--- /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(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 = 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$.predictShapeAux(data,
+ fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
+ }
+
+ # Indices of similar days for cross-validation; TODO: 45 = magic number
+ sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
+
+ # 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$.predictShapeAux(data,
+ fdays, i, horizon, h, kernel, simtype, FALSE)
+ if (!is.na(prediction[1]))
+ {
+ nb_jours = nb_jours + 1
+ error = error +
+ mean((data$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$.predictShapeAux(data,
+ fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
+ }
+ if (simtype == "exo")
+ {
+ return (private$.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$.predictShapeAux(data,
+ fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
+ }
+ }
+ ),
+ private = list(
+ # Precondition: "today" is full (no NAs)
+ .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
+ {
+ fdays = fdays[ fdays < today ]
+ # TODO: 3 = magic number
+ if (length(fdays) < 3)
+ return (NA)
+
+ if (simtype != "exo")
+ {
+ h_endo = ifelse(simtype=="mix", h[1], h)
+
+ # Distances from last observed day to days in the past
+ distances2 = rep(NA, length(fdays))
+ for (i in seq_along(fdays))
+ {
+ delta = data$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