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"
- kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
- if (hasArg(h_window))
+ if (hasArg("window"))
{
return ( private$.predictShapeAux(data,
- fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
+ fdays, today, horizon, local, list(...)$window, simtype, TRUE) )
}
- # Indices of similar days for cross-validation; TODO: 45 = magic number
- sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+ # Indices of similar days for cross-validation; TODO: 20 = magic number
+ cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
+ days_in=fdays)
- 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))]
-
- # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
- errorOnLastNdays = function(h, kernel, simtype)
+ # Optimize h : h |--> sum of prediction errors on last 45 "similar" days
+ errorOnLastNdays = function(window, simtype)
{
error = 0
nb_jours = 0
for (i in seq_along(cv_days))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(data,
- fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(data, fdays, cv_days[i], horizon, local,
+ window, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
error = error +
- mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
+ mean((data$getSerie(cv_days[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")$minimum
+ best_window_exo = optimize(
+ errorOnLastNdays, c(0,7), simtype="exo")$minimum
}
if (simtype != "exo")
{
- h_best_endo = optimize(
- errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ best_window_endo = optimize(
+ errorOnLastNdays, c(0,7), simtype="endo")$minimum
}
if (simtype == "endo")
{
- return (private$.predictShapeAux(data,
- fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ return (private$.predictShapeAux(data, fdays, today, horizon, local,
+ best_window_endo, "endo", TRUE))
}
if (simtype == "exo")
{
- return (private$.predictShapeAux(data,
- fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ return (private$.predictShapeAux(data, fdays, today, horizon, local,
+ best_window_exo, "exo", TRUE))
}
if (simtype == "mix")
{
- h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(data,
- fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
+ return(private$.predictShapeAux(data, fdays, today, horizon, local,
+ c(best_window_endo,best_window_exo), "mix", TRUE))
}
}
),
private = list(
# Precondition: "today" is full (no NAs)
- .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
+ .predictShapeAux = function(data, fdays, today, horizon, local, window, simtype,
+ final_call)
{
- fdays = fdays[ fdays < today ]
- # TODO: 3 = magic number
- if (length(fdays) < 3)
+ fdays_cut = fdays[ fdays < today ]
+ if (length(fdays_cut) <= 1)
return (NA)
+ if (local)
+ {
+ # Neighbors: days in "same season"; TODO: 60 == magic number...
+ fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE,
+ days_in=fdays_cut)
+ if (length(fdays) <= 1)
+ return (NA)
+ levelToday = data$getLevel(today)
+ distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
+ #TODO: 2, 3, 5, 10 magic numbers here...
+ dist_thresh = 2
+ min_neighbs = min(3,length(fdays))
+ repeat
+ {
+ same_pollution = (distances <= dist_thresh)
+ nb_neighbs = sum(same_pollution)
+ if (nb_neighbs >= min_neighbs) #will eventually happen
+ break
+ dist_thresh = dist_thresh + 3
+ }
+ fdays = fdays[same_pollution]
+ max_neighbs = 10
+ if (nb_neighbs > max_neighbs)
+ {
+ # Keep only max_neighbs closest neighbors
+ fdays = fdays[
+ sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
+ }
+ if (length(fdays) == 1) #the other extreme...
+ {
+ if (final_call)
+ {
+ private$.params$weights <- 1
+ private$.params$indices <- fdays
+ private$.params$window <- 1
+ }
+ return ( data$getSerie(fdays[1])[1:horizon] ) #what else?!
+ }
+ }
+ else
+ fdays = fdays_cut #no conditioning
+
if (simtype != "exo")
{
- h_endo = ifelse(simtype=="mix", h[1], h)
+ # Compute endogen similarities using given window
+ window_endo = ifelse(simtype=="mix", window[1], window)
# Distances from last observed day to days in the past
serieToday = data$getSerie(today)
})
sd_dist = sd(distances2)
- if (sd_dist < .Machine$double.eps)
+ if (sd_dist < .25 * sqrt(.Machine$double.eps))
{
# warning("All computed distances are very close: stdev too small")
sd_dist = 1 #mostly for tests... FIXME:
}
- simils_endo =
- if (kernel=="Gauss")
- exp(-distances2/(sd_dist*h_endo^2))
- else
- {
- # Epanechnikov
- u = 1 - distances2/(sd_dist*h_endo^2)
- u[abs(u)>1] = 0.
- u
- }
+ simils_endo = exp(-distances2/(sd_dist*window_endo^2))
}
if (simtype != "endo")
{
- h_exo = ifelse(simtype=="mix", h[2], h)
+ # Compute exogen similarities using given window
+ h_exo = ifelse(simtype=="mix", window[2], window)
M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
sigma = cov(M) #NOTE: robust covariance is way too slow
- sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
+ # TODO: 10 == magic number; more robust way == det, or always ginv()
+ sigma_inv =
+ if (length(fdays) > 10)
+ solve(sigma)
+ else
+ MASS::ginv(sigma)
# Distances from last observed day to days in the past
distances2 = sapply(seq_along(fdays), function(i) {
})
sd_dist = sd(distances2)
- if (sd_dist < .Machine$double.eps)
+ if (sd_dist < .25 * sqrt(.Machine$double.eps))
{
# warning("All computed distances are very close: stdev too small")
sd_dist = 1 #mostly for tests... FIXME:
}
- simils_exo =
- if (kernel=="Gauss")
- exp(-distances2/(sd_dist*h_exo^2))
- else
- {
- # Epanechnikov
- u = 1 - distances2/(sd_dist*h_exo^2)
- u[abs(u)>1] = 0.
- u
- }
+ simils_exo = exp(-distances2/(sd_dist*window_exo^2))
}
similarities =
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$getCenteredSerie(fdays[i]+1)[1:horizon]
- prediction = prediction / sum(similarities, na.rm=TRUE)
+ prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
if (final_call)
{
private$.params$indices <- fdays
private$.params$window <-
if (simtype=="endo")
- h_endo
+ window_endo
else if (simtype=="exo")
- h_exo
+ window_exo
else #mix
- c(h_endo,h_exo)
+ c(window_endo,window_exo)
}
return (prediction)