3dac8ec499fbbfe706e343de8c812b09d17fb9dd
[talweg.git] / reports / report.ipynb
1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {
6 "deletable": true,
7 "editable": true
8 },
9 "source": [
10 "\n",
11 "\n",
12 "<h2>Introduction</h2>\n",
13 "\n",
14 "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\"\n",
15 "(la seule dont on a parlé).<br>Il semble que le mieux soit\n",
16 "\n",
17 " * simtype=\"exo\" ou \"mix\" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)\n",
18 " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n",
19 " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n",
20 "\n",
21 "J'ai systématiquement comparé à une approche naïve : la moyennes des lendemains des jours\n",
22 "\"similaires\" dans tout le passé ; à chaque fois sans prédiction du saut (sauf pour Neighbors :\n",
23 "prédiction basée sur les poids calculés).\n",
24 "\n",
25 "Ensuite j'affiche les erreurs, quelques courbes prévues/mesurées, quelques filaments puis les\n",
26 "histogrammes de quelques poids. Concernant les graphes de filaments, la moitié gauche du graphe\n",
27 "correspond aux jours similaires au jour courant, tandis que la moitié droite affiche les\n",
28 "lendemains : ce sont donc les voisinages tels qu'utilisés dans l'algorithme.\n",
29 "\n"
30 ]
31 },
32 {
33 "cell_type": "code",
34 "execution_count": null,
35 "metadata": {
36 "collapsed": false,
37 "deletable": true,
38 "editable": true
39 },
40 "outputs": [],
41 "source": [
42 "library(talweg)\n",
43 "\n",
44 "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n",
45 "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n",
46 "# Predict from P+1 to P+H included\n",
47 "H = 17\n",
48 "data = getData(ts_data, exo_data, input_tz = \"GMT\", working_tz=\"GMT\", predict_at=7)\n",
49 "\n",
50 "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n",
51 "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n",
52 "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")"
53 ]
54 },
55 {
56 "cell_type": "markdown",
57 "metadata": {
58 "deletable": true,
59 "editable": true
60 },
61 "source": [
62 "<h2 style=\"color:blue;font-size:2em\">Pollution par chauffage</h2>"
63 ]
64 },
65 {
66 "cell_type": "code",
67 "execution_count": null,
68 "metadata": {
69 "collapsed": false,
70 "deletable": true,
71 "editable": true
72 },
73 "outputs": [],
74 "source": [
75 "reload(\"../pkg\")\n",
76 "#p1 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"exo\")\n",
77 "#p2 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"endo\")\n",
78 "#p3 = computeForecast(data, indices_ch, \"Neighbors\", \"Zero\", horizon=H, simtype=\"mix\")\n",
79 "p4 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"exo\")\n",
80 "p5 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"endo\")\n",
81 "p6 = computeForecast(data, indices_ch, \"Neighbors2\", \"Zero\", horizon=H, simtype=\"mix\")"
82 ]
83 },
84 {
85 "cell_type": "code",
86 "execution_count": null,
87 "metadata": {
88 "collapsed": false
89 },
90 "outputs": [],
91 "source": [
92 "getSimilarDaysIndices(1000,10,TRUE,data)"
93 ]
94 },
95 {
96 "cell_type": "code",
97 "execution_count": null,
98 "metadata": {
99 "collapsed": false
100 },
101 "outputs": [],
102 "source": [
103 "as.POSIXlt(data$getTime(1000)[1])"
104 ]
105 },
106 {
107 "cell_type": "code",
108 "execution_count": null,
109 "metadata": {
110 "collapsed": false
111 },
112 "outputs": [],
113 "source": [
114 "e1 = computeError(data, p1, H)\n",
115 "e2 = computeError(data, p2, H)\n",
116 "e3 = computeError(data, p3, H)\n",
117 "e4 = computeError(data, p4, H)\n",
118 "e5 = computeError(data, p5, H)\n",
119 "e6 = computeError(data, p6, H)\n",
120 "plotError(list(e1,e2,e3,e4,e5,e6), cols=c(1,2,colors()[258], 4,5,6))"
121 ]
122 },
123 {
124 "cell_type": "code",
125 "execution_count": null,
126 "metadata": {
127 "collapsed": false,
128 "deletable": true,
129 "editable": true
130 },
131 "outputs": [],
132 "source": [
133 "plotError(list(e4,e1,e2,e3, e5,e6), cols=c(1,2,3,4,5,6))"
134 ]
135 },
136 {
137 "cell_type": "code",
138 "execution_count": null,
139 "metadata": {
140 "collapsed": false,
141 "deletable": true,
142 "editable": true
143 },
144 "outputs": [],
145 "source": [
146 "options(repr.plot.width=9, repr.plot.height=4)\n",
147 "par(mfrow=c(1,2))\n",
148 "\n",
149 "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
150 "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
151 "\n",
152 "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
153 "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
154 "\n",
155 "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
156 "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
157 "\n",
158 "# Bleu: prévue, noir: réalisée"
159 ]
160 },
161 {
162 "cell_type": "code",
163 "execution_count": null,
164 "metadata": {
165 "collapsed": false,
166 "deletable": true,
167 "editable": true
168 },
169 "outputs": [],
170 "source": [
171 "par(mfrow=c(1,2))\n",
172 "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
173 "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
174 "\n",
175 "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
176 "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
177 ]
178 },
179 {
180 "cell_type": "code",
181 "execution_count": null,
182 "metadata": {
183 "collapsed": false,
184 "deletable": true,
185 "editable": true
186 },
187 "outputs": [],
188 "source": [
189 "par(mfrow=c(1,2))\n",
190 "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
191 "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
192 "\n",
193 "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
194 "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
195 ]
196 },
197 {
198 "cell_type": "code",
199 "execution_count": null,
200 "metadata": {
201 "collapsed": false,
202 "deletable": true,
203 "editable": true
204 },
205 "outputs": [],
206 "source": [
207 "par(mfrow=c(1,2))\n",
208 "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
209 "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
210 "\n",
211 "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
212 "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
213 "\n",
214 "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
215 ]
216 },
217 {
218 "cell_type": "code",
219 "execution_count": null,
220 "metadata": {
221 "collapsed": false,
222 "deletable": true,
223 "editable": true
224 },
225 "outputs": [],
226 "source": [
227 "par(mfrow=c(1,2))\n",
228 "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
229 "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
230 "\n",
231 "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
232 "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
233 "\n",
234 "# - pollué à gauche, + pollué à droite"
235 ]
236 },
237 {
238 "cell_type": "code",
239 "execution_count": null,
240 "metadata": {
241 "collapsed": false,
242 "deletable": true,
243 "editable": true
244 },
245 "outputs": [],
246 "source": [
247 "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
248 "p_nn_exo$getParams(i_np)$window\n",
249 "p_nn_exo$getParams(i_p)$window\n",
250 "\n",
251 "p_nn_mix$getParams(i_np)$window\n",
252 "p_nn_mix$getParams(i_p)$window"
253 ]
254 },
255 {
256 "cell_type": "markdown",
257 "metadata": {
258 "deletable": true,
259 "editable": true
260 },
261 "source": [
262 "\n",
263 "\n",
264 "<h2 style=\"color:blue;font-size:2em\">Pollution par épandage</h2>"
265 ]
266 },
267 {
268 "cell_type": "code",
269 "execution_count": null,
270 "metadata": {
271 "collapsed": false,
272 "deletable": true,
273 "editable": true
274 },
275 "outputs": [],
276 "source": [
277 "p_nn_exo = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
278 "\thorizon=3, simtype=\"exo\")\n",
279 "p_nn_mix = computeForecast(data, indices_ep, \"Neighbors\", \"Neighbors\",\n",
280 "\thorizon=3, simtype=\"mix\")\n",
281 "p_az = computeForecast(data, indices_ep, \"Average\", \"Zero\",\n",
282 "\thorizon=3)\n",
283 "p_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\",\n",
284 "\thorizon=3, same_day=TRUE)"
285 ]
286 },
287 {
288 "cell_type": "code",
289 "execution_count": null,
290 "metadata": {
291 "collapsed": false,
292 "deletable": true,
293 "editable": true
294 },
295 "outputs": [],
296 "source": [
297 "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
298 "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
299 "e_az = computeError(data, p_az, 3)\n",
300 "e_pz = computeError(data, p_pz, 3)\n",
301 "options(repr.plot.width=9, repr.plot.height=7)\n",
302 "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
303 "\n",
304 "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
305 "\n",
306 "i_np = which.min(e_nn_exo$abs$indices)\n",
307 "i_p = which.max(e_nn_exo$abs$indices)"
308 ]
309 },
310 {
311 "cell_type": "code",
312 "execution_count": null,
313 "metadata": {
314 "collapsed": false,
315 "deletable": true,
316 "editable": true
317 },
318 "outputs": [],
319 "source": [
320 "options(repr.plot.width=9, repr.plot.height=4)\n",
321 "par(mfrow=c(1,2))\n",
322 "\n",
323 "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
324 "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
325 "\n",
326 "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
327 "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
328 "\n",
329 "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
330 "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
331 "\n",
332 "# Bleu: prévue, noir: réalisée"
333 ]
334 },
335 {
336 "cell_type": "code",
337 "execution_count": null,
338 "metadata": {
339 "collapsed": false,
340 "deletable": true,
341 "editable": true
342 },
343 "outputs": [],
344 "source": [
345 "par(mfrow=c(1,2))\n",
346 "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
347 "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
348 "\n",
349 "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
350 "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
351 ]
352 },
353 {
354 "cell_type": "code",
355 "execution_count": null,
356 "metadata": {
357 "collapsed": false,
358 "deletable": true,
359 "editable": true
360 },
361 "outputs": [],
362 "source": [
363 "par(mfrow=c(1,2))\n",
364 "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
365 "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
366 "\n",
367 "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
368 "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
369 ]
370 },
371 {
372 "cell_type": "code",
373 "execution_count": null,
374 "metadata": {
375 "collapsed": false,
376 "deletable": true,
377 "editable": true
378 },
379 "outputs": [],
380 "source": [
381 "par(mfrow=c(1,2))\n",
382 "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
383 "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
384 "\n",
385 "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
386 "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
387 "\n",
388 "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
389 ]
390 },
391 {
392 "cell_type": "code",
393 "execution_count": null,
394 "metadata": {
395 "collapsed": false,
396 "deletable": true,
397 "editable": true
398 },
399 "outputs": [],
400 "source": [
401 "par(mfrow=c(1,2))\n",
402 "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
403 "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
404 "\n",
405 "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
406 "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
407 "\n",
408 "# - pollué à gauche, + pollué à droite"
409 ]
410 },
411 {
412 "cell_type": "code",
413 "execution_count": null,
414 "metadata": {
415 "collapsed": false,
416 "deletable": true,
417 "editable": true
418 },
419 "outputs": [],
420 "source": [
421 "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
422 "p_nn_exo$getParams(i_np)$window\n",
423 "p_nn_exo$getParams(i_p)$window\n",
424 "\n",
425 "p_nn_mix$getParams(i_np)$window\n",
426 "p_nn_mix$getParams(i_p)$window"
427 ]
428 },
429 {
430 "cell_type": "markdown",
431 "metadata": {
432 "deletable": true,
433 "editable": true
434 },
435 "source": [
436 "\n",
437 "\n",
438 "<h2 style=\"color:blue;font-size:2em\">Semaine non polluée</h2>"
439 ]
440 },
441 {
442 "cell_type": "code",
443 "execution_count": null,
444 "metadata": {
445 "collapsed": false,
446 "deletable": true,
447 "editable": true
448 },
449 "outputs": [],
450 "source": [
451 "p_nn_exo = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
452 "\thorizon=3, simtype=\"exo\")\n",
453 "p_nn_mix = computeForecast(data, indices_np, \"Neighbors\", \"Neighbors\",\n",
454 "\thorizon=3, simtype=\"mix\")\n",
455 "p_az = computeForecast(data, indices_np, \"Average\", \"Zero\",\n",
456 "\thorizon=3)\n",
457 "p_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\",\n",
458 "\thorizon=3, same_day=FALSE)"
459 ]
460 },
461 {
462 "cell_type": "code",
463 "execution_count": null,
464 "metadata": {
465 "collapsed": false,
466 "deletable": true,
467 "editable": true
468 },
469 "outputs": [],
470 "source": [
471 "e_nn_exo = computeError(data, p_nn_exo, 3)\n",
472 "e_nn_mix = computeError(data, p_nn_mix, 3)\n",
473 "e_az = computeError(data, p_az, 3)\n",
474 "e_pz = computeError(data, p_pz, 3)\n",
475 "options(repr.plot.width=9, repr.plot.height=7)\n",
476 "plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4))\n",
477 "\n",
478 "# Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence\n",
479 "\n",
480 "i_np = which.min(e_nn_exo$abs$indices)\n",
481 "i_p = which.max(e_nn_exo$abs$indices)"
482 ]
483 },
484 {
485 "cell_type": "code",
486 "execution_count": null,
487 "metadata": {
488 "collapsed": false,
489 "deletable": true,
490 "editable": true
491 },
492 "outputs": [],
493 "source": [
494 "options(repr.plot.width=9, repr.plot.height=4)\n",
495 "par(mfrow=c(1,2))\n",
496 "\n",
497 "plotPredReal(data, p_nn_exo, i_np); title(paste(\"PredReal nn exo day\",i_np))\n",
498 "plotPredReal(data, p_nn_exo, i_p); title(paste(\"PredReal nn exo day\",i_p))\n",
499 "\n",
500 "plotPredReal(data, p_nn_mix, i_np); title(paste(\"PredReal nn mix day\",i_np))\n",
501 "plotPredReal(data, p_nn_mix, i_p); title(paste(\"PredReal nn mix day\",i_p))\n",
502 "\n",
503 "plotPredReal(data, p_az, i_np); title(paste(\"PredReal az day\",i_np))\n",
504 "plotPredReal(data, p_az, i_p); title(paste(\"PredReal az day\",i_p))\n",
505 "\n",
506 "# Bleu: prévue, noir: réalisée"
507 ]
508 },
509 {
510 "cell_type": "code",
511 "execution_count": null,
512 "metadata": {
513 "collapsed": false,
514 "deletable": true,
515 "editable": true
516 },
517 "outputs": [],
518 "source": [
519 "par(mfrow=c(1,2))\n",
520 "f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste(\"Filaments nn exo day\",i_np))\n",
521 "f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste(\"Filaments nn exo day\",i_p))\n",
522 "\n",
523 "f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste(\"Filaments nn mix day\",i_np))\n",
524 "f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste(\"Filaments nn mix day\",i_p))"
525 ]
526 },
527 {
528 "cell_type": "code",
529 "execution_count": null,
530 "metadata": {
531 "collapsed": false,
532 "deletable": true,
533 "editable": true
534 },
535 "outputs": [],
536 "source": [
537 "par(mfrow=c(1,2))\n",
538 "plotFilamentsBox(data, f_np_exo); title(paste(\"FilBox nn exo day\",i_np))\n",
539 "plotFilamentsBox(data, f_p_exo); title(paste(\"FilBox nn exo day\",i_p))\n",
540 "\n",
541 "plotFilamentsBox(data, f_np_mix); title(paste(\"FilBox nn mix day\",i_np))\n",
542 "plotFilamentsBox(data, f_p_mix); title(paste(\"FilBox nn mix day\",i_p))"
543 ]
544 },
545 {
546 "cell_type": "code",
547 "execution_count": null,
548 "metadata": {
549 "collapsed": false,
550 "deletable": true,
551 "editable": true
552 },
553 "outputs": [],
554 "source": [
555 "par(mfrow=c(1,2))\n",
556 "plotRelVar(data, f_np_exo); title(paste(\"StdDev nn exo day\",i_np))\n",
557 "plotRelVar(data, f_p_exo); title(paste(\"StdDev nn exo day\",i_p))\n",
558 "\n",
559 "plotRelVar(data, f_np_mix); title(paste(\"StdDev nn mix day\",i_np))\n",
560 "plotRelVar(data, f_p_mix); title(paste(\"StdDev nn mix day\",i_p))\n",
561 "\n",
562 "# Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir"
563 ]
564 },
565 {
566 "cell_type": "code",
567 "execution_count": null,
568 "metadata": {
569 "collapsed": false,
570 "deletable": true,
571 "editable": true
572 },
573 "outputs": [],
574 "source": [
575 "par(mfrow=c(1,2))\n",
576 "plotSimils(p_nn_exo, i_np); title(paste(\"Weights nn exo day\",i_np))\n",
577 "plotSimils(p_nn_exo, i_p); title(paste(\"Weights nn exo day\",i_p))\n",
578 "\n",
579 "plotSimils(p_nn_mix, i_np); title(paste(\"Weights nn mix day\",i_np))\n",
580 "plotSimils(p_nn_mix, i_p); title(paste(\"Weights nn mix day\",i_p))\n",
581 "\n",
582 "# - pollué à gauche, + pollué à droite"
583 ]
584 },
585 {
586 "cell_type": "code",
587 "execution_count": null,
588 "metadata": {
589 "collapsed": false,
590 "deletable": true,
591 "editable": true
592 },
593 "outputs": [],
594 "source": [
595 "# Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n",
596 "p_nn_exo$getParams(i_np)$window\n",
597 "p_nn_exo$getParams(i_p)$window\n",
598 "\n",
599 "p_nn_mix$getParams(i_np)$window\n",
600 "p_nn_mix$getParams(i_p)$window"
601 ]
602 },
603 {
604 "cell_type": "markdown",
605 "metadata": {
606 "deletable": true,
607 "editable": true
608 },
609 "source": [
610 "\n",
611 "\n",
612 "<h2>Bilan</h2>\n",
613 "\n",
614 "Problème difficile : on ne fait guère mieux qu'une naïve moyenne des lendemains des jours\n",
615 "similaires dans le passé, ce qui n'est pas loin de prédire une série constante égale à la\n",
616 "dernière valeur observée (méthode \"zéro\"). La persistence donne parfois de bons résultats\n",
617 "mais est trop instable (sensibilité à l'argument <code>same_day</code>).\n",
618 "\n",
619 "Comment améliorer la méthode ?"
620 ]
621 }
622 ],
623 "metadata": {
624 "kernelspec": {
625 "display_name": "R",
626 "language": "R",
627 "name": "ir"
628 },
629 "language_info": {
630 "codemirror_mode": "r",
631 "file_extension": ".r",
632 "mimetype": "text/x-r-source",
633 "name": "R",
634 "pygments_lexer": "r",
635 "version": "3.3.3"
636 }
637 },
638 "nbformat": 4,
639 "nbformat_minor": 2
640 }