fdays = getNoNA2(data, max(today-memory,1), today-1)
# Get optional args
- local = ifelse(hasArg("local"), list(...)$local, FALSE) #same level + season?
- simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
+ local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
+ simtype = ifelse(hasArg("simtype"), list(...)$simtype, "none") #or "endo", or "exo"
if (hasArg("window"))
{
return ( private$.predictShapeAux(data,
cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
days_in=fdays)
- # Optimize h : h |--> sum of prediction errors on last 45 "similar" days
+ # Optimize h : h |--> sum of prediction errors on last N "similar" days
errorOnLastNdays = function(window, simtype)
{
error = 0
return (error / nb_jours)
}
+ # TODO: 7 == magic number
if (simtype != "endo")
{
best_window_exo = optimize(
return (NA)
levelToday = data$getLevel(today)
distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- #TODO: 2, 3, 5, 10 magic numbers here...
+ #TODO: 2, 10, 3, 12 magic numbers here...
dist_thresh = 2
- min_neighbs = min(3,length(fdays))
+ min_neighbs = min(10,length(fdays))
repeat
{
same_pollution = (distances <= dist_thresh)
dist_thresh = dist_thresh + 3
}
fdays = fdays[same_pollution]
- max_neighbs = 10
+ max_neighbs = 12
if (nb_neighbs > max_neighbs)
{
# Keep only max_neighbs closest neighbors
else
fdays = fdays_cut #no conditioning
- if (simtype != "exo")
+ if (simtype == "endo" || simtype == "mix")
{
# Compute endogen similarities using given window
window_endo = ifelse(simtype=="mix", window[1], window)
simils_endo = exp(-distances2/(sd_dist*window_endo^2))
}
- if (simtype != "endo")
+ if (simtype == "exo" || simtype == "mix")
{
# Compute exogen similarities using given window
- h_exo = ifelse(simtype=="mix", window[2], window)
+ window_exo = ifelse(simtype=="mix", window[2], window)
M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
simils_exo
else if (simtype == "endo")
simils_endo
- else #mix
+ else if (simtype == "mix")
simils_endo * simils_exo
+ else #none
+ rep(1, length(fdays))
similarities = similarities / sum(similarities)
prediction = rep(0, horizon)