From: Benjamin Auder Date: Wed, 1 Mar 2017 15:51:25 +0000 (+0100) Subject: a few simplifications in computeFilaments, prepare markdown report X-Git-Url: https://git.auder.net/doc/html/css/current/pieces/%24%7BgetWhatsApp%28link%29%7D?a=commitdiff_plain;h=8f84543c2d469933d0fc122543d3eeb824970011;p=talweg.git a few simplifications in computeFilaments, prepare markdown report --- diff --git a/NOTES b/NOTES index 0d059ab..78bebc0 100644 --- a/NOTES +++ b/NOTES @@ -41,3 +41,5 @@ Neighbors: - same_season --> same_day(?) data : carrément supprimer les bouts tronqués au début et à la fin --> ça simplifie beaucoup + +shiny --> visualisation courbes, clic shift date sur 24h....... diff --git a/pkg/R/plot.R b/pkg/R/plot.R index 372abf0..eb0c81a 100644 --- a/pkg/R/plot.R +++ b/pkg/R/plot.R @@ -142,7 +142,8 @@ plotFbox <- function(data, indices=seq_len(data$getSize())) #' Get similar days in the past, as black as distances are small #' #' @param data Object as returned by \code{getData} -#' @param index Index in data (integer or date) +#' @param pred Object of class Forecast +#' @param index Index in forecast (integer or date) #' @param limit Number of neighbors to consider #' @param plot Should the result be plotted? #' @@ -154,40 +155,33 @@ plotFbox <- function(data, indices=seq_len(data$getSize())) #' } #' #' @export -computeFilaments <- function(data, index, limit=60, plot=TRUE) +computeFilaments <- function(data, pred, index, limit=60, plot=TRUE) { - ref_serie = data$getCenteredSerie(index) + ref_serie = data$getCenteredSerie( pred$getIndexInData(index) ) if (any(is.na(ref_serie))) stop("computeFilaments requires a serie without NAs") - # Determine indices of no-NAs days followed by no-NAs tomorrows - fdays = getNoNA2(data, 1, dateIndexToInteger(index,data)-1) - # Series + tomorrows in columns, ref_serie first - centered_series = data$getCenteredSeries(fdays) - - # Obtain neighbors (closest for euclidian norm) - L = length(ref_serie) - distances = sqrt( colSums( (centered_series - ref_serie)^2 / L ) ) - sorted_distances = sort(distances, index.return=TRUE) - # Compute colors for each neighbor (from darkest to lightest) - nn = min(limit, length(distances)) - min_dist = min(sorted_distances$x[1:nn]) - max_dist = max(sorted_distances$x[1:nn]) - color_values = floor( 19.5 * (sorted_distances$x[1:nn]-min_dist) / (max_dist-min_dist) ) + 1 + sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE) + nn = min(limit, length(sorted_dists$x)) + min_dist = min(sorted_dists$x[1:nn]) + max_dist = max(sorted_dists$x[1:nn]) + color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1 colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number if (plot) { # Complete series with (past and present) tomorrows - ref_serie = c(ref_serie,data$getCenteredSerie(index+1)) - centered_series = rbind( centered_series, data$getCenteredSeries(fdays+1) ) - yrange = quantile(cbind(ref_serie,centered_series), probs=c(0.025,0.975), na.rm=TRUE) + ref_serie = c(ref_serie, data$getCenteredSerie( pred$getIndexInData(index)+1 )) + centered_series = rbind( + data$getCenteredSeries( pred$getParams(index)$indices ), + data$getCenteredSeries( pred$getParams(index)$indices+1 ) ) + yrange = range( ref_serie, quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) ) par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2) for (i in nn:1) { - plot(centered_series[,sorted_distances$ix[i]], ylim=yrange, type="l", col=colors[i], - xlab=ifelse(i==nn,"Temps (en heures)",""), ylab=ifelse(i==nn,"PM10 centré","")) + plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i], + xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré","")) par(new=TRUE) } # Also plot ref curve, in red @@ -195,7 +189,10 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE) abline(v=24, lty=2, col=colors()[56], lwd=1) } - list("index"=index,"neighb_indices"=fdays[sorted_distances$ix[1:nn]],"colors"=colors) + list( + "index"=pred$getIndexInData(index), + "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]], + "colors"=colors) } #' Functional boxplot on filaments diff --git a/pkg/inst/extdata/meteo_extra_noNAs.csv b/pkg/inst/extdata/meteo_extra_noNAs.csv index 5d8dd99..872b72a 100644 --- a/pkg/inst/extdata/meteo_extra_noNAs.csv +++ b/pkg/inst/extdata/meteo_extra_noNAs.csv @@ -1,2792 +1,2792 @@ -Date,PAmoy,VVmoy,Tmoy,GTmax,Season,Week,Pollution,PAmoy_prev,VVmoy_prev,Tmoy_prev,GTmax_prev,Season_prev,Week_prev,Pollution_prev -12/10/2008,1017.5,2,1.4,0.4,0,1,1,1016.825,3.32375,1.4625,2.15,0,1,1 -12/11/2008,1013.5,2,0.3,2.5,0,1,1,1013.5875,2.07625,-0.6425,3.68,0,1,1 -12/12/2008,1016.8,4,-0.9,-0.5,0,1,0,1017.8,3.84625,-1.925,0.95,0,1,0 -12/13/2008,996.4,8,1.5,-1.1,0,0,0,997.5225,6.8925,-0.35125,0.24,0,0,0 -12/14/2008,998.3,2,3.1,0,0,0,0,998.8425,3.38875,1.63625,1.81,0,0,0 -12/15/2008,1014.5,4,2.3,2.9,0,1,1,1014.2375,4.3025,0.135,2.09,0,1,1 -12/16/2008,1016.5,1,1.3,-1.2,0,1,1,1016.2375,2.31625,-0.4325,-0.7,0,1,1 -12/17/2008,1016.5,1,3.6,1.4,0,1,2,1015.6375,3.355,2.05625,1.88,0,1,2 -12/18/2008,1022.3,3,5.8,3,0,1,1,1022.3375,3.85125,4.71125,2.73,0,1,1 -12/19/2008,1027.3,4,7.1,-0.1,0,1,1,1027.45,4.73375,6.2425,0.75,0,1,1 -12/20/2008,1030,5,9.9,-0.699999999999999,0,0,0,1030.3625,6.46875,8.12375,0.66,0,0,0 -12/21/2008,1035.8,4,9,-1.2,0,0,0,1035.4,4.66375,8.095,-0.59,0,0,0 -12/22/2008,1036.5,2,8.5,-1.2,0,1,0,1036.375,2.7825,7.59625,-0.57,0,1,0 -12/23/2008,1036.5,2,5.7,3.3,0,1,1,1035.9375,2.50125,6.41625,1.21,0,1,1 -12/24/2008,1034.5,2,4.4,2.9,0,1,1,1033.75,2.81375,3.81625,3.04,0,1,1 -12/25/2008,1031.6,5,4.7,-0.8,0,1,0,1031.175,5.83875,3.10875,-0.1,0,1,0 -12/26/2008,1032,7,0.7,-1.2,0,1,0,1032.3375,7.79625,-0.7325,-0.15,0,1,0 -12/27/2008,1030.9,6,-1.3,-0.2,0,0,0,1031.0375,6.445,-1.975,0.38,0,0,0 -12/28/2008,1026.9,5,-3.3,-0.8,0,0,1,1026.6,5.6925,-3.40875,0.18,0,0,1 -12/29/2008,1027.8,3,-3.3,0.5,0,1,1,1028.175,3.38,-3.5775,1.49,0,1,1 -12/30/2008,1028.8,3,-0.9,5,0,1,2,1028.325,3.25875,-1.93,3.15,0,1,2 -12/31/2008,1027.4,0,0.2,0.4,0,1,2,1027.3125,1.625,-1.96875,3.59,0,1,2 -1/1/2009,1029.2,3,-1.5,1.1,0,1,2,1028.925,2.45375,-2.71875,2.74,0,1,2 -1/2/2009,1028.1,4,-1.2,0.1,0,1,1,1028.0875,4.28375,-2.12875,-0.88,0,1,1 -1/3/2009,1027.8,4,-2.3,-0.9,0,0,1,1027.475,4.2625,-3.55875,0.6,0,0,1 -1/4/2009,1024.4,0,-3,5.2,0,0,2,1023.2375,1.87,-4.3975,5.3,0,0,2 -1/5/2009,1018.8,2,-0.7,0.4,0,1,1,1018.6,4.57625,-1.1425,0.06,0,1,1 -1/6/2009,1024.3,4,-6,-0.3,0,1,1,1023.75,5.26625,-6.47,-0.45,0,1,1 -1/7/2009,1025.8,2,-3.4,6.3,0,1,2,1024.9625,3.27625,-3.69125,3.85,0,1,2 -1/8/2009,1028.4,3,-1.6,1.7,0,1,1,1028.025,4.5075,-2.5425,0.39,0,1,1 -1/9/2009,1029,2,-6.8,3.8,0,1,2,1028.675,1.83375,-6.7825,1.73,0,1,2 -1/10/2009,1029.2,2,-6.1,7.7,0,0,2,1029.075,1.1925,-6.2975,4.76,0,0,2 -1/11/2009,1029.7,3,-1.6,14.6,0,0,-1,1030.4625,3.24875,-3.0475,9.18,0,0,-1 -1/12/2009,1020.3,6,3.4,0.9,0,1,-1,1021.7,7.0125,2.09875,1.8,0,1,-1 -1/13/2009,1014,4,4.9,-1,0,1,0,1015.0375,4.85625,3.4175,-0.1,0,1,0 -1/14/2009,1016.8,2,2.2,1.3,0,1,0,1017.4,2.85625,2.39,0.35,0,1,0 -1/15/2009,1017,5,2.2,-0.6,0,1,0,1018.15,4.68625,0.41125,-0.79,0,1,0 -1/16/2009,1018.3,5,3.2,0.8,0,1,1,1019.4375,4.36,2.3,1.52,0,1,1 -1/17/2009,1014.2,6,7.2,0.199999999999999,0,0,0,1016.3125,7.28875,5.2475,0.04,0,0,0 -1/18/2009,1002.5,6,5.9,-0.5,0,0,0,1002.79125,8.93125,5.7475,0.08,0,0,0 -1/19/2009,989.4,6,5.6,-0.700000000000001,0,1,0,989.78875,9.48,6.4725,0.19,0,1,0 -1/20/2009,996.1,4,2,0.1,0,1,0,996.62625,3.9375,1.6975,1.07,0,1,0 -1/21/2009,1006,4,0.8,3.1,0,1,0,1006.85,3.7975,2.3,0.56,0,1,0 -1/22/2009,992.8,7,6,-0.9,0,1,0,992.68,9.24625,4.895,-0.23,0,1,0 -1/23/2009,980.4,5,6.5,-0.399999999999999,0,1,0,981.36,10.14375,6.59,-0.03,0,1,0 -1/24/2009,991.9,0,2.7,2.2,0,0,1,991.1425,3.49125,1.76,2.03,0,0,1 -1/25/2009,992.2,7,3.9,1,0,0,0,992.88625,7.07875,3.025,0.97,0,0,0 -1/26/2009,1001.7,2,3,1.8,0,1,0,1001.82,2.115,4.08625,-0.28,0,1,0 -1/27/2009,1015.2,1,2.1,-1.1,0,1,1,1015.275,1.92875,2.69875,-0.01,0,1,1 -1/28/2009,1016.7,3,1.7,-1.5,0,1,0,1017.7875,2.44375,1.60375,-0.93,0,1,0 -1/29/2009,1018.6,4,-0.9,0.7,0,1,2,1018.475,3.58125,-1.28625,1.12,0,1,2 -1/30/2009,1016.3,6,0.7,2,0,1,1,1017.025,5.68625,-0.145,1.08,0,1,1 -1/31/2009,1010.7,8,0.6,-1.1,0,0,1,1011.5,6.64125,-0.34125,0.27,0,0,1 -2/1/2009,1005.4,8,-1.4,-1,0,0,0,1004.925,6.36625,-2.47,0.26,0,0,0 -2/2/2009,994.8,2,-1.7,-1.5,0,1,1,994.39375,4.18125,-1.295,0.56,0,1,1 -2/3/2009,1000.7,3,-3.6,2.7,0,1,1,1001.18625,2.925,-3.04875,-1.14,0,1,1 -2/4/2009,995.3,5,-2.3,5.1,0,1,2,995.505,3.63625,-1.2,2.46,0,1,2 -2/5/2009,990.4,4,5,1.2,0,1,0,991.12625,5.2675,3.885,1.72,0,1,0 -2/6/2009,988.8,4,5.5,1.9,0,1,0,988.7575,3.5425,4.07875,3.59,0,1,0 -2/7/2009,995.9,4,1.5,0.5,0,0,0,994.86375,4.14625,1.28375,2,0,0,0 -2/8/2009,1005.7,3,2.2,1.4,0,0,0,1006.25,4.0025,1.1825,1.03,0,0,0 -2/9/2009,997.6,6,5.8,1.3,0,1,0,998.39625,8.3325,5.08,0.44,0,1,0 -2/10/2009,1001.6,7,4.1,-1.1,0,1,0,1001.08,9.4175,3.63875,-0.31,0,1,0 -2/11/2009,1018.7,3,3.2,1.5,0,1,1,1017.7875,3.28,2.1525,3.79,0,1,1 -2/12/2009,1024.9,3,1.7,0,0,1,0,1024.2375,4.69875,1.645,0.11,0,1,0 -2/13/2009,1022.1,2,1.6,1.8,0,1,1,1021.325,4.28375,1.33625,1.07,0,1,1 -2/14/2009,1028.9,2,1.1,1.2,0,0,1,1028.7625,2.605,0.0725,0.51,0,0,1 -2/15/2009,1030.4,2,1.1,2.8,0,0,1,1029.9,2.165,-0.07875,2.83,0,0,1 -2/16/2009,1026.7,3,5.8,-0.6,0,1,1,1026.175,3.895,4.475,0.55,0,1,1 -2/17/2009,1025.7,5,7.2,-0.7,0,1,1,1024.35,5.34,6.50125,-0.19,0,1,1 -2/18/2009,1026.2,3,5.6,-0.699999999999999,0,1,1,1025.45,2.97,4.8675,-0.72,0,1,1 -2/19/2009,1028.8,1,3.9,1.2,0,1,2,1028.725,1.265,2.7,1.33,0,1,2 -2/20/2009,1032.3,1,6.1,-0.2,0,1,2,1032.0875,2.2575,4.4775,0.11,0,1,2 -2/21/2009,1033.2,3,5.7,0.3,0,0,2,1033.175,2.31625,4.5,1.81,0,0,2 -2/22/2009,1028.4,4,6.9,-1.4,0,0,0,1027.725,4.77375,6.21625,-0.5,0,0,0 -2/23/2009,1025,3,6.2,-0.199999999999999,0,1,0,1024.0875,3.675,5.74375,0.27,0,1,0 -2/24/2009,1027.7,2,5.7,-0.699999999999999,0,1,0,1027.3,2.53125,5.0425,-0.04,0,1,0 -2/25/2009,1030.5,1,3.9,2.4,0,1,2,1030.2125,1.405,3.675,-0.91,0,1,2 -2/26/2009,1026.5,3,5.5,0.9,0,1,1,1025.875,3.54125,4.67,4.56,0,1,1 -2/27/2009,1022.9,3,6.2,0.6,0,1,1,1022.4625,3.11,6.8325,-0.59,0,1,1 -2/28/2009,1014.7,3,6.5,5.3,0,0,1,1014.3,2.1325,5.74375,4.29,0,0,1 -3/1/2009,1009.5,1,6.9,4.4,0,0,2,1009.1125,3.3675,6.91375,2.48,0,0,2 -3/2/2009,1018,3,5.4,0.5,0,1,1,1017.95,2.83375,6.18125,0.19,0,1,1 -3/3/2009,1009.5,6,5.4,2.2,0,1,0,1009.94125,5.99375,5.26625,2.88,0,1,0 -3/4/2009,986.4,4,3.4,-1.5,0,1,0,986.68125,5.31875,3.405,-0.59,0,1,0 -3/5/2009,993.1,0,1.6,3.1,0,1,0,993.4225,2.93125,2.05125,1.42,0,1,0 -3/6/2009,1007.8,3,3.6,1.2,0,1,1,1008.15,4.12125,2.6275,1.66,0,1,1 -3/7/2009,1013.8,3,6.5,2.2,0,0,1,1014.1875,3.7975,5.33375,2.01,0,0,1 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+7/27/2016,1019.3,2.6,16.9,1.7,1018.95,3.48125,17.32125,1.08 +7/28/2016,1016.6,3.4,18.7,-0.5,1016.075,4.375,18.83125,-0.76 +7/29/2016,1014.4,3.3,18.2,-0.6,1013.9375,3.94375,17.8775,-0.55 +7/30/2016,1014.9,2.2,17.1,-0.600000000000001,1014.475,3.38375,17.58375,0.07 +7/31/2016,1017.7,2.2,16.9,-0.299999999999999,1017.6375,2.6775,16.7275,-1 diff --git a/pkg/tests/testthat/test.computeFilaments.R b/pkg/tests/testthat/test.computeFilaments.R index 34e07f7..ec39340 100644 --- a/pkg/tests/testthat/test.computeFilaments.R +++ b/pkg/tests/testthat/test.computeFilaments.R @@ -8,15 +8,15 @@ I = function(i) getDataTest = function(n) { data = Data$new() - x = seq(0,10,0.1) - L = length(x) + x = seq(0,9.5,0.1) + L = length(x) #96 1/4h s1 = cos(x) s2 = sin(x) s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] ) - #sum((s1-s2)^2) == 97.59381 - #sum((s1-s3)^2) == 57.03051 - #sum((s2-s3)^2) == 40.5633 - s = list( s1, s2, s3 ) + #sum((s1-s2)^2) == 96 + #sum((s1-s3)^2) == 58 + #sum((s2-s3)^2) == 38 + s = list(s1, s2, s3) series = list() for (i in seq_len(n)) { @@ -26,7 +26,10 @@ getDataTest = function(n) # 10 series with NAs for index 2 if (I(i) == 2 && i >= 60 && i<= 90) serie[sample(seq_len(L),1)] = NA - data$append(c(), serie, level, c(), c()) #no need for more + time = as.POSIXct(i*15*60, origin="2007-01-01", tz="GMT") + exo = runif(4) + exo_hat = runif(4) + data$append(time, serie, level, exo, exo_hat) } data } @@ -36,7 +39,9 @@ test_that("output is as expected on simulated series", data = getDataTest(150) # index 143 : serie type 2 - f = computeFilaments(data, 143, limit=60, plot=FALSE) + pred = computeForecast(data, 143, "Neighbors", "Zero", + horizon=length(data$getSerie(1)), simtype="endo", h_window=1) + f = computeFilaments(data, pred, 1, limit=60, plot=FALSE) # Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next) expect_identical(length(f$neighb_indices), as.integer(60)) @@ -57,7 +62,9 @@ test_that("output is as expected on simulated series", expect_match(f$colors[38], "#E*") # index 142 : serie type 1 - f = computeFilaments(data, 142, limit=50, plot=FALSE) + pred = computeForecast(data, 142, "Neighbors", "Zero", + horizon=length(data$getSerie(1)), simtype="endo", h_window=1) + f = computeFilaments(data, pred, 1, limit=50, plot=FALSE) # Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next) # NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[ diff --git a/pkg/vignettes/talweg.Rmd b/pkg/vignettes/talweg.Rmd index aace6af..b6e94cb 100644 --- a/pkg/vignettes/talweg.Rmd +++ b/pkg/vignettes/talweg.Rmd @@ -1,6 +1,6 @@ --- -title: "Vignette Title" -author: "Vignette Author" +title: "talweg" +author: "Benjamin Auder" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > @@ -9,6 +9,108 @@ vignette: > %\VignetteEncoding{UTF-8} --- + +## Introduction + +J'ai fait quelques essais dans différentes configurations pour la méthode "Neighbors" (la seule dont on a parlé).
Il semble que le mieux soit + + * simtype="exo" ou "mix" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC) + * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons + * mix_strategy="mult" : on multiplie les poids (au lieu d'en éteindre) + +J'ai systématiquement comparé à une approche naïve : 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). + +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. + +

Pollution par chauffage

+ + +loop: indices_ch, ep, np et titres: Pollution par chauffage, Pollution par épandage, Semaine non polluée + + + +p_nn_exo = computeForecast(data, indices_ch, "Neighbors", "Neighbors", simtype="exo", horizon=H) +p_nn_mix = computeForecast(data, indices_ch, "Neighbors", "Neighbors", simtype="mix", horizon=H) +p_az = computeForecast(data, indices_ch, "Average", "Zero", horizon=H) #, memory=183) +p_pz = computeForecast(data, indices_ch, "Persistence", "Zero", horizon=H, same_day=TRUE) + +e_nn_exo = computeError(data, p_nn_exo) +e_nn_mix = computeError(data, p_nn_mix) +e_az = computeError(data, p_az) +e_pz = computeError(data, p_pz) +options(repr.plot.width=9, repr.plot.height=7) +plotError(list(e_nn_mix, e_pz, e_az, e_nn_exo), cols=c(1,2,colors()[258], 4)) + +#Noir: neighbors_mix, bleu: neighbors_exo, vert: moyenne, rouge: persistence + +i_np = which.min(e_nn_exo$abs$indices) +i_p = which.max(e_nn_exo$abs$indices) + +options(repr.plot.width=9, repr.plot.height=4) +par(mfrow=c(1,2)) + +plotPredReal(data, p_nn_exo, i_np); title(paste("PredReal nn exo day",i_np)) +plotPredReal(data, p_nn_exo, i_p); title(paste("PredReal nn exo day",i_p)) + +plotPredReal(data, p_nn_mix, i_np); title(paste("PredReal nn mix day",i_np)) +plotPredReal(data, p_nn_mix, i_p); title(paste("PredReal nn mix day",i_p)) + +plotPredReal(data, p_az, i_np); title(paste("PredReal az day",i_np)) +plotPredReal(data, p_az, i_p); title(paste("PredReal az day",i_p)) + +#Bleu: prévue, noir: réalisée + +par(mfrow=c(1,2)) +f_np_exo = computeFilaments(data, p_nn_exo, i_np, plot=TRUE); title(paste("Filaments nn exo day",i_np)) +f_p_exo = computeFilaments(data, p_nn_exo, i_p, plot=TRUE); title(paste("Filaments nn exo day",i_p)) + +f_np_mix = computeFilaments(data, p_nn_mix, i_np, plot=TRUE); title(paste("Filaments nn mix day",i_np)) +f_p_mix = computeFilaments(data, p_nn_mix, i_p, plot=TRUE); title(paste("Filaments nn mix day",i_p)) + +par(mfrow=c(1,2)) +plotFilamentsBox(data, f_np_exo); title(paste("FilBox nn exo day",i_np)) +plotFilamentsBox(data, f_p_exo); title(paste("FilBox nn exo day",i_p)) + +plotFilamentsBox(data, f_np_mix); title(paste("FilBox nn mix day",i_np)) +plotFilamentsBox(data, f_p_mix); title(paste("FilBox nn mix day",i_p)) + +par(mfrow=c(1,2)) +plotRelVar(data, f_np_exo); title(paste("StdDev nn exo day",i_np)) +plotRelVar(data, f_p_exo); title(paste("StdDev nn exo day",i_p)) + +plotRelVar(data, f_np_mix); title(paste("StdDev nn mix day",i_np)) +plotRelVar(data, f_p_mix); title(paste("StdDev nn mix day",i_p)) + +#Variabilité globale en rouge ; sur les 60 voisins (+ lendemains) en noir + +par(mfrow=c(1,2)) +plotSimils(p_nn_exo, i_np); title(paste("Weights nn exo day",i_np)) +plotSimils(p_nn_exo, i_p); title(paste("Weights nn exo day",i_p)) + +plotSimils(p_nn_mix, i_np); title(paste("Weights nn mix day",i_np)) +plotSimils(p_nn_mix, i_p); title(paste("Weights nn mix day",i_p) + +#- pollué à gauche, + pollué à droite + +#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite +p_ch_nn_exo$getParams(i_np)$window +p_ch_nn_exo$getParams(i_p)$window + +p_ch_nn_mix$getParams(i_np)$window +p_ch_nn_mix$getParams(i_p)$window + + + + +## Bilan + +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 same_day). + +Comment améliorer la méthode ? + + + + Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The `html_vignette` output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The `html_vignette` format: - Never uses retina figures diff --git a/reports/report_2017-03-01.13h_average.ipynb b/reports/report_2017-03-01.13h.ipynb similarity index 100% rename from reports/report_2017-03-01.13h_average.ipynb rename to reports/report_2017-03-01.13h.ipynb diff --git a/reports/report_2017-03-01.13h_zero.ipynb b/reports/report_2017-03-01.13h_zero.ipynb deleted file mode 100644 index cf51e90..0000000 --- a/reports/report_2017-03-01.13h_zero.ipynb +++ /dev/null @@ -1,458 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "library(talweg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "data = getData(ts_data=\"../data/pm10_mesures_H_loc.csv\", exo_data=\"../data/meteo_extra_noNAs.csv\",\n", - " input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).
Il semble que le mieux soit\n", - "\n", - " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n", - " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", - " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", - "\n", - "J'ai systématiquement comparé à deux autres approches : la persistence et la répétition de la dernière valeur observée (sur tout l'horizon, donc \"zero\") ; à chaque fois sans prédiction du saut (sauf pour Neighbors : prédiction basée sur les poids calculés).\n", - "\n", - "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", - "\n", - "

Pollution par chauffage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", - "p_ch_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_ch_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "#p_ch_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_ch_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_ch_l = getForecast(data, indices, \"Level\", same_day=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ch_nn = getError(data, p_ch_nn)\n", - "e_ch_pz = getError(data, p_ch_pz)\n", - "#e_ch_az = getError(data, p_ch_az)\n", - "e_ch_zz = getError(data, p_ch_zz)\n", - "#e_ch_l = getError(data, p_ch_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_ch_nn, e_ch_pz, e_ch_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La méthode Neighbors fait assez nettement mieux que les autres dans ce cas." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ch_nn, 3)\n", - "plotPredReal(data, p_ch_nn, 4)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prédictions d'autant plus lisses que le jour à prévoir est atypique (pollué)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ch_zz, 3)\n", - "plotPredReal(data, p_ch_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_ch_nn$getIndexInData(3))\n", - "plotFilaments(data, p_ch_nn$getIndexInData(4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Beaucoup de courbes similaires dans le cas peu pollué, très peu pour un jour pollué." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,3))\n", - "plotSimils(p_ch_nn, 3)\n", - "plotSimils(p_ch_nn, 4)\n", - "plotSimils(p_ch_nn, 5)\n", - "\n", - "#Non pollué à gauche, pollué au milieu, autre pollué à droite" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La plupart des poids très proches de zéro ; pas pour le jour 5 : autre type de jour, cf. ci-dessous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ch_nn, 5)\n", - "plotFilaments(data, p_ch_nn$getIndexInData(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Pollution par épandage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", - "p_ep_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_ep_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "#p_ep_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_ep_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_ep_l = getForecast(data, indices, \"Level\", same_day=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ep_nn = getError(data, p_ep_nn)\n", - "e_ep_pz = getError(data, p_ep_pz)\n", - "#e_ep_az = getError(data, p_ep_az)\n", - "e_ep_zz = getError(data, p_ep_zz)\n", - "#e_ep_l = getError(data, p_ep_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_ep_nn, e_ep_pz, e_ep_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cette fois les deux méthodes naïves font en moyenne moins d'erreurs que Neighbors. Prédiction trop difficile ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ep_nn, 4)\n", - "plotPredReal(data, p_ep_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "À gauche un jour \"bien\" prévu, à droite le pic d'erreur (jour 6)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ep_zz, 4)\n", - "plotPredReal(data, p_ep_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_ep_nn$getIndexInData(4))\n", - "plotFilaments(data, p_ep_nn$getIndexInData(6))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotSimils(p_ep_nn, 4)\n", - "plotSimils(p_ep_nn, 6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Même observation concernant les poids : concentrés près de zéro pour les prédictions avec peu de voisins." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Semaine non polluée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", - "p_np_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_np_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=FALSE)\n", - "#p_np_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_np_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_np_l = getForecast(data, indices, \"Level\", same_day=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_np_nn = getError(data, p_np_nn)\n", - "e_np_pz = getError(data, p_np_pz)\n", - "#e_np_az = getError(data, p_np_az)\n", - "e_np_zz = getError(data, p_np_zz)\n", - "#e_np_l = getError(data, p_np_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_np_nn, e_np_pz, e_np_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Performances des méthodes \"Zero\" et \"Neighbors\" comparables ; mauvais résultats pour la persistence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_np_nn, 3)\n", - "plotPredReal(data, p_np_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les \"bonnes\" prédictions (à gauche) sont tout de même trop lissées." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_np_zz, 4)\n", - "plotPredReal(data, p_np_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_np_nn$getIndexInData(3))\n", - "plotFilaments(data, p_np_nn$getIndexInData(6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Jours \"typiques\", donc beaucoup de voisins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,3))\n", - "plotSimils(p_np_nn, 3)\n", - "plotSimils(p_np_nn, 4)\n", - "plotSimils(p_np_nn, 6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Répartition idéale des poids : quelques uns au-delà de 0.3-0.4, le reste très proche de zéro." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bilan\n", - "\n", - "Problème difficile : en terme d'erreur moyenne, on ne fait guère mieux que 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 same_day).\n", - "\n", - "Comment améliorer la méthode ?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "3.3.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/reports/report_2017-03-01.7h.ipynb b/reports/report_2017-03-01.7h.ipynb new file mode 100644 index 0000000..f71de9b --- /dev/null +++ b/reports/report_2017-03-01.7h.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "library(talweg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n", + "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n", + "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", + "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", + "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", + "H = 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for (i in 1:3) {" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#blabla\n", + "plot(function(x) cos(x+i))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).
Il semble que le mieux soit\n", + "\n", + " * simtype=\"exo\" ou \"mix\" : similarités exogènes avec/sans endogènes (fenêtre optimisée par VC)\n", + " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", + " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", + "\n", + "J'ai systématiquement comparé à une approche naïve : 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", + "\n", + "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", + "\n", + "

Pollution par chauffage

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bilan\n", + "\n", + "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 same_day).\n", + "\n", + "Comment améliorer la méthode ?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "3.3.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/reports/report_2017-03-01.7h_average.ipynb b/reports/report_2017-03-01.7h_average.ipynb deleted file mode 100644 index 5789d7d..0000000 --- a/reports/report_2017-03-01.7h_average.ipynb +++ /dev/null @@ -1,486 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "library(talweg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ts_data = read.csv(system.file(\"extdata\",\"pm10_mesures_H_loc_report.csv\",package=\"talweg\"))\n", - "exo_data = read.csv(system.file(\"extdata\",\"meteo_extra_noNAs.csv\",package=\"talweg\"))\n", - "data = getData(ts_data, exo_data, input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).
Il semble que le mieux soit\n", - "\n", - " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n", - " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", - " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", - "\n", - "(valeurs par défaut).\n", - "\n", - "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", - "\n", - "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", - "\n", - "

Pollution par chauffage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices_ch = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", - "p_ch_nn = computeForecast(data, indices_ch, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", - "p_ch_pz = computeForecast(data, indices_ch, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "p_ch_az = computeForecast(data, indices_ch, \"Average\", \"Zero\") #, memory=183)\n", - "#p_ch_zz = computeForecast(data, indices_ch, \"Zero\", \"Zero\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ch_nn = computeError(data, p_ch_nn)\n", - "e_ch_pz = computeError(data, p_ch_pz)\n", - "e_ch_az = computeError(data, p_ch_az)\n", - "#e_ch_zz = computeError(data, p_ch_zz)\n", - "options(repr.plot.width=9, repr.plot.height=7)\n", - "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: moyenne" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ch_nn, 3)\n", - "plotPredReal(data, p_ch_nn, 4)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ch_az, 3)\n", - "plotPredReal(data, p_ch_az, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "f3_ch = computeFilaments(data, p_ch_nn$getIndexInData(3), plot=TRUE)\n", - "f4_ch = computeFilaments(data, p_ch_nn$getIndexInData(4), plot=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilamentsBox(data, f3_ch)\n", - "plotFilamentsBox(data, f4_ch)\n", - "\n", - "#À gauche : jour 3 + lendemain (4) ; à droite : jour 4 + lendemain (5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotRelVar(data, f3_ch)\n", - "plotRelVar(data, f4_ch)\n", - "\n", - "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotSimils(p_ch_nn, 3)\n", - "plotSimils(p_ch_nn, 4)\n", - "\n", - "#Non pollué à gauche, pollué à droite" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", - "p_ch_nn$getParams(3)$window\n", - "p_ch_nn$getParams(4)$window" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Pollution par épandage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices_ep = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", - "p_ep_nn = computeForecast(data,indices_ep, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", - "p_ep_pz = computeForecast(data, indices_ep, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "p_ep_az = computeForecast(data, indices_ep, \"Average\", \"Zero\") #, memory=183)\n", - "#p_ep_zz = computeForecast(data, indices_ep, \"Zero\", \"Zero\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ep_nn = computeError(data, p_ep_nn)\n", - "e_ep_pz = computeError(data, p_ep_pz)\n", - "e_ep_az = computeError(data, p_ep_az)\n", - "#e_ep_zz = computeError(data, p_ep_zz)\n", - "options(repr.plot.width=9, repr.plot.height=7)\n", - "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: moyenne" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ep_nn, 4)\n", - "plotPredReal(data, p_ep_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ep_az, 4)\n", - "plotPredReal(data, p_ep_az, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "f4_ep = computeFilaments(data, p_ep_nn$getIndexInData(4), plot=TRUE)\n", - "f6_ep = computeFilaments(data, p_ep_nn$getIndexInData(6), plot=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilamentsBox(data, f4_ep)\n", - "plotFilamentsBox(data, f6_ep)\n", - "\n", - "#À gauche : jour 4 + lendemain (5) ; à droite : jour 6 + lendemain (7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotRelVar(data, f4_ep)\n", - "plotRelVar(data, f6_ep)\n", - "\n", - "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotSimils(p_ep_nn, 4)\n", - "plotSimils(p_ep_nn, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", - "p_ep_nn$getParams(4)$window\n", - "p_ep_nn$getParams(6)$window" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Semaine non polluée

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices_np = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", - "p_np_nn = computeForecast(data,indices_np, \"Neighbors\", \"Neighbors\", simtype=\"mix\")\n", - "p_np_pz = computeForecast(data, indices_np, \"Persistence\", \"Zero\", same_day=FALSE)\n", - "p_np_az = computeForecast(data, indices_np, \"Average\", \"Zero\") #, memory=183)\n", - "#p_np_zz = computeForecast(data, indices_np, \"Zero\", \"Zero\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_np_nn = computeError(data, p_np_nn)\n", - "e_np_pz = computeError(data, p_np_pz)\n", - "e_np_az = computeError(data, p_np_az)\n", - "#e_np_zz = computeError(data, p_np_zz)\n", - "options(repr.plot.width=9, repr.plot.height=7)\n", - "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: moyenne" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_np_nn, 5)\n", - "plotPredReal(data, p_np_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_np_az, 5)\n", - "plotPredReal(data, p_np_az, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "f5_np = computeFilaments(data, p_np_nn$getIndexInData(5), plot=TRUE)\n", - "f6_np = computeFilaments(data, p_np_nn$getIndexInData(6), plot=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilamentsBox(data, f5_np)\n", - "plotFilamentsBox(data, f6_np)\n", - "\n", - "#À gauche : jour 5 + lendemain (6) ; à droite : jour 6 + lendemain (7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotRelVar(data, f5_np)\n", - "plotRelVar(data, f6_np)\n", - "\n", - "#Variabilité globale en rouge ; sur nos 60 voisins (+ lendemains) en noir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotSimils(p_np_nn, 5)\n", - "plotSimils(p_np_nn, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#Fenêtres sélectionnées dans ]0,10] / endo à gauche, exo à droite\n", - "p_np_nn$getParams(5)$window\n", - "p_np_nn$getParams(6)$window" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bilan\n", - "\n", - "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 same_day).\n", - "\n", - "Comment améliorer la méthode ?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "3.3.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/reports/report_2017-03-01.7h_zero.ipynb b/reports/report_2017-03-01.7h_zero.ipynb deleted file mode 100644 index cf51e90..0000000 --- a/reports/report_2017-03-01.7h_zero.ipynb +++ /dev/null @@ -1,458 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "library(talweg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "data = getData(ts_data=\"../data/pm10_mesures_H_loc.csv\", exo_data=\"../data/meteo_extra_noNAs.csv\",\n", - " input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "J'ai fait quelques essais dans différentes configurations pour la méthode \"Neighbors\" (la seule dont on a parlé).
Il semble que le mieux soit\n", - "\n", - " * simtype=\"mix\" : on utilise les similarités endogènes et exogènes (fenêtre optimisée par VC)\n", - " * same_season=FALSE : les indices pour la validation croisée ne tiennent pas compte des saisons\n", - " * mix_strategy=\"mult\" : on multiplie les poids (au lieu d'en éteindre)\n", - "\n", - "J'ai systématiquement comparé à deux autres approches : la persistence et la répétition de la dernière valeur observée (sur tout l'horizon, donc \"zero\") ; à chaque fois sans prédiction du saut (sauf pour Neighbors : prédiction basée sur les poids calculés).\n", - "\n", - "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", - "\n", - "

Pollution par chauffage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\")\n", - "p_ch_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_ch_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "#p_ch_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_ch_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_ch_l = getForecast(data, indices, \"Level\", same_day=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ch_nn = getError(data, p_ch_nn)\n", - "e_ch_pz = getError(data, p_ch_pz)\n", - "#e_ch_az = getError(data, p_ch_az)\n", - "e_ch_zz = getError(data, p_ch_zz)\n", - "#e_ch_l = getError(data, p_ch_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_ch_nn, e_ch_pz, e_ch_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La méthode Neighbors fait assez nettement mieux que les autres dans ce cas." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ch_nn, 3)\n", - "plotPredReal(data, p_ch_nn, 4)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Prédictions d'autant plus lisses que le jour à prévoir est atypique (pollué)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ch_zz, 3)\n", - "plotPredReal(data, p_ch_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_ch_nn$getIndexInData(3))\n", - "plotFilaments(data, p_ch_nn$getIndexInData(4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Beaucoup de courbes similaires dans le cas peu pollué, très peu pour un jour pollué." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,3))\n", - "plotSimils(p_ch_nn, 3)\n", - "plotSimils(p_ch_nn, 4)\n", - "plotSimils(p_ch_nn, 5)\n", - "\n", - "#Non pollué à gauche, pollué au milieu, autre pollué à droite" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La plupart des poids très proches de zéro ; pas pour le jour 5 : autre type de jour, cf. ci-dessous." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ch_nn, 5)\n", - "plotFilaments(data, p_ch_nn$getIndexInData(5))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Pollution par épandage

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\")\n", - "p_ep_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_ep_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=TRUE)\n", - "#p_ep_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_ep_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_ep_l = getForecast(data, indices, \"Level\", same_day=TRUE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_ep_nn = getError(data, p_ep_nn)\n", - "e_ep_pz = getError(data, p_ep_pz)\n", - "#e_ep_az = getError(data, p_ep_az)\n", - "e_ep_zz = getError(data, p_ep_zz)\n", - "#e_ep_l = getError(data, p_ep_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_ep_nn, e_ep_pz, e_ep_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cette fois les deux méthodes naïves font en moyenne moins d'erreurs que Neighbors. Prédiction trop difficile ?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_ep_nn, 4)\n", - "plotPredReal(data, p_ep_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "À gauche un jour \"bien\" prévu, à droite le pic d'erreur (jour 6)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_ep_zz, 4)\n", - "plotPredReal(data, p_ep_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_ep_nn$getIndexInData(4))\n", - "plotFilaments(data, p_ep_nn$getIndexInData(6))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotSimils(p_ep_nn, 4)\n", - "plotSimils(p_ep_nn, 6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Même observation concernant les poids : concentrés près de zéro pour les prédictions avec peu de voisins." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Semaine non polluée" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "indices = seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\")\n", - "p_np_nn = getForecast(data,indices,\"Neighbors\",\"Neighbors\",simtype=\"mix\",same_season=FALSE,mix_strategy=\"mult\")\n", - "p_np_pz = getForecast(data, indices, \"Persistence\", \"Zero\", same_day=FALSE)\n", - "#p_np_az = getForecast(data, indices, \"Average\", \"Zero\")\n", - "p_np_zz = getForecast(data, indices, \"Zero\", \"Zero\")\n", - "#p_np_l = getForecast(data, indices, \"Level\", same_day=FALSE)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_np_nn = getError(data, p_np_nn)\n", - "e_np_pz = getError(data, p_np_pz)\n", - "#e_np_az = getError(data, p_np_az)\n", - "e_np_zz = getError(data, p_np_zz)\n", - "#e_np_l = getError(data, p_np_l)\n", - "options(repr.plot.width=9, repr.plot.height=6)\n", - "plotError(list(e_np_nn, e_np_pz, e_np_zz), cols=c(1,2,colors()[258]))\n", - "\n", - "#Noir: neighbors, rouge: persistence, vert: zero" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Performances des méthodes \"Zero\" et \"Neighbors\" comparables ; mauvais résultats pour la persistence." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "options(repr.plot.width=9, repr.plot.height=4)\n", - "plotPredReal(data, p_np_nn, 3)\n", - "plotPredReal(data, p_np_nn, 6)\n", - "\n", - "#Bleu: prévue, noir: réalisée" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Les \"bonnes\" prédictions (à gauche) sont tout de même trop lissées." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotPredReal(data, p_np_zz, 4)\n", - "plotPredReal(data, p_np_zz, 6)\n", - "\n", - "#Méthode \"zero\" :" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,2))\n", - "plotFilaments(data, p_np_nn$getIndexInData(3))\n", - "plotFilaments(data, p_np_nn$getIndexInData(6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Jours \"typiques\", donc beaucoup de voisins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "par(mfrow=c(1,3))\n", - "plotSimils(p_np_nn, 3)\n", - "plotSimils(p_np_nn, 4)\n", - "plotSimils(p_np_nn, 6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Répartition idéale des poids : quelques uns au-delà de 0.3-0.4, le reste très proche de zéro." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bilan\n", - "\n", - "Problème difficile : en terme d'erreur moyenne, on ne fait guère mieux que 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 same_day).\n", - "\n", - "Comment améliorer la méthode ?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "R", - "language": "R", - "name": "ir" - }, - "language_info": { - "codemirror_mode": "r", - "file_extension": ".r", - "mimetype": "text/x-r-source", - "name": "R", - "pygments_lexer": "r", - "version": "3.3.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}