#' Predict tomorrow as a weighted combination of "futures of the past" days.
#' Inherits \code{\link{Forecaster}}
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
- inherit = "Forecaster",
+ inherit = Forecaster,
public = list(
predictShape = function(today, memory, horizon, ...)
{
# (re)initialize computed parameters
- params <<- list("weights"=NA, "indices"=NA, "window"=NA)
+ private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
# Get optional args
simtype = ifelse(hasArg("simtype"), list(...)$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))
+ {
+ return ( private$.predictShapeAux(
+ fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
+ }
# Determine indices of no-NAs days followed by no-NAs tomorrows
first_day = max(today - memory, 1)
for (i in intersect(fdays,sdays))
{
# mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = .predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
+ prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
if (!is.na(prediction[1]))
{
nb_jours = nb_jours + 1
h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
if (simtype == "endo")
- return (.predictShapeAux(fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ return(private$.predictShapeAux(fdays,today,horizon,h_best_endo,kernel,"endo",TRUE))
if (simtype == "exo")
- return (.predictShapeAux(fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
+ return(private$.predictShapeAux(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$.predictShapeAux(fdays,today,horizon,h_best_mix,kernel,"mix",TRUE))
}
}
),
simils_endo * simils_exo
prediction = rep(0, horizon)
- for (i in seq_along(fdays_indices))
- prediction = prediction + similarities[i] * data$getSerie(fdays_indices[i]+1)[1:horizon]
+ for (i in seq_along(fdays))
+ prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
prediction = prediction / sum(similarities, na.rm=TRUE)
if (final_call)
{
- params$weights <<- similarities
- params$indices <<- fdays_indices
- params$window <<-
+ private$.params$weights <- similarities
+ private$.params$indices <- fdays
+ private$.params$window <-
if (simtype=="endo") {
h_endo
} else if (simtype=="exo") {