#'
#' Predict tomorrow as a weighted combination of "futures of the past" days.
#' Inherits \code{\link{Forecaster}}
+#'
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
public = list(
- predictShape = function(today, memory, horizon, ...)
+ predictShape = function(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
+ # Do not forecast on days with NAs (TODO: softer condition...)
+ if (any(is.na(data$getCenteredSerie(today))))
+ return (NA)
+
# Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays = private$.data$getCoupleDays(max(today-memory,1), today-1)
+ fdays = getNoNA2(data, max(today-memory,1), today-1)
# Get optional args
simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
if (hasArg(h_window))
{
- return ( private$.predictShapeAux(
+ return ( private$.predictShapeAux(data,
fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
# Indices of similar days for cross-validation; TODO: 45 = magic number
sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+ cv_days = intersect(fdays,sdays)
+ # Limit to 20 most recent matching days (TODO: 20 == magic number)
+ cv_days = sort(cv_days,decreasing=TRUE)[1:min(20,length(cv_days))]
+
# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
errorOnLastNdays = function(h, kernel, simtype)
{
error = 0
nb_jours = 0
- for (i in intersect(fdays,sdays))
+ for (i in seq_along(cv_days))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(data,
+ fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
error = error +
- mean((private$.data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
}
}
return (error / nb_jours)
if (simtype == "endo")
{
- return (private$.predictShapeAux(
+ return (private$.predictShapeAux(data,
fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
}
if (simtype == "exo")
{
- return (private$.predictShapeAux(
+ return (private$.predictShapeAux(data,
fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
}
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(
+ return(private$.predictShapeAux(data,
fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
}
}
),
private = list(
# Precondition: "today" is full (no NAs)
- .predictShapeAux = function(fdays, today, horizon, h, kernel, simtype, final_call)
+ .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
{
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
if (length(fdays) < 3)
return (NA)
- data = private$.data #shorthand
-
if (simtype != "exo")
{
h_endo = ifelse(simtype=="mix", h[1], h)
# Distances from last observed day to days in the past
- distances2 = rep(NA, length(fdays))
- for (i in seq_along(fdays))
- {
- delta = data$getCenteredSerie(today) - data$getCenteredSerie(fdays[i])
- # Require at least half of non-NA common values to compute the distance
- if (sum(is.na(delta)) <= 0) #length(delta)/2)
- distances2[i] = mean(delta^2) #, na.rm=TRUE)
- }
+ serieToday = data$getSerie(today)
+ distances2 = sapply(fdays, function(i) {
+ delta = serieToday - data$getSerie(i)
+ mean(delta^2)
+ })
sd_dist = sd(distances2)
if (sd_dist < .Machine$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
+ else
+ {
+ # Epanechnikov
u = 1 - distances2/(sd_dist*h_endo^2)
u[abs(u)>1] = 0.
u
sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
# Distances from last observed day to days in the past
- distances2 = rep(NA, nrow(M)-1)
- for (i in 2:nrow(M))
- {
- delta = M[1,] - M[i,]
- distances2[i-1] = delta %*% sigma_inv %*% delta
- }
+ distances2 = sapply(seq_along(fdays), function(i) {
+ delta = M[1,] - M[i+1,]
+ delta %*% sigma_inv %*% delta
+ })
sd_dist = sd(distances2)
+ if (sd_dist < .Machine$double.eps)
+ {
+# warning("All computed distances are very close: stdev too small")
+ sd_dist = 1 #mostly for tests... FIXME:
+ }
simils_exo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_exo^2))
- else { #Epanechnikov
+ else
+ {
+ # Epanechnikov
u = 1 - distances2/(sd_dist*h_exo^2)
u[abs(u)>1] = 0.
u
prediction = rep(0, horizon)
for (i in seq_along(fdays))
- prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
+ prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
prediction = prediction / sum(similarities, na.rm=TRUE)
if (final_call)
private$.params$weights <- similarities
private$.params$indices <- fdays
private$.params$window <-
- if (simtype=="endo") {
+ if (simtype=="endo")
h_endo
- } else if (simtype=="exo") {
+ else if (simtype=="exo")
h_exo
- } else { #mix
+ else #mix
c(h_endo,h_exo)
- }
}
return (prediction)
projects / talweg.git / blobdiff