X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=reports%2FExperiments.gj;fp=reports%2FExperiments.gj;h=d7ade4006c12b9b31f87548263911468a16ce920;hp=0f102ad7afbbc87872e14718d51d95200b576b55;hb=1e8327df4e8abce5c23808be4f98037635bb2714;hpb=d2ab47a744d8fb29c03a76a7ca2368dae53f9a57
diff --git a/reports/Experiments.gj b/reports/Experiments.gj
index 0f102ad..d7ade40 100644
--- a/reports/Experiments.gj
+++ b/reports/Experiments.gj
@@ -2,8 +2,8 @@
# Résultats numériques
Cette partie montre les résultats obtenus avec des variantes de l'algorithme décrit au
-chapitre , en utilisant le package présenté à la section 3. Cet algorithme est
-systématiquement comparé à deux approches naïves :
+à la section 4, en utilisant le package présenté au chapitre précédent. Cet
+algorithme est systématiquement comparé à deux approches naïves :
* la moyenne des lendemains des jours "similaires" dans tout le passé, c'est-à -dire
prédiction = moyenne de tous les mardis passés si le jour courant est un lundi.
@@ -35,8 +35,8 @@ list_indices = ['indices_ch', 'indices_ep', 'indices_np']
-----r
library(talweg)
-P = ${P} #instant de prévision
-H = ${H} #horizon (en heures)
+P = ${P} #première heure de prévision
+H = ${H} #dernière heure de prévision
ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc_report.csv",
package="talweg"))
@@ -44,8 +44,7 @@ exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",
package="talweg"))
# NOTE: 'GMT' because DST gaps are filled and multiple values merged in
# above dataset. Prediction from P+1 to P+H included.
-data = getData(ts_data, exo_data, input_tz = "GMT", working_tz="GMT",
- predict_at=P)
+data = getData(ts_data, exo_data)
indices_ch = seq(as.Date("2015-01-18"),as.Date("2015-01-24"),"days")
indices_ep = seq(as.Date("2015-03-15"),as.Date("2015-03-21"),"days")
@@ -55,21 +54,22 @@ indices_np = seq(as.Date("2015-04-26"),as.Date("2015-05-02"),"days")
##
${list_titles[i]}
${"##"} ${list_titles[i]}
-----r
-p1 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
- simtype="mix", local=FALSE)
-p2 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", horizon=H,
- simtype="endo", local=TRUE)
-p3 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Zero", horizon=H,
- simtype="none", local=TRUE)
-p4 = computeForecast(data, ${list_indices[i]}, "Average", "Zero", horizon=H)
-p5 = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", horizon=H,
- same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
+p1 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", predict_from=P,
+ horizon=H, simtype="mix", local=FALSE)
+p2 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Neighbors", predict_from=P,
+ horizon=H, simtype="endo", local=TRUE)
+p3 = computeForecast(data, ${list_indices[i]}, "Neighbors", "Zero", predict_from=P,
+ horizon=H, simtype="none", local=TRUE)
+p4 = computeForecast(data, ${list_indices[i]}, "Average", "Zero", predict_from=P,
+ horizon=H)
+p5 = computeForecast(data, ${list_indices[i]}, "Persistence", "Zero", predict_from=P,
+ horizon=H, same_day=${'TRUE' if loop.index < 2 else 'FALSE'})
-----r
-e1 = computeError(data, p1, H)
-e2 = computeError(data, p2, H)
-e3 = computeError(data, p3, H)
-e4 = computeError(data, p4, H)
-e5 = computeError(data, p5, H)
+e1 = computeError(data, p1, P, H)
+e2 = computeError(data, p2, P, H)
+e3 = computeError(data, p3, P, H)
+e4 = computeError(data, p4, P, H)
+e5 = computeError(data, p5, P, H)
options(repr.plot.width=9, repr.plot.height=7)
plotError(list(e1, e5, e4, e2, e3), cols=c(1,2,colors()[258],4,6))
@@ -134,14 +134,14 @@ journeÌe sur la courbes "difficile à prévoir".
% endif
-----r
par(mfrow=c(1,2))
-f_np1 = computeFilaments(data, p1, i_np, plot=TRUE)
+f_np1 = computeFilaments(data, p1, i_np, predict_from=P, plot=TRUE)
title(paste("Filaments p1 day",i_np))
-f_p1 = computeFilaments(data, p1, i_p, plot=TRUE)
+f_p1 = computeFilaments(data, p1, i_p, predict_from=P, plot=TRUE)
title(paste("Filaments p1 day",i_p))
-f_np2 = computeFilaments(data, p2, i_np, plot=TRUE)
+f_np2 = computeFilaments(data, p2, i_np, predict_from=P, plot=TRUE)
title(paste("Filaments p2 day",i_np))
-f_p2 = computeFilaments(data, p2, i_p, plot=TRUE)
+f_p2 = computeFilaments(data, p2, i_p, predict_from=P, plot=TRUE)
title(paste("Filaments p2 day",i_p))
-----
% if i == 0:
@@ -161,8 +161,8 @@ de variabiliteÌ relative.
% endif
-----r
par(mfrow=c(1,2))
-plotFilamentsBox(data, f_np1); title(paste("FilBox p1 day",i_np))
-plotFilamentsBox(data, f_p1); title(paste("FilBox p1 day",i_p))
+plotFilamentsBox(data, f_np1, predict_from=P); title(paste("FilBox p1 day",i_np))
+plotFilamentsBox(data, f_p1, predict_from=P); title(paste("FilBox p1 day",i_p))
# En pointilleÌs la courbe du jour courant + lendemain (aÌ preÌdire)
-----
@@ -185,11 +185,11 @@ lendemains de voisins atypiques, courbe aÌ preÌvoir elle-meÌme leÌgeÌrement
% endif
-----r
par(mfrow=c(1,2))
-plotRelVar(data, f_np1); title(paste("StdDev p1 day",i_np))
-plotRelVar(data, f_p1); title(paste("StdDev p1 day",i_p))
+plotRelVar(data, f_np1, predict_from=P); title(paste("StdDev p1 day",i_np))
+plotRelVar(data, f_p1, predict_from=P); title(paste("StdDev p1 day",i_p))
-plotRelVar(data, f_np2); title(paste("StdDev p2 day",i_np))
-plotRelVar(data, f_p2); title(paste("StdDev p2 day",i_p))
+plotRelVar(data, f_np2, predict_from=P); title(paste("StdDev p2 day",i_np))
+plotRelVar(data, f_p2, preidct_from=P); title(paste("StdDev p2 day",i_p))
# Variabilité globale en rouge ; sur les voisins (+ lendemains) en noir
-----