inherit = Forecaster,
public = list(
- predictShape = function(today, memory, horizon, ...)
+ predictShape = function(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
+ # Do not forecast on days with NAs (TODO: softer condition...)
+ if (any(is.na(data$getCenteredSerie(today))))
+ return (NA)
+
+ # Determine indices of no-NAs days followed by no-NAs tomorrows
+ fdays = getNoNA2(data, 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"
if (hasArg(h_window))
{
- return ( private$.predictShapeAux(
+ return ( private$.predictShapeAux(data,
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$getSerie(i+1)))
- }) ]
-
# Indices of similar days for cross-validation; TODO: 45 = magic number
sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
for (i in intersect(fdays,sdays))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(data,
+ fdays, i, horizon, h, kernel, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
- error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ error = error +
+ mean((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$.predictShapeAux(data,
+ fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ }
if (simtype == "exo")
- return(private$.predictShapeAux(fdays,today,horizon,h_best_exo,kernel,"exo",TRUE))
+ {
+ return (private$.predictShapeAux(data,
+ fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ }
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(fdays,today,horizon,h_best_mix,kernel,"mix",TRUE))
+ return(private$.predictShapeAux(data,
+ fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
}
),
private = list(
# Precondition: "today" is full (no NAs)
- .predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
+ .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number