fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
- # Indices of similar days for cross-validation; TODO: 45 = magic number
- sdays = getSimilarDaysIndices(today, data, limit=45, same_season=FALSE)
-
- cv_days = intersect(fdays,sdays)
- # Limit to 20 most recent matching days (TODO: 20 == magic number)
- cv_days = sort(cv_days,decreasing=TRUE)[1:min(20,length(cv_days))]
+ # Indices of similar days for cross-validation; TODO: 20 = magic number
+ cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE, days_in=fdays)
# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
errorOnLastNdays = function(h, kernel, simtype)
if (simtype != "endo")
{
h_best_exo = optimize(
- errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+ errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
}
if (simtype != "exo")
{
h_best_endo = optimize(
- errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$minimum
}
if (simtype == "endo")
simils_endo
else #mix
simils_endo * simils_exo
+ similarities = similarities / sum(similarities)
prediction = rep(0, horizon)
for (i in seq_along(fdays))
prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
- prediction = prediction / sum(similarities, na.rm=TRUE)
if (final_call)
{