+++ /dev/null
-#' @include Forecaster.R
-#'
-#' Neighbors2 Forecaster
-#'
-#' Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{Forecaster}}
-#'
-Neighbors2Forecaster = R6::R6Class("Neighbors2Forecaster",
- 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: 20 = magic number
- cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE, days_in=fdays)
-
- # 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 seq_along(cv_days))
- {
- # mix_strategy is never used here (simtype != "mix"), therefore left blank
- prediction = private$.predictShapeAux(data,
- fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
- if (!is.na(prediction[1]))
- {
- nb_jours = nb_jours + 1
- error = error + mean((data$getSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
- }
- }
- return (error / nb_jours)
- }
-
- if (simtype != "endo")
- {
- h_best_exo = optimize(
- errorOnLastNdays, c(0,7), kernel=kernel, simtype="exo")$minimum
- }
- if (simtype != "exo")
- {
- h_best_endo = optimize(
- errorOnLastNdays, c(0,7), kernel=kernel, simtype="endo")$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$.predictShapeAux(data,
- fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
- }
- }
- ),
- private = list(
- # Precondition: "today" is full (no NAs)
- .predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
- {
- fdays_cut = fdays[ fdays < today ]
- # TODO: 3 = magic number
- if (length(fdays_cut) < 3)
- return (NA)
-
- # Neighbors: days in "same season"; TODO: 60 == magic number...
- fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE, days_in=fdays_cut)
- if (length(fdays) <= 1)
- return (NA)
- levelToday = data$getLevel(today)
- distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- #TODO: 2, 3, 5, 10 magic numbers here...
- dist_thresh = 2
- min_neighbs = min(3,length(fdays))
- repeat
- {
- same_pollution = (distances <= dist_thresh)
- nb_neighbs = sum(same_pollution)
- if (nb_neighbs >= min_neighbs) #will eventually happen
- break
- dist_thresh = dist_thresh + 3
- }
- fdays = fdays[same_pollution]
- max_neighbs = 10
- if (nb_neighbs > max_neighbs)
- {
- # Keep only max_neighbs closest neighbors
- fdays = fdays[ sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
- }
- if (length(fdays) == 1) #the other extreme...
- {
- if (final_call)
- {
- private$.params$weights <- 1
- private$.params$indices <- fdays
- private$.params$window <- 1
- }
- return ( data$getSerie(fdays[1])[1:horizon] ) #what else?!
- }
-
- if (simtype != "exo")
- {
- h_endo = ifelse(simtype=="mix", h[1], h)
-
- # Distances from last observed day to days in the past
- serieToday = data$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
- 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$getExo(fdays[i])) )
-
- sigma = cov(M) #NOTE: robust covariance is way too slow
- # TODO: 10 == magic number; more robust way == det, or always ginv()
- sigma_inv =
- if (length(fdays) > 10)
- solve(sigma)
- else
- MASS::ginv(sigma)
-
- # Distances from last observed day to days in the past
- distances2 = sapply(seq_along(fdays), function(i) {
- delta = M[1,] - M[i+1,]
- delta %*% sigma_inv %*% delta
- })
-
- sd_dist = sd(distances2)
- if (sd_dist < .25 * sqrt(.Machine$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
- 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
- similarities = similarities / sum(similarities)
-
- prediction = rep(0, horizon)
- for (i in seq_along(fdays))
- prediction = prediction + similarities[i] * data$getSerie(fdays[i]+1)[1:horizon]
-
- if (final_call)
- {
- prediction = prediction - mean(prediction) #predict centered serie (artificial...)
- private$.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