--- /dev/null
+#' @include Forecaster.R
+#'
+#' @title 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, ...)
+ {
+ # (re)initialize computed parameters
+ params <<- list("weights"=NA, "indices"=NA, "window"=NA)
+
+ first_day = max(today - memory, 1)
+ # The first day is generally not complete:
+ if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
+ first_day = 2
+
+ # Predict only on (almost) non-NAs days
+ nas_in_serie = is.na(data$getSerie(today))
+ if (any(nas_in_serie))
+ {
+ #TODO: better define "repairing" conditions (and method)
+ if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
+ return (NA)
+ for (i in seq_along(nas_in_serie))
+ {
+ if (nas_in_serie[i])
+ {
+ #look left
+ left = i-1
+ while (left>=1 && nas_in_serie[left])
+ left = left-1
+ #look right
+ right = i+1
+ while (right<=length(nas_in_serie) && nas_in_serie[right])
+ right = right+1
+ #HACK: modify by-reference Data object...
+ data$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]]$serie[right]) / 2.
+ }
+ }
+ }
+
+ # Determine indices of no-NAs days followed by no-NAs tomorrows
+ fdays_indices = c()
+ for (i in first_day:(today-1))
+ {
+ if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
+ fdays_indices = c(fdays_indices, i)
+ }
+
+ #GET OPTIONAL PARAMS
+ # Similarity computed with exogenous variables ? endogenous ? both ? ("exo","endo","mix")
+ simtype = ifelse(hasArg("simtype"), list(...)$simtype, "exo")
+ simthresh = ifelse(hasArg("simthresh"), list(...)$simthresh, 0.)
+ kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss")
+ mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "neighb") #or "mult"
+ same_season = ifelse(hasArg("same_season"), list(...)$same_season, TRUE)
+ if (hasArg(h_window))
+ return (.predictShapeAux(fdays_indices, today, horizon, list(...)$h_window, kernel,
+ simtype, simthresh, mix_strategy, FALSE))
+ #END GET
+
+ # Indices for cross-validation; TODO: 45 = magic number
+ indices = getSimilarDaysIndices(today, limit=45, same_season=same_season)
+ #indices = (end_index-45):(end_index-1)
+
+ # Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
+ errorOnLastNdays = function(h, kernel, simtype)
+ {
+ error = 0
+ nb_jours = 0
+ for (i in indices)
+ {
+ # NOTE: predict only on non-NAs days followed by non-NAs (TODO:)
+ if (!any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))))
+ {
+ nb_jours = nb_jours + 1
+ # mix_strategy is never used here (simtype != "mix"), therefore left blank
+ prediction = .predictShapeAux(fdays_indices, i, horizon, h, kernel, simtype,
+ simthresh, "", FALSE)
+ if (!is.na(prediction[1]))
+ error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+ }
+ }
+ return (error / nb_jours)
+ }
+
+ h_best_exo = 1.
+ if (simtype != "endo" && !(simtype=="mix" && mix_strategy=="neighb"))
+ {
+ h_best_exo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
+ simtype="exo")$minimum
+ }
+ if (simtype != "exo")
+ {
+ h_best_endo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
+ simtype="endo")$minimum
+ }
+
+ if (simtype == "endo")
+ {
+ return (.predictShapeAux(fdays_indices, today, horizon, h_best_endo, kernel, "endo",
+ simthresh, "", TRUE))
+ }
+ if (simtype == "exo")
+ {
+ return (.predictShapeAux(fdays_indices, today, horizon, h_best_exo, kernel, "exo",
+ simthresh, "", TRUE))
+ }
+ if (simtype == "mix")
+ {
+ return (.predictShapeAux(fdays_indices, today, horizon, c(h_best_endo,h_best_exo),
+ kernel, "mix", simthresh, mix_strategy, TRUE))
+ }
+ },
+ # Precondition: "today" is full (no NAs)
+ .predictShapeAux = function(fdays_indices, today, horizon, h, kernel, simtype, simthresh,
+ mix_strategy, final_call)
+ {
+ dat = data$data #HACK: faster this way...
+
+ fdays_indices = fdays_indices[fdays_indices < today]
+ # TODO: 3 = magic number
+ if (length(fdays_indices) < 3)
+ return (NA)
+
+ if (simtype != "exo")
+ {
+ h_endo = ifelse(simtype=="mix", h[1], h)
+
+ # Distances from last observed day to days in the past
+ distances2 = rep(NA, length(fdays_indices))
+ for (i in seq_along(fdays_indices))
+ {
+ delta = dat[[today]]$serie - dat[[ fdays_indices[i] ]]$serie
+ # Require at least half of non-NA common values to compute the distance
+ if (sum(is.na(delta)) <= 0) #length(delta)/2)
+ distances2[i] = mean(delta^2) #, na.rm=TRUE)
+ }
+
+ sd_dist = sd(distances2)
+ simils_endo =
+ if (kernel=="Gauss") {
+ exp(-distances2/(sd_dist*h_endo^2))
+ } else { #Epanechnikov
+ u = 1 - distances2/(sd_dist*h_endo^2)
+ u[abs(u)>1] = 0.
+ u
+ }
+ }
+
+ if (simtype != "endo")
+ {
+ h_exo = ifelse(simtype=="mix", h[2], h)
+
+ # TODO: [rnormand] if predict_at == 0h then we should use measures from day minus 1
+ M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo_hat) )
+ M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo_hat) )
+ for (i in seq_along(fdays_indices))
+ {
+ M[i+1,] = c( dat[[ fdays_indices[i] ]]$level,
+ as.double(dat[[ fdays_indices[i] ]]$exo_hat) )
+ }
+
+ sigma = cov(M) #NOTE: robust covariance is way too slow
+ sigma_inv = qr.solve(sigma)
+
+ # 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
+ }
+ }
+
+ if (simtype=="mix")
+ {
+ if (mix_strategy == "neighb")
+ {
+ #Only (60) most similar days according to exogen variables are kept into consideration
+ #TODO: 60 = magic number
+ keep_indices = sort(simils_exo, index.return=TRUE)$ix[1:(min(60,length(simils_exo)))]
+ simils_endo[-keep_indices] = 0.
+ } else #mix_strategy == "mult"
+ {
+ simils_endo = simils_endo * simils_exo
+ }
+ }
+
+ similarities =
+ if (simtype != "exo") {
+ simils_endo
+ } else {
+ simils_exo
+ }
+
+ if (simthresh > 0.)
+ {
+ max_sim = max(similarities)
+ # Set to 0 all similarities s where s / max_sim < simthresh, but keep at least 60
+ ordering = sort(similarities / max_sim, index.return=TRUE)
+ if (ordering[60] < simthresh)
+ {
+ similarities[ ordering$ix[ - (1:60) ] ] = 0.
+ } else
+ {
+ limit = 61
+ while (limit < length(similarities) && ordering[limit] >= simthresh)
+ limit = limit + 1
+ similarities[ ordering$ix[ - 1:limit] ] = 0.
+ }
+ }
+
+ prediction = rep(0, horizon)
+ for (i in seq_along(fdays_indices))
+ prediction = prediction + similarities[i] * dat[[ fdays_indices[i]+1 ]]$serie[1:horizon]
+
+ prediction = prediction / sum(similarities, na.rm=TRUE)
+ if (final_call)
+ {
+ params$weights <<- similarities
+ params$indices <<- fdays_indices
+ params$window <<-
+ if (simtype=="endo") {
+ h_endo
+ } else if (simtype=="exo") {
+ h_exo
+ } else {
+ c(h_endo,h_exo)
+ }
+ }
+ return (prediction)
+ }
+ )
+)
projects / talweg.git / blobdiff