+++ /dev/null
-#' @include Forecaster.R
-#'
-#' Neighbors Forecaster
-#'
-#' 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, ...)
- {
- # (re)initialize computed parameters
- 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(...)$kernel, "Gauss") #or "Epan"
- if (hasArg(h_window))
- {
- return ( private$.predictShapeAux(
- fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
- }
-
- # Determine indices of no-NAs days followed by no-NAs tomorrows
- first_day = max(today - memory, 1)
- fdays = (first_day:(today-1))[ sapply(first_day:(today-1), function(i) {
- !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1)))
- }) ]
-
- # Indices of similar days for cross-validation; TODO: 45 = magic number
- sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
-
- # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
- errorOnLastNdays = function(h, kernel, simtype)
- {
- error = 0
- nb_jours = 0
- for (i in intersect(fdays,sdays))
- {
- # mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(fdays, i, horizon, h, kernel, simtype, FALSE)
- if (!is.na(prediction[1]))
- {
- nb_jours = nb_jours + 1
- error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
- }
- }
- return (error / nb_jours)
- }
-
- if (simtype != "endo")
- h_best_exo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
- if (simtype != "exo")
- h_best_endo = optimize(errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
-
- if (simtype == "endo")
- return(private$.predictShapeAux(fdays,today,horizon,h_best_endo,kernel,"endo",TRUE))
- if (simtype == "exo")
- return(private$.predictShapeAux(fdays,today,horizon,h_best_exo,kernel,"exo",TRUE))
- if (simtype == "mix")
- {
- h_best_mix = c(h_best_endo,h_best_exo)
- return(private$.predictShapeAux(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)
- {
- fdays = fdays[ fdays < today ]
- # TODO: 3 = magic number
- if (length(fdays) < 3)
- return (NA)
-
- 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)
- }
-
- sd_dist = sd(distances2)
- if (sd_dist < .Machine$double.eps)
- sd_dist = 1 #mostly for tests... FIXME:
- simils_endo =
- if (kernel=="Gauss")
- exp(-distances2/(sd_dist*h_endo^2))
- else { #Epanechnikov
- u = 1 - distances2/(sd_dist*h_endo^2)
- u[abs(u)>1] = 0.
- u
- }
- }
-
- if (simtype != "endo")
- {
- h_exo = ifelse(simtype=="mix", h[2], h)
-
- M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
- M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
- for (i in seq_along(fdays))
- M[i+1,] = c( 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
- }
-
- sd_dist = sd(distances2)
- 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
- }
- }
-
- similarities =
- if (simtype == "exo")
- simils_exo
- else if (simtype == "endo")
- simils_endo
- else #mix
- simils_endo * simils_exo
-
- prediction = rep(0, horizon)
- for (i in seq_along(fdays))
- prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
- prediction = prediction / sum(similarities, na.rm=TRUE)
-
- if (final_call)
- {
- private$.params$weights <- similarities
- private$.params$indices <- fdays
- private$.params$window <-
- if (simtype=="endo") {
- h_endo
- } else if (simtype=="exo") {
- h_exo
- } else { #mix
- c(h_endo,h_exo)
- }
- }
-
- return (prediction)
- }
- )
-)
projects / talweg.git / blobdiff