#' @include Forecaster.R
#'
-#' @title Neighbors Forecaster
+#' Neighbors Forecaster
#'
-#' @description Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{Forecaster}}
-NeighborsForecaster = setRefClass(
- Class = "NeighborsForecaster",
- contains = "Forecaster",
-
- methods = list(
- initialize = function(...)
- {
- callSuper(...)
- },
- predictShape = function(today, memory, horizon, ...)
+#' 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(data, today, memory, horizon, ...)
{
# (re)initialize computed parameters
- params <<- list("weights"=NA, "indices"=NA, "window"=NA)
+ 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 = 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 (.predictShapeAux(fdays,today,horizon,list(...)$h_window,kernel,simtype,TRUE))
-
- # HACK for test reports: complete some days with a few NAs, for nicer graphics
- nas_in_serie = is.na(data$getSerie(today))
- if (any(nas_in_serie))
{
- if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
- return (NA)
- for (i in seq_along(nas_in_serie))
- {
- if (nas_in_serie[i])
- {
- #look left
- left = i-1
- while (left>=1 && nas_in_serie[left])
- left = left-1
- #look right
- right = i+1
- while (right<=length(nas_in_serie) && nas_in_serie[right])
- right = right+1
- #HACK: modify by-reference Data object...
- data$data[[today]]$serie[i] <<-
- if (left==0) data$data[[today]]$serie[right]
- else if (right==0) data$data[[today]]$serie[left]
- else (data$data[[today]]$serie[left] + data$data[[today]]$serie[right]) / 2.
- }
- }
+ return ( private$.predictShapeAux(data,
+ fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
}
- # Determine indices of no-NAs days followed by no-NAs tomorrows
- first_day = max(today - memory, 1)
- fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
- !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1)))
- }) ]
-
# 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 = .predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(data,
+ fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
- error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ error = error +
+ mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
}
}
return (error / nb_jours)
}
if (simtype != "endo")
- h_best_exo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+ {
+ h_best_exo = optimize(
+ errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
+ }
if (simtype != "exo")
- h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ {
+ h_best_endo = optimize(
+ errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
+ }
if (simtype == "endo")
- return (.predictShapeAux(fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ {
+ return (private$.predictShapeAux(data,
+ fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ }
if (simtype == "exo")
- return (.predictShapeAux(fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ {
+ return (private$.predictShapeAux(data,
+ fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ }
if (simtype == "mix")
{
h_best_mix = c(h_best_endo,h_best_exo)
- return (.predictShapeAux(fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
+ return(private$.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)
{
- dat = data$data #HACK: faster this way...
-
fdays = fdays[ fdays < today ]
# TODO: 3 = magic number
if (length(fdays) < 3)
h_endo = ifelse(simtype=="mix", h[1], h)
# Distances from last observed day to days in the past
- distances2 = rep(NA, length(fdays))
- for (i in seq_along(fdays))
- {
- delta = dat[[today]]$serie - dat[[ fdays[i] ]]$serie
- # Require at least half of non-NA common values to compute the distance
- if (sum(is.na(delta)) <= 0) #length(delta)/2)
- distances2[i] = mean(delta^2) #, na.rm=TRUE)
- }
+ serieToday = data$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
{
h_exo = ifelse(simtype=="mix", h[2], h)
- M = matrix( nrow=1+length(fdays), ncol=1+length(dat[[today]]$exo) )
- M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
+ M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
+ M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
for (i in seq_along(fdays))
- M[i+1,] = c( dat[[ fdays[i] ]]$level, as.double(dat[[ fdays[i] ]]$exo) )
+ 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?
# 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$double.eps)
+ {
+# warning("All computed distances are very close: stdev too small")
+ sd_dist = 1 #mostly for tests... FIXME:
+ }
simils_exo =
if (kernel=="Gauss")
exp(-distances2/(sd_dist*h_exo^2))
- else { #Epanechnikov
+ else
+ {
+ # Epanechnikov
u = 1 - distances2/(sd_dist*h_exo^2)
u[abs(u)>1] = 0.
u
simils_endo * simils_exo
prediction = rep(0, horizon)
- for (i in seq_along(fdays_indices))
- prediction = prediction + similarities[i] * dat[[ fdays_indices[i]+1 ]]$serie[1:horizon]
+ for (i in seq_along(fdays))
+ prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
prediction = prediction / sum(similarities, na.rm=TRUE)
if (final_call)
{
- params$weights <<- similarities
- params$indices <<- fdays_indices
- params$window <<-
- if (simtype=="endo") {
+ private$.params$weights <- similarities
+ private$.params$indices <- fdays
+ private$.params$window <-
+ 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