# (re)initialize computed parameters
private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
+ # Determine indices of no-NAs days followed by no-NAs tomorrows
+ fdays = private$.data$getCoupleDays(max(today-memory,1), today-1)
+
# Get optional args
simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
- # Determine indices of no-NAs days followed by no-NAs tomorrows
- first_day = max(today - memory, 1)
- fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
- !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1)))
- }) ]
-
# Indices of similar days for cross-validation; TODO: 45 = magic number
sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
- error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ error = error +
+ mean((private$.data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
}
}
return (error / nb_jours)
}
if (simtype != "endo")
- h_best_exo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+ {
+ h_best_exo = optimize(
+ errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+ }
if (simtype != "exo")
- h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ {
+ h_best_endo = optimize(
+ errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ }
if (simtype == "endo")
- return(private$.predictShapeAux(fdays,today,horizon,h_best_endo,kernel,"endo",TRUE))
+ {
+ return (private$.predictShapeAux(
+ fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ }
if (simtype == "exo")
- return(private$.predictShapeAux(fdays,today,horizon,h_best_exo,kernel,"exo",TRUE))
+ {
+ return (private$.predictShapeAux(
+ fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ }
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(fdays,today,horizon,h_best_mix,kernel,"mix",TRUE))
+ return(private$.predictShapeAux(
+ fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
}
),
if (length(fdays) < 3)
return (NA)
+ data = private$.data #shorthand
+
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)