Commit | Line | Data |
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fa8078f9 BA |
1 | { |
2 | "cells": [ | |
3 | { | |
4 | "cell_type": "code", | |
1e20780e | 5 | "execution_count": null, |
fa8078f9 BA |
6 | "metadata": { |
7 | "collapsed": false | |
8 | }, | |
9 | "outputs": [], | |
10 | "source": [ | |
11 | "library(talweg)" | |
12 | ] | |
13 | }, | |
14 | { | |
15 | "cell_type": "code", | |
1e20780e | 16 | "execution_count": null, |
fa8078f9 BA |
17 | "metadata": { |
18 | "collapsed": false | |
19 | }, | |
20 | "outputs": [], | |
21 | "source": [ | |
99f83c9a BA |
22 | "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc.csv\",package=\"talweg\"))\n", |
23 | "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n", | |
24 | "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=13)" | |
fa8078f9 BA |
25 | ] |
26 | }, | |
27 | { | |
28 | "cell_type": "markdown", | |
29 | "metadata": {}, | |
30 | "source": [ | |
31 | "## Introduction\n", | |
32 | "\n", | |
33 | "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).<br>Il semble que le mieux soit\n", | |
34 | "\n", | |
35 | " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n", | |
36 | " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", | |
37 | " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", | |
38 | "\n", | |
99f83c9a BA |
39 | "(valeurs par défaut).\n", |
40 | "\n", | |
fa8078f9 BA |
41 | "J'ai systématiquement comparé à deux autres approches : la persistence et la moyennes des lendemains des jours \"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors : prédiction basée sur les poids calculés).\n", |
42 | "\n", | |
43 | "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n", | |
44 | "\n", | |
45 | "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>" | |
46 | ] | |
47 | }, | |
48 | { | |
49 | "cell_type": "code", | |
1e20780e | 50 | "execution_count": null, |
fa8078f9 BA |
51 | "metadata": { |
52 | "collapsed": false | |
53 | }, | |
54 | "outputs": [], | |
55 | "source": [ | |
99f83c9a BA |
56 | "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", |
57 | "p_ch_nn = computeForecast(data,indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", | |
58 | "p_ch_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", same_day=TRUE)\n", | |
59 | "p_ch_az = computeForecast(data, indices_ch, \"Average\", \"Zero\") #, memory=183)\n", | |
60 | "#p_ch_zz = computeForecast(data, indices_ch, \"Zero\", \"Zero\")" | |
fa8078f9 BA |
61 | ] |
62 | }, | |
63 | { | |
64 | "cell_type": "code", | |
1e20780e | 65 | "execution_count": null, |
fa8078f9 BA |
66 | "metadata": { |
67 | "collapsed": false | |
68 | }, | |
1e20780e | 69 | "outputs": [], |
fa8078f9 | 70 | "source": [ |
99f83c9a BA |
71 | "e_ch_nn = computeError(data, p_ch_nn)\n", |
72 | "e_ch_pz = computeError(data, p_ch_pz)\n", | |
73 | "e_ch_az = computeError(data, p_ch_az)\n", | |
74 | "#e_ch_zz = computeError(data, p_ch_zz)\n", | |
75 | "options(repr.plot.width=9, repr.plot.height=7)\n", | |
fa8078f9 BA |
76 | "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n", |
77 | "\n", | |
78 | "#Noir: neighbors, rouge: persistence, vert: moyenne" | |
79 | ] | |
80 | }, | |
fa8078f9 BA |
81 | { |
82 | "cell_type": "code", | |
1e20780e | 83 | "execution_count": null, |
fa8078f9 BA |
84 | "metadata": { |
85 | "collapsed": false | |
86 | }, | |
1e20780e | 87 | "outputs": [], |
fa8078f9 BA |
88 | "source": [ |
89 | "par(mfrow=c(1,2))\n", | |
90 | "options(repr.plot.width=9, repr.plot.height=4)\n", | |
91 | "plotPredReal(data, p_ch_nn, 3)\n", | |
92 | "plotPredReal(data, p_ch_nn, 4)\n", | |
93 | "\n", | |
94 | "#Bleu: prévue, noir: réalisée" | |
95 | ] | |
96 | }, | |
97 | { | |
98 | "cell_type": "markdown", | |
99 | "metadata": {}, | |
100 | "source": [ | |
99f83c9a | 101 | "Prédictions très lisses." |
fa8078f9 BA |
102 | ] |
103 | }, | |
104 | { | |
105 | "cell_type": "code", | |
1e20780e | 106 | "execution_count": null, |
fa8078f9 BA |
107 | "metadata": { |
108 | "collapsed": false | |
109 | }, | |
1e20780e | 110 | "outputs": [], |
fa8078f9 BA |
111 | "source": [ |
112 | "par(mfrow=c(1,2))\n", | |
113 | "plotPredReal(data, p_ch_az, 3)\n", | |
114 | "plotPredReal(data, p_ch_az, 4)" | |
115 | ] | |
116 | }, | |
fa8078f9 BA |
117 | { |
118 | "cell_type": "code", | |
1e20780e | 119 | "execution_count": null, |
fa8078f9 BA |
120 | "metadata": { |
121 | "collapsed": false | |
122 | }, | |
1e20780e | 123 | "outputs": [], |
fa8078f9 BA |
124 | "source": [ |
125 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
126 | "f3_ch = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n", |
127 | "f4_ch = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)" | |
fa8078f9 BA |
128 | ] |
129 | }, | |
130 | { | |
131 | "cell_type": "code", | |
1e20780e | 132 | "execution_count": null, |
fa8078f9 BA |
133 | "metadata": { |
134 | "collapsed": false | |
135 | }, | |
1e20780e | 136 | "outputs": [], |
fa8078f9 BA |
137 | "source": [ |
138 | "par(mfrow=c(2,2))\n", | |
99f83c9a BA |
139 | "options(repr.plot.width=9, repr.plot.height=7)\n", |
140 | "plotFilamentsBox(data, f3_ch$indices)\n", | |
141 | "plotFilamentsBox(data, f3_ch$indices+1)\n", | |
142 | "plotFilamentsBox(data, f4_ch$indices)\n", | |
143 | "plotFilamentsBox(data, f4_ch$indices+1)\n", | |
144 | "\n", | |
145 | "#En haut : jour 3 + lendemain (4) ; en bas : jour 4 + lendemain (5)\n", | |
146 | "#À gauche : premières 24h ; à droite : 24h suivantes" | |
fa8078f9 BA |
147 | ] |
148 | }, | |
149 | { | |
150 | "cell_type": "markdown", | |
151 | "metadata": {}, | |
152 | "source": [ | |
99f83c9a | 153 | "Peu de voisins, les courbes sont assez isolées (en particulier les lendemains)." |
fa8078f9 BA |
154 | ] |
155 | }, | |
156 | { | |
157 | "cell_type": "code", | |
158 | "execution_count": null, | |
159 | "metadata": { | |
160 | "collapsed": false | |
161 | }, | |
162 | "outputs": [], | |
163 | "source": [ | |
164 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
165 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
166 | "plotRelativeVariability(data, f3_ch$indices)\n", | |
167 | "plotRelativeVariability(data, f4_ch$indices)\n", | |
fa8078f9 | 168 | "\n", |
99f83c9a | 169 | "#Variabilité sur 60 courbes au hasard en rouge ; sur nos 60 voisins (+ lendemains) en noir" |
fa8078f9 BA |
170 | ] |
171 | }, | |
172 | { | |
173 | "cell_type": "markdown", | |
174 | "metadata": {}, | |
175 | "source": [ | |
99f83c9a | 176 | "Il faudrait que la courbe noire soit nettement plus basse que la courbe rouge." |
fa8078f9 BA |
177 | ] |
178 | }, | |
179 | { | |
180 | "cell_type": "code", | |
181 | "execution_count": null, | |
182 | "metadata": { | |
183 | "collapsed": false | |
184 | }, | |
185 | "outputs": [], | |
186 | "source": [ | |
187 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
188 | "plotSimils(p_ch_nn, 3)\n", |
189 | "plotSimils(p_ch_nn, 4)\n", | |
190 | "\n", | |
191 | "#Non pollué à gauche, pollué à droite" | |
fa8078f9 BA |
192 | ] |
193 | }, | |
194 | { | |
195 | "cell_type": "markdown", | |
196 | "metadata": {}, | |
197 | "source": [ | |
99f83c9a | 198 | "Poids plus concentrés autour de 0 pour un jour plus pollué." |
fa8078f9 BA |
199 | ] |
200 | }, | |
201 | { | |
202 | "cell_type": "code", | |
203 | "execution_count": null, | |
204 | "metadata": { | |
205 | "collapsed": false | |
206 | }, | |
207 | "outputs": [], | |
208 | "source": [ | |
99f83c9a BA |
209 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", |
210 | "p_ch_nn$getParams(3)$window\n", | |
211 | "p_ch_nn$getParams(4)$window" | |
212 | ] | |
213 | }, | |
214 | { | |
215 | "cell_type": "markdown", | |
216 | "metadata": {}, | |
217 | "source": [ | |
218 | "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>" | |
fa8078f9 BA |
219 | ] |
220 | }, | |
221 | { | |
222 | "cell_type": "code", | |
223 | "execution_count": null, | |
224 | "metadata": { | |
225 | "collapsed": false | |
226 | }, | |
227 | "outputs": [], | |
228 | "source": [ | |
99f83c9a BA |
229 | "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", |
230 | "p_ep_nn = computeForecast(data,indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", | |
231 | "p_ep_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", same_day=TRUE)\n", | |
232 | "p_ep_az = computeForecast(data, indices_ep, \"Average\", \"Zero\") #, memory=183)\n", | |
233 | "#p_ep_zz = computeForecast(data, indices_ep, \"Zero\", \"Zero\")" | |
fa8078f9 BA |
234 | ] |
235 | }, | |
236 | { | |
237 | "cell_type": "code", | |
238 | "execution_count": null, | |
239 | "metadata": { | |
240 | "collapsed": false | |
241 | }, | |
242 | "outputs": [], | |
243 | "source": [ | |
99f83c9a BA |
244 | "e_ep_nn = computeError(data, p_ep_nn)\n", |
245 | "e_ep_pz = computeError(data, p_ep_pz)\n", | |
246 | "e_ep_az = computeError(data, p_ep_az)\n", | |
247 | "#e_ep_zz = computeError(data, p_ep_zz)\n", | |
248 | "options(repr.plot.width=9, repr.plot.height=7)\n", | |
fa8078f9 BA |
249 | "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n", |
250 | "\n", | |
251 | "#Noir: neighbors, rouge: persistence, vert: moyenne" | |
252 | ] | |
253 | }, | |
254 | { | |
255 | "cell_type": "markdown", | |
256 | "metadata": {}, | |
257 | "source": [ | |
99f83c9a | 258 | "Neighbors et Average comparables, Persistence moins performante." |
fa8078f9 BA |
259 | ] |
260 | }, | |
261 | { | |
262 | "cell_type": "code", | |
263 | "execution_count": null, | |
264 | "metadata": { | |
265 | "collapsed": false | |
266 | }, | |
267 | "outputs": [], | |
268 | "source": [ | |
269 | "par(mfrow=c(1,2))\n", | |
270 | "options(repr.plot.width=9, repr.plot.height=4)\n", | |
99f83c9a BA |
271 | "plotPredReal(data, p_ep_nn, 6)\n", |
272 | "plotPredReal(data, p_ep_nn, 3)\n", | |
fa8078f9 BA |
273 | "\n", |
274 | "#Bleu: prévue, noir: réalisée" | |
275 | ] | |
276 | }, | |
277 | { | |
278 | "cell_type": "markdown", | |
279 | "metadata": {}, | |
280 | "source": [ | |
99f83c9a | 281 | "À gauche un jour \"bien\" prévu, à droite le pic d'erreur (jour 3)." |
fa8078f9 BA |
282 | ] |
283 | }, | |
284 | { | |
285 | "cell_type": "code", | |
286 | "execution_count": null, | |
287 | "metadata": { | |
288 | "collapsed": false | |
289 | }, | |
290 | "outputs": [], | |
291 | "source": [ | |
292 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
293 | "plotPredReal(data, p_ep_az, 6)\n", |
294 | "plotPredReal(data, p_ep_az, 3)" | |
fa8078f9 BA |
295 | ] |
296 | }, | |
297 | { | |
298 | "cell_type": "markdown", | |
299 | "metadata": {}, | |
300 | "source": [ | |
99f83c9a | 301 | "Average : autre type de prévision." |
fa8078f9 BA |
302 | ] |
303 | }, | |
304 | { | |
305 | "cell_type": "code", | |
306 | "execution_count": null, | |
307 | "metadata": { | |
308 | "collapsed": false | |
309 | }, | |
310 | "outputs": [], | |
311 | "source": [ | |
312 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
313 | "f6_ep = computeFilaments(data, p_ep_nn$getIndexInData(6), plot=TRUE)\n", |
314 | "f3_ep = computeFilaments(data, p_ep_nn$getIndexInData(3), plot=TRUE)" | |
315 | ] | |
316 | }, | |
317 | { | |
318 | "cell_type": "code", | |
319 | "execution_count": null, | |
320 | "metadata": { | |
321 | "collapsed": false | |
322 | }, | |
323 | "outputs": [], | |
324 | "source": [ | |
325 | "par(mfrow=c(2,2))\n", | |
326 | "options(repr.plot.width=9, repr.plot.height=7)\n", | |
327 | "plotFilamentsBox(data, f6_ep$indices)\n", | |
328 | "plotFilamentsBox(data, f6_ep$indices+1)\n", | |
329 | "plotFilamentsBox(data, f3_ep$indices)\n", | |
330 | "plotFilamentsBox(data, f3_ep$indices+1)\n", | |
331 | "\n", | |
332 | "#En haut : jour 4 + lendemain (5) ; en bas : jour 6 + lendemain (7)\n", | |
333 | "#À gauche : premières 24h ; à droite : 24h suivantes" | |
fa8078f9 BA |
334 | ] |
335 | }, | |
336 | { | |
337 | "cell_type": "code", | |
338 | "execution_count": null, | |
339 | "metadata": { | |
340 | "collapsed": false | |
341 | }, | |
342 | "outputs": [], | |
343 | "source": [ | |
344 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
345 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
346 | "plotRelativeVariability(data, f6_ep$indices)\n", | |
347 | "plotRelativeVariability(data, f3_ep$indices)\n", | |
348 | "\n", | |
349 | "#Variabilité sur 60 courbes au hasard en rouge ; sur nos 60 voisins (+ lendemains) en noir" | |
350 | ] | |
351 | }, | |
352 | { | |
353 | "cell_type": "markdown", | |
354 | "metadata": {}, | |
355 | "source": [ | |
356 | "Il faudrait que la courbe noire soit nettement plus basse que la courbe rouge..." | |
357 | ] | |
358 | }, | |
359 | { | |
360 | "cell_type": "code", | |
361 | "execution_count": null, | |
362 | "metadata": { | |
363 | "collapsed": false | |
364 | }, | |
365 | "outputs": [], | |
366 | "source": [ | |
367 | "par(mfrow=c(1,2))\n", | |
368 | "plotSimils(p_ep_nn, 6)\n", | |
369 | "plotSimils(p_ep_nn, 3)" | |
fa8078f9 BA |
370 | ] |
371 | }, | |
372 | { | |
373 | "cell_type": "markdown", | |
374 | "metadata": {}, | |
375 | "source": [ | |
376 | "Même observation concernant les poids : concentrés près de zéro pour les prédictions avec peu de voisins." | |
377 | ] | |
378 | }, | |
99f83c9a BA |
379 | { |
380 | "cell_type": "code", | |
381 | "execution_count": null, | |
382 | "metadata": { | |
383 | "collapsed": false | |
384 | }, | |
385 | "outputs": [], | |
386 | "source": [ | |
387 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", | |
388 | "p_ep_nn$getParams(6)$window\n", | |
389 | "p_ep_nn$getParams(3)$window" | |
390 | ] | |
391 | }, | |
fa8078f9 BA |
392 | { |
393 | "cell_type": "markdown", | |
394 | "metadata": {}, | |
395 | "source": [ | |
99f83c9a | 396 | "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>" |
fa8078f9 BA |
397 | ] |
398 | }, | |
399 | { | |
400 | "cell_type": "code", | |
401 | "execution_count": null, | |
402 | "metadata": { | |
403 | "collapsed": false | |
404 | }, | |
405 | "outputs": [], | |
406 | "source": [ | |
99f83c9a BA |
407 | "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", |
408 | "p_np_nn = computeForecast(data,indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", | |
409 | "p_np_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", same_day=FALSE)\n", | |
410 | "p_np_az = computeForecast(data, indices_np, \"Average\", \"Zero\") #, memory=183)\n", | |
411 | "#p_np_zz = computeForecast(data, indices_np, \"Zero\", \"Zero\")" | |
fa8078f9 BA |
412 | ] |
413 | }, | |
414 | { | |
415 | "cell_type": "code", | |
416 | "execution_count": null, | |
417 | "metadata": { | |
418 | "collapsed": false | |
419 | }, | |
420 | "outputs": [], | |
421 | "source": [ | |
99f83c9a BA |
422 | "e_np_nn = computeError(data, p_np_nn)\n", |
423 | "e_np_pz = computeError(data, p_np_pz)\n", | |
424 | "e_np_az = computeError(data, p_np_az)\n", | |
425 | "#e_np_zz = computeError(data, p_np_zz)\n", | |
426 | "options(repr.plot.width=9, repr.plot.height=7)\n", | |
fa8078f9 BA |
427 | "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n", |
428 | "\n", | |
429 | "#Noir: neighbors, rouge: persistence, vert: moyenne" | |
430 | ] | |
431 | }, | |
432 | { | |
433 | "cell_type": "markdown", | |
434 | "metadata": {}, | |
435 | "source": [ | |
99f83c9a | 436 | "Performances des méthodes \"Average\" et \"Neighbors\" identiques ; mauvais résultats pour la persistence." |
fa8078f9 BA |
437 | ] |
438 | }, | |
439 | { | |
440 | "cell_type": "code", | |
441 | "execution_count": null, | |
442 | "metadata": { | |
443 | "collapsed": false | |
444 | }, | |
445 | "outputs": [], | |
446 | "source": [ | |
447 | "par(mfrow=c(1,2))\n", | |
448 | "options(repr.plot.width=9, repr.plot.height=4)\n", | |
99f83c9a | 449 | "plotPredReal(data, p_np_nn, 5)\n", |
fa8078f9 | 450 | "plotPredReal(data, p_np_nn, 3)\n", |
fa8078f9 BA |
451 | "\n", |
452 | "#Bleu: prévue, noir: réalisée" | |
453 | ] | |
454 | }, | |
455 | { | |
99f83c9a BA |
456 | "cell_type": "code", |
457 | "execution_count": null, | |
458 | "metadata": { | |
459 | "collapsed": false | |
460 | }, | |
461 | "outputs": [], | |
fa8078f9 | 462 | "source": [ |
99f83c9a BA |
463 | "par(mfrow=c(1,2))\n", |
464 | "plotPredReal(data, p_np_az, 5)\n", | |
465 | "plotPredReal(data, p_np_az, 3)" | |
fa8078f9 BA |
466 | ] |
467 | }, | |
468 | { | |
469 | "cell_type": "code", | |
470 | "execution_count": null, | |
471 | "metadata": { | |
472 | "collapsed": false | |
473 | }, | |
474 | "outputs": [], | |
475 | "source": [ | |
476 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
477 | "f5_np = computeFilaments(data, p_np_nn$getIndexInData(5), plot=TRUE)\n", |
478 | "f3_np = computeFilaments(data, p_np_nn$getIndexInData(3), plot=TRUE)" | |
fa8078f9 BA |
479 | ] |
480 | }, | |
481 | { | |
482 | "cell_type": "markdown", | |
483 | "metadata": {}, | |
484 | "source": [ | |
99f83c9a BA |
485 | "Jours \"typiques\", donc beaucoup de voisins." |
486 | ] | |
487 | }, | |
488 | { | |
489 | "cell_type": "code", | |
490 | "execution_count": null, | |
491 | "metadata": { | |
492 | "collapsed": false | |
493 | }, | |
494 | "outputs": [], | |
495 | "source": [ | |
496 | "par(mfrow=c(2,2))\n", | |
497 | "options(repr.plot.width=9, repr.plot.height=7)\n", | |
498 | "plotFilamentsBox(data, f5_np$indices)\n", | |
499 | "plotFilamentsBox(data, f5_np$indices+1)\n", | |
500 | "plotFilamentsBox(data, f3_np$indices)\n", | |
501 | "plotFilamentsBox(data, f3_np$indices+1)\n", | |
502 | "\n", | |
503 | "#En haut : jour 3 + lendemain (4) ; en bas : jour 6 + lendemain (7)\n", | |
504 | "#À gauche : premières 24h ; à droite : 24h suivantes" | |
fa8078f9 BA |
505 | ] |
506 | }, | |
507 | { | |
508 | "cell_type": "code", | |
509 | "execution_count": null, | |
510 | "metadata": { | |
511 | "collapsed": false | |
512 | }, | |
513 | "outputs": [], | |
514 | "source": [ | |
515 | "par(mfrow=c(1,2))\n", | |
99f83c9a BA |
516 | "options(repr.plot.width=9, repr.plot.height=4)\n", |
517 | "plotRelativeVariability(data, f5_np$indices)\n", | |
518 | "plotRelativeVariability(data, f3_np$indices)\n", | |
519 | "\n", | |
520 | "#Variabilité sur 60 courbes au hasard en rouge ; sur nos 60 voisins (+ lendemains) en noir" | |
fa8078f9 BA |
521 | ] |
522 | }, | |
523 | { | |
524 | "cell_type": "markdown", | |
525 | "metadata": {}, | |
526 | "source": [ | |
99f83c9a | 527 | "Situation meilleure que dans les autres cas, mais assez difficile tout de même." |
fa8078f9 BA |
528 | ] |
529 | }, | |
530 | { | |
531 | "cell_type": "code", | |
532 | "execution_count": null, | |
533 | "metadata": { | |
534 | "collapsed": false | |
535 | }, | |
536 | "outputs": [], | |
537 | "source": [ | |
99f83c9a BA |
538 | "par(mfrow=c(1,2))\n", |
539 | "plotSimils(p_np_nn, 5)\n", | |
540 | "plotSimils(p_np_nn, 3)" | |
fa8078f9 BA |
541 | ] |
542 | }, | |
543 | { | |
544 | "cell_type": "markdown", | |
545 | "metadata": {}, | |
546 | "source": [ | |
99f83c9a BA |
547 | "Répartition des poids difficile à interpréter." |
548 | ] | |
549 | }, | |
550 | { | |
551 | "cell_type": "code", | |
552 | "execution_count": null, | |
553 | "metadata": { | |
554 | "collapsed": false | |
555 | }, | |
556 | "outputs": [], | |
557 | "source": [ | |
558 | "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", | |
559 | "p_np_nn$getParams(5)$window\n", | |
560 | "p_np_nn$getParams(3)$window" | |
fa8078f9 BA |
561 | ] |
562 | }, | |
563 | { | |
564 | "cell_type": "markdown", | |
565 | "metadata": {}, | |
566 | "source": [ | |
567 | "## Bilan\n", | |
568 | "\n", | |
569 | "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n", | |
570 | "\n", | |
571 | "Comment améliorer la méthode ?" | |
572 | ] | |
573 | } | |
574 | ], | |
575 | "metadata": { | |
576 | "kernelspec": { | |
577 | "display_name": "R", | |
578 | "language": "R", | |
579 | "name": "ir" | |
580 | }, | |
581 | "language_info": { | |
582 | "codemirror_mode": "r", | |
583 | "file_extension": ".r", | |
584 | "mimetype": "text/x-r-source", | |
585 | "name": "R", | |
586 | "pygments_lexer": "r", | |
587 | "version": "3.3.2" | |
588 | } | |
589 | }, | |
590 | "nbformat": 4, | |
591 | "nbformat_minor": 2 | |
592 | } |