "collapsed": false |
| 63 | }, |
| 64 | "outputs": [], |
| 65 | "source": [ |
| 66 | "# Voir ?getData pour les arguments\n", |
| 67 | "data = getData(ts_data=\"data/pm10_mesures_H_loc.csv\", exo_data=\"data/meteo_extra_noNAs.csv\",\n", |
| 68 | " input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=\"07\")\n", |
| 69 | "data$getLevel(10) #niveau du jour 10\n", |
| 70 | "data$getExoHat(17) #météo prévue pour le jour 18" |
| 71 | ] |
| 72 | }, |
| 73 | { |
| 74 | "cell_type": "markdown", |
| 75 | "metadata": {}, |
| 76 | "source": [ |
| 77 | "### Prédiction\n", |
| 78 | "\n", |
| 79 | "Deux types de prévisions du prochain bloc de $24h$ sont à distinguer :\n", |
| 80 | "\n", |
| 81 | " * prévision de la forme (centrée) ;\n", |
| 82 | " * prévision du saut d'une fin de série au début de la suivante.\n", |
| 83 | "\n", |
| 84 | "Il faut ainsi préciser à la fois une méthode de prévision de forme (\"Average\", \"Persistence\" et \"Neighbors\" sont implémentées), et une méthode de prédiction de saut (\"Zero\", \"Persistence\" ou \"Neighbors\"). On détaille surtout la méthode à voisins ci-après, les autres étant des approches naïves que l'on peut considérer comme des références à améliorer.\n", |
| 85 | "\n", |
| 86 | " 1. **Préparation des données** : fenêtrage si demandé (paramètre \"memory\"), recherche des paires de jours consécutifs sans valeurs manquantes.\n", |
| 87 | " 2. **Optimisation des paramètres d'échelle** : via la fonction <code>optimize</code> minimisant la somme des 45 dernières erreurs jounalières RMSE, sur des jours similaires.\n", |
| 88 | " 3. **Prédiction finale** : une fois le (ou les, si \"simtype\" vaut \"mix\") paramètre d'échelle $h$ déterminé, les similarités sont évaluées sur les variables exogènes et/ou endogènes, sous la forme $s(i,j) = \\mbox{exp}\\left(-\\frac{\\mbox{dist}^2(i,j)}{h^2}\\right)$. La formule indiquée plus haut dans le rapport est alors appliquée.\n", |
| 89 | "\n", |
| 90 | "Détail technique : la sortie de la méthode <code>getForecast</code> est un objet de type Forecast, encapsulant les séries prévues ainsi que tous les paramètres optimisés par la méthode \"Neighbors\". Fonctions disponibles (argument \"index\" comme pour les fonctions sur Data) :\n", |
| 91 | "\n", |
| 92 | " * <code>getSerie</code> : série prévue (sans les information de temps)\n", |
| 93 | " * <code>getParams</code> : liste des paramètres (poids, fenêtre, ...)\n", |
| 94 | " * <code>getIndexInData</code> : indice du jour où s'effectue la prévision relativement au jeu de données\n", |
| 95 | "\n", |
| 96 | "### Calcul des erreurs\n", |
| 97 | "\n", |
| 98 | "Pour chacun des instants à prévoir jusqu'à minuit du jour courant (ou avant : argument *horizon*), on calcule l'erreur moyenne sur tous les instants similaires du passé. Deux types d'erreurs sont considérées :\n", |
| 99 | "\n", |
| 100 | " * l'erreur \"abs\" égale à la valeur absolue moyenne entre la mesure et la prédiction ;\n", |
| 101 | " * l'erreur \"MAPE\" égale à l'erreur absolue normalisée par la mesure.\n", |
| 102 | "\n", |
| 103 | "### Expériences numériques" |
| 104 | ] |
| 105 | }, |
| 106 | { |
| 107 | "cell_type": "code", |
| 108 | "execution_count": null, |
| 109 | "metadata": { |
| 110 | "collapsed": false |
| 111 | }, |
| 112 | "outputs": [], |
| 113 | "source": [ |
| 114 | "options(repr.plot.width=9, repr.plot.height=3)" |
| 115 | ] |
| 116 | }, |
| 117 | { |
| 118 | "cell_type": "code", |
| 119 | "execution_count": null, |
| 120 | "metadata": { |
| 121 | "collapsed": false |
| 122 | }, |
| 123 | "outputs": [], |
| 124 | "source": [ |
| 125 | "p_endo = predictPM10(data, 2200, 2230, 0,0, \"Neighbors\", \"Neighbors\", simtype=\"endo\")" |
| 126 | ] |
| 127 | }, |
| 128 | { |
| 129 | "cell_type": "code", |
| 130 | "execution_count": null, |
| 131 | "metadata": { |
| 132 | "collapsed": false |
| 133 | }, |
| 134 | "outputs": [], |
| 135 | "source": [ |
| 136 | "p_exo = predictPM10(data, 2200, 2230, 0,0, \"Neighbors\", \"Neighbors\", simtype=\"exo\")" |
| 137 | ] |
| 138 | }, |
| 139 | { |
| 140 | "cell_type": "code", |
| 141 | "execution_count": null, |
| 142 | "metadata": { |
| 143 | "collapsed": false |
| 144 | }, |
| 145 | "outputs": [], |
| 146 | "source": [ |
| 147 | "p_mix = predictPM10(data, 2200, 2230, 0,0, \"Neighbors\", \"Neighbors\", simtype=\"mix\")" |
| 148 | ] |
| 149 | }, |
| 150 | { |
| 151 | "cell_type": "code", |
| 152 | "execution_count": null, |
| 153 | "metadata": { |
| 154 | "collapsed": false |
| 155 | }, |
| 156 | "outputs": [], |
| 157 | "source": [ |
| 158 | "p = list(p_endo, p_exo, p_mix)" |
| 159 | ] |
| 160 | }, |
| 161 | { |
| 162 | "cell_type": "code", |
| 163 | "execution_count": null, |
| 164 | "metadata": { |
| 165 | "collapsed": false |
| 166 | }, |
| 167 | "outputs": [], |
| 168 | "source": [ |
| 169 | "yrange_MAPE = range(p_mix$errors$MAPE, p_endo$errors$MAPE, p_exo$errors$MAPE)\n", |
| 170 | "yrange_abs = range(p_mix$errors$abs, p_endo$errors$abs, p_exo$errors$abs)\n", |
| 171 | "yrange_RMSE = range(p_mix$errors$RMSE, p_endo$errors$RMSE, p_exo$errors$RMSE)\n", |
| 172 | "ranges = list(yrange_MAPE,yrange_abs,yrange_RMSE)\n", |
| 173 | "\n", |
| 174 | "par(mfrow=c(1,3))\n", |
| 175 | "titles = paste(\"Erreur\",c(\"MAPE\",\"abs\",\"RMSE\"))\n", |
| 176 | "for (i in 1:3) #error type (MAPE,abs,RMSE)\n", |
| 177 | "{\n", |
| 178 | " for (j in 1:3) #model (mix,endo,exo)\n", |
| 179 | " {\n", |
| 180 | " xlab = if (j>1) \"\" else \"Temps\"\n", |
| 181 | " ylab = if (j>1) \"\" else \"Erreur\"\n", |
| 182 | " main = if (j>1) \"\" else titles[i]\n", |
| 183 | " plot(p[[j]]$errors[[i]], type=\"l\", col=j, main=main, xlab=xlab, ylab=ylab, ylim=ranges[[i]])\n", |
| 184 | " if (j<3)\n", |
| 185 | " par(new=TRUE)\n", |
| 186 | " }\n", |
| 187 | "}" |
| 188 | ] |
| 189 | }, |
| 190 | { |
| 191 | "cell_type": "markdown", |
| 192 | "metadata": {}, |
| 193 | "source": [ |
| 194 | "Ne tenir compte que des similarités sur les variables exogènes semble conduire à l'erreur la plus faible." |
| 195 | ] |
| 196 | }, |
| 197 | { |
| 198 | "cell_type": "code", |
| 199 | "execution_count": null, |
| 200 | "metadata": { |
| 201 | "collapsed": false |
| 202 | }, |
| 203 | "outputs": [], |
| 204 | "source": [ |
| 205 | "p_nn = predictPM10(data, 2200, 2230, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE)" |
| 206 | ] |
| 207 | }, |
| 208 | { |
| 209 | "cell_type": "code", |
| 210 | "execution_count": null, |
| 211 | "metadata": { |
| 212 | "collapsed": false |
| 213 | }, |
| 214 | "outputs": [], |
| 215 | "source": [ |
| 216 | "p_np = predictPM10(data, 2200, 2230, 0, 0, \"Neighbors\", \"Persistence\", sameSeaon=TRUE)" |
| 217 | ] |
| 218 | }, |
| 219 | { |
| 220 | "cell_type": "code", |
| 221 | "execution_count": null, |
| 222 | "metadata": { |
| 223 | "collapsed": false |
| 224 | }, |
| 225 | "outputs": [], |
| 226 | "source": [ |
| 227 | "p_nz = predictPM10(data, 2200, 2230, 0, 0, \"Neighbors\", \"Zero\", sameSeaon=TRUE)" |
| 228 | ] |
| 229 | }, |
| 230 | { |
| 231 | "cell_type": "code", |
| 232 | "execution_count": null, |
| 233 | "metadata": { |
| 234 | "collapsed": false |
| 235 | }, |
| 236 | "outputs": [], |
| 237 | "source": [ |
| 238 | "p_pp = predictPM10(data, 2200, 2230, 0, 0, \"Persistence\", \"Persistence\")" |
| 239 | ] |
| 240 | }, |
| 241 | { |
| 242 | "cell_type": "code", |
| 243 | "execution_count": null, |
| 244 | "metadata": { |
| 245 | "collapsed": false |
| 246 | }, |
| 247 | "outputs": [], |
| 248 | "source": [ |
| 249 | "p_pz = predictPM10(data, 2200, 2230, 0, 0, \"Persistence\", \"Zero\")" |
| 250 | ] |
| 251 | }, |
| 252 | { |
| 253 | "cell_type": "code", |
| 254 | "execution_count": null, |
| 255 | "metadata": { |
| 256 | "collapsed": false |
| 257 | }, |
| 258 | "outputs": [], |
| 259 | "source": [ |
| 260 | "p = list(p_nn, p_np, p_nz, p_pp, p_pz)" |
| 261 | ] |
| 262 | }, |
| 263 | { |
| 264 | "cell_type": "code", |
| 265 | "execution_count": null, |
| 266 | "metadata": { |
| 267 | "collapsed": false |
| 268 | }, |
| 269 | "outputs": [], |
| 270 | "source": [ |
| 271 | "yrange_MAPE = range(p_nn$errors$MAPE, p_nz$errors$MAPE, p_np$errors$MAPE, p_pp$errors$MAPE, p_pz$errors$MAPE)\n", |
| 272 | "yrange_abs = range(p_nn$errors$abs, p_nz$errors$abs, p_np$errors$abs, p_pp$errors$abs, p_pz$errors$abs)\n", |
| 273 | "yrange_RMSE = range(p_nn$errors$RMSE, p_nz$errors$RMSE, p_np$errors$RMSE, p_pp$errors$RMSE, p_pz$errors$RMSE)\n", |
| 274 | "ranges = list(yrange_MAPE,yrange_abs,yrange_RMSE)\n", |
| 275 | "\n", |
| 276 | "par(mfrow=c(1,3))\n", |
| 277 | "titles = paste(\"Erreur\",c(\"MAPE\",\"abs\",\"RMSE\"))\n", |
| 278 | "for (i in 1:3) #error type (MAPE,abs,RMSE)\n", |
| 279 | "{\n", |
| 280 | " for (j in 1:5) #model (nn,np,nz,pp,pz)\n", |
| 281 | " {\n", |
| 282 | " xlab = if (j>1) \"\" else \"Temps\"\n", |
| 283 | " ylab = if (j>1) \"\" else \"Erreur\"\n", |
| 284 | " main = if (j>1) \"\" else titles[i]\n", |
| 285 | " plot(p[[j]]$errors[[i]], type=\"l\", col=j, main=titles[i], xlab=xlab, ylab=ylab, ylim=ranges[[i]])\n", |
| 286 | " if (j<5)\n", |
| 287 | " par(new=TRUE)\n", |
| 288 | " }\n", |
| 289 | "}\n", |
| 290 | " \n", |
| 291 | "\n", |
| 292 | "p = list(p_nn_epandage, p_nn_nonpollue, p_nn_chauffage)\n", |
| 293 | "forecasts_2 = lapply(1:length(data), function(index) ( if (is.na(p[[2]]$forecasts[[index]][1])) rep(NA,24) else p[[2]]$forecasts[[index]]$pred ) )\n", |
| 294 | "e1 = getErrors(data, forecasts_1, 17)\n", |
| 295 | " \n", |
| 296 | "e = list(e1,e2,e3)\n", |
| 297 | "yrange_MAPE = range(e1$MAPE, e2$MAPE, e3$MAPE)\n", |
| 298 | "yrange_abs = range(e1$abs, e2$abs, e3$abs)\n", |
| 299 | "yrange_RMSE = range(e1$RMSE, e2$RMSE, e3$RMSE)\n", |
| 300 | "ranges = list(yrange_MAPE,yrange_abs,yrange_RMSE)\n", |
| 301 | "\n", |
| 302 | "par(mfrow=c(1,3))\n", |
| 303 | "titles = paste(\"Erreur\",c(\"MAPE\",\"abs\",\"RMSE\"))\n", |
| 304 | "for (i in 1:3) #error type (MAPE,abs,RMSE)\n", |
| 305 | "{\n", |
| 306 | " for (j in 1:3) #model (nn,np,nz,pp,pz)\n", |
| 307 | " {\n", |
| 308 | " xlab = if (j>1) \"\" else \"Temps\"\n", |
| 309 | " ylab = if (j>1) \"\" else \"Erreur\"\n", |
| 310 | " main = if (j>1) \"\" else titles[i]\n", |
| 311 | " plot(e[[j]][[i]], type=\"l\", col=j, main=titles[i], xlab=xlab, ylab=ylab, ylim=ranges[[i]])\n", |
| 312 | " if (j<3)\n", |
| 313 | " par(new=TRUE)\n", |
| 314 | " }\n", |
| 315 | "}\n", |
| 316 | "\n", |
| 317 | "par(mfrow=c(1,2))\n", |
| 318 | "#p[[i]]$forecasts[[index]]\n", |
| 319 | "#futurs des blocs du passé pour le jour 2290 ::\n", |
| 320 | "futurs = lapply(1:length(p[[1]]$forecasts[[2290]]$indices),\n", |
| 321 | " function(index) ( data[[ p[[1]]$forecasts[[2290]]$indices[index]+1 ]]$pm10 ) )\n", |
| 322 | "#vrai futur (en rouge), vrai jour (en noir)\n", |
| 323 | "r_min = min( sapply( 1:length(futurs), function(index) ( min(futurs[[index]] ) ) ) )\n", |
| 324 | "r_max = max( sapply( 1:length(futurs), function(index) ( max(futurs[[index]] ) ) ) )\n", |
| 325 | "for (i in 1:length(futurs))\n", |
| 326 | "{\n", |
| 327 | " plot(futurs[[i]], col=1, ylim=c(r_min,r_max), type=\"l\")\n", |
| 328 | " if (i<length(futurs))\n", |
| 329 | " par(new=TRUE)\n", |
| 330 | "}\n", |
| 331 | "\n", |
| 332 | "plot(data[[2290]]$pm10, ylim=c(r_min, r_max), col=1, type=\"l\")\n", |
| 333 | " par(new=TRUE)\n", |
| 334 | "plot(data[[2291]]$pm10, ylim=c(r_min, r_max), col=2, type=\"l\")\n" |
| 335 | ] |
| 336 | }, |
| 337 | { |
| 338 | "cell_type": "markdown", |
| 339 | "metadata": {}, |
| 340 | "source": [ |
| 341 | "Meilleurs results: nn et nz (np moins bon)" |
| 342 | ] |
| 343 | }, |
| 344 | { |
| 345 | "cell_type": "code", |
| 346 | "execution_count": null, |
| 347 | "metadata": { |
| 348 | "collapsed": false |
| 349 | }, |
| 350 | "outputs": [], |
| 351 | "source": [ |
| 352 | "#%%TODO: analyse sur les trois périodes indiquées par Michel ; simtype==\"exo\" par defaut\n", |
| 353 | "#16/03/2015 2288\n", |
| 354 | "p_nn_epandage = predictPM10(data, 2287, 2293, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"mix\")" |
| 355 | ] |
| 356 | }, |
| 357 | { |
| 358 | "cell_type": "code", |
| 359 | "execution_count": null, |
| 360 | "metadata": { |
| 361 | "collapsed": false |
| 362 | }, |
| 363 | "outputs": [], |
| 364 | "source": [ |
| 365 | "p_nn_epandage = predictPM10(data, 2287, 2293, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"mix\")" |
| 366 | ] |
| 367 | }, |
| 368 | { |
| 369 | "cell_type": "code", |
| 370 | "execution_count": null, |
| 371 | "metadata": { |
| 372 | "collapsed": false |
| 373 | }, |
| 374 | "outputs": [], |
| 375 | "source": [ |
| 376 | "options(repr.plot.width=9, repr.plot.height=6)\n", |
| 377 | "plot(p_nn_epandage$errors$abs, type=\"l\", col=1, main=\"Erreur absolue\", xlab=\"Temps\",\n", |
| 378 | " ylab=\"Erreur\", ylim=range(p_nn_epandage$errors$abs))" |
| 379 | ] |
| 380 | }, |
| 381 | { |
| 382 | "cell_type": "code", |
| 383 | "execution_count": null, |
| 384 | "metadata": { |
| 385 | "collapsed": false |
| 386 | }, |
| 387 | "outputs": [], |
| 388 | "source": [ |
| 389 | "#19/01/2015 2232\n", |
| 390 | "p_nn_chauffage = predictPM10(data, 2231, 2237, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE)" |
| 391 | ] |
| 392 | }, |
| 393 | { |
| 394 | "cell_type": "code", |
| 395 | "execution_count": null, |
| 396 | "metadata": { |
| 397 | "collapsed": false |
| 398 | }, |
| 399 | "outputs": [], |
| 400 | "source": [ |
| 401 | "#23/02/2015 2267\n", |
| 402 | "p_nn_nonpollue = predictPM10(data, 2266, 2272, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"mix\")" |
| 403 | ] |
| 404 | }, |
| 405 | { |
| 406 | "cell_type": "code", |
| 407 | "execution_count": null, |
| 408 | "metadata": { |
| 409 | "collapsed": false |
| 410 | }, |
| 411 | "outputs": [], |
| 412 | "source": [ |
| 413 | "plot(p_nn_nonpollue$errors$abs, type=\"l\", col=2, main=\"Erreur absolue\", xlab=\"Temps\",\n", |
| 414 | " ylab=\"Erreur\", ylim=range(p_nn_nonpollue$errors$abs))" |
| 415 | ] |
| 416 | }, |
| 417 | { |
| 418 | "cell_type": "code", |
| 419 | "execution_count": null, |
| 420 | "metadata": { |
| 421 | "collapsed": false |
| 422 | }, |
| 423 | "outputs": [], |
| 424 | "source": [ |
| 425 | "library(ppmfun)\n", |
| 426 | "data = getData(\"local\", \"7h\")\n", |
| 427 | "p_nn_epandage = predictPM10(data, 2287, 2293, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"endo\")\n", |
| 428 | "p_nn_nonpollue = predictPM10(data, 2266, 2272, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"endo\")\n", |
| 429 | "p_nn_chauffage = predictPM10(data, 2231, 2237, 0, 0, \"Neighbors\", \"Neighbors\", sameSeaon=TRUE, simtype=\"endo\")" |
| 430 | ] |
| 431 | }, |
| 432 | { |
| 433 | "cell_type": "code", |
| 434 | "execution_count": null, |
| 435 | "metadata": { |
| 436 | "collapsed": false |
| 437 | }, |
| 438 | "outputs": [], |
| 439 | "source": [ |
| 440 | "p = list(p_nn_epandage, p_nn_nonpollue, p_nn_chauffage)\n", |
| 441 | "#yrange_MAPE = range(p[[1]]$errors$MAPE, p[[2]]$errors$MAPE, p[[3]]$errors$MAPE)\n", |
| 442 | "#yrange_abs = range(p[[1]]$errors$abs, p[[2]]$errors$abs, p[[3]]$errors$abs)\n", |
| 443 | "#yrange_RMSE = range(p[[1]]$errors$RMSE, p[[2]]$errors$RMSE, p[[3]]$errors$RMSE)\n", |
| 444 | "#ranges = list(yrange_MAPE,yrange_abs,yrange_RMSE)\n", |
| 445 | "print(p[[1]]$forecasts[[2290]])" |
| 446 | ] |
| 447 | }, |
| 448 | { |
| 449 | "cell_type": "code", |
| 450 | "execution_count": null, |
| 451 | "metadata": { |
| 452 | "collapsed": false |
| 453 | }, |
| 454 | "outputs": [], |
| 455 | "source": [ |
| 456 | "par(mfrow=c(1,3))\n", |
| 457 | "titles = paste(\"Erreur\",c(\"MAPE\",\"abs\",\"RMSE\"))\n", |
| 458 | "for (i in 1:3) #error type (MAPE,abs,RMSE)\n", |
| 459 | "{\n", |
| 460 | " for (j in 1:5) #model (nn,np,nz,pp,pz)\n", |
| 461 | " {\n", |
| 462 | " xlab = if (j>1) \"\" else \"Temps\"\n", |
| 463 | " ylab = if (j>1) \"\" else \"Erreur\"\n", |
| 464 | " main = if (j>1) \"\" else titles[i]\n", |
| 465 | " plot(p[[j]]$errors[[i]], type=\"l\", col=j, main=titles[i], xlab=xlab, ylab=ylab, ylim=ranges[[i]])\n", |
| 466 | " if (j<5)\n", |
| 467 | " par(new=TRUE)\n", |
| 468 | " }\n", |
| 469 | "}" |
| 470 | ] |
| 471 | }, |
| 472 | { |
| 473 | "cell_type": "markdown", |
| 474 | "metadata": {}, |
| 475 | "source": [ |
| 476 | "## Bilan\n", |
| 477 | "\n", |
| 478 | "TODO" |
| 479 | ] |
| 480 | } |
| 481 | ], |
| 482 | "metadata": { |
| 483 | "kernelspec": { |
| 484 | "display_name": "R", |
| 485 | "language": "R", |
| 486 | "name": "ir" |
| 487 | }, |
| 488 | "language_info": { |
| 489 | "codemirror_mode": "r", |
| 490 | "file_extension": ".r", |
| 491 | "mimetype": "text/x-r-source", |
| 492 | "name": "R", |
| 493 | "pygments_lexer": "r", |
| 494 | "version": "3.3.2" |
| 495 | } |
| 496 | }, |
| 497 | "nbformat": 4, |
| 498 | "nbformat_minor": 2 |
| 499 | } |