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")
return (NA)
levelToday = data$getLevel(today)
distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- dist_thresh = 1
+ #TODO: 2, 3, 5, 10 magic numbers here...
+ dist_thresh = 2
+ min_neighbs = min(3,length(fdays))
repeat
{
same_pollution = (distances <= dist_thresh)
- if (sum(same_pollution) >= 2) #will eventually happen
+ nb_neighbs = sum(same_pollution)
+ if (nb_neighbs >= min_neighbs) #will eventually happen
break
- dist_thresh = dist_thresh + 1
+ dist_thresh = dist_thresh + 3
}
fdays = fdays[same_pollution]
- if (length(fdays) == 1)
+ max_neighbs = 10
+ if (nb_neighbs > max_neighbs)
+ {
+ # Keep only max_neighbs closest neighbors
+ fdays = fdays[ sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
+ }
+ if (length(fdays) == 1) #the other extreme...
{
if (final_call)
{
simils_endo
else #mix
simils_endo * simils_exo
+ similarities = similarities / sum(similarities)
prediction = rep(0, horizon)
for (i in seq_along(fdays))
prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
- prediction = prediction / sum(similarities, na.rm=TRUE)
if (final_call)
{