if (!opera)
tdays = setdiff(tdays, today) #always exclude current day
- # Shortcut if window is known #TODO: cross-validation for number of days, on similar (yerste)days
+ # Shortcut if window is known
if (hasArg("window"))
{
return ( private$.predictShapeAux(data, tdays, today, predict_from, horizon,
best_window_exo = optimize(
errorOnLastNdays, c(0,7), simtype="exo")$minimum
}
+ if (local)
+ {
+ best_window_local = optimize(
+ errorOnLastNdays, c(3,30), simtype="none")$minimum
+ }
best_window =
if (simtype == "endo")
best_window_exo
else if (simtype == "mix")
c(best_window_endo,best_window_exo)
- else #none: value doesn't matter
- 1
+ else #none: no value
+ NULL
+ if (local)
+ best_window = c(best_window, best_window_local)
return( private$.predictShapeAux(data, tdays, today, predict_from, horizon, local,
best_window, simtype, opera, TRUE) )
if (local)
{
# limit=Inf to not censor any day (TODO: finite limit? 60?)
- tdays = getSimilarDaysIndices(today, data, limit=Inf, same_season=TRUE,
+ tdays <- getSimilarDaysIndices(today, data, limit=Inf, same_season=TRUE,
days_in=tdays_cut, operational=opera)
+ nb_neighbs <- round( window[length(window)] )
# TODO: 10 == magic number
- tdays = .getConstrainedNeighbs(today, data, tdays, min_neighbs=10)
+ tdays <- .getConstrainedNeighbs(today, data, tdays, nb_neighbs, opera)
if (length(tdays) == 1)
{
if (final_call)
{
private$.params$weights <- 1
private$.params$indices <- tdays
- private$.params$window <- 1
+ private$.params$window <- window
}
return ( data$getSerie(tdays[1])[predict_from:horizon] )
}
- max_neighbs = 12 #TODO: 10 or 12 or... ?
+ max_neighbs = nb_neighbs #TODO: something else?
if (length(tdays) > max_neighbs)
{
distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
if (simtype == "endo" || simtype == "mix")
{
- # Compute endogen similarities using given window
- window_endo = ifelse(simtype=="mix", window[1], window)
-
# Distances from last observed day to selected days in the past
# TODO: redundant computation if local==TRUE
distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
- simils_endo <- .computeSimils(distances2, window_endo)
+ # Compute endogen similarities using the given window
+ simils_endo <- .computeSimils(distances2, window[1])
}
if (simtype == "exo" || simtype == "mix")
{
- # Compute exogen similarities using given window
- window_exo = ifelse(simtype=="mix", window[2], window)
-
- distances2 <- .computeDistsExo(data, today, tdays)
+ distances2 <- .computeDistsExo(data, today, tdays, opera)
+ # Compute exogen similarities using the given window
+ window_exo = ifelse(simtype=="mix", window[2], window[1])
simils_exo <- .computeSimils(distances2, window_exo)
}
{
private$.params$weights <- similarities
private$.params$indices <- tdays
- private$.params$window <-
- if (simtype=="endo")
- window_endo
- else if (simtype=="exo")
- window_exo
- else if (simtype=="mix")
- c(window_endo,window_exo)
- else #none
- 1
+ private$.params$window <- window
}
return (prediction)
# @param min_neighbs Minimum number of points in a neighborhood
# @param max_neighbs Maximum number of points in a neighborhood
#
-.getConstrainedNeighbs = function(today, data, tdays, min_neighbs=10)
+.getConstrainedNeighbs = function(today, data, tdays, min_neighbs, opera)
{
- levelToday = data$getLevelHat(today)
+ levelToday = ifelse(opera, tail(data$getLevelHat(today),1), data$getLevel(today))
distances = sapply( tdays, function(i) abs(data$getLevel(i) - levelToday) )
#TODO: 1, +1, +3 : magic numbers
dist_thresh = 1
})
}
-.computeDistsExo <- function(data, today, tdays)
+.computeDistsExo <- function(data, today, tdays, opera)
{
M = matrix( ncol=1+length(tdays), nrow=1+length(data$getExo(1)) )
- M[,1] = c( data$getLevelHat(today), as.double(data$getExoHat(today)) )
+ if (opera)
+ M[,1] = c( tail(data$getLevelHat(today),1), as.double(data$getExoHat(today)) )
+ else
+ M[,1] = c( data$getLevel(today), as.double(data$getExo(today)) )
for (i in seq_along(tdays))
M[,i+1] = c( data$getLevel(tdays[i]), as.double(data$getExo(tdays[i])) )