-#' @include Forecaster.R
-#'
#' Neighbors Forecaster
#'
-#' Predict tomorrow as a weighted combination of "futures of the past" days.
-#' Inherits \code{\link{Forecaster}}
+#' Predict next serie as a weighted combination of "futures of the past" days,
+#' where days in the past are chosen and weighted according to some similarity measures.
+#'
+#' The main method is \code{predictShape()}, taking arguments data, today, memory,
+#' horizon respectively for the dataset (object output of \code{getData()}), the current
+#' index, the data depth (in days) and the number of time steps to forecast.
+#' In addition, optional arguments can be passed:
+#' \itemize{
+#' \item local : TRUE (default) to constrain neighbors to be "same days within same
+#' season"
+#' \item simtype : 'endo' for a similarity based on the series only,<cr>
+#' 'exo' for a similaruty based on exogenous variables only,<cr>
+#' 'mix' for the product of 'endo' and 'exo',<cr>
+#' 'none' (default) to apply a simple average: no computed weights
+#' \item window : A window for similarities computations; override cross-validation
+#' window estimation.
+#' }
+#' The method is summarized as follows:
+#' \enumerate{
+#' \item Determine N (=20) recent days without missing values, and followed by a
+#' tomorrow also without missing values.
+#' \item Optimize the window parameters (if relevant) on the N chosen days.
+#' \item Considering the optimized window, compute the neighbors (with locality
+#' constraint or not), compute their similarities -- using a gaussian kernel if
+#' simtype != "none" -- and average accordingly the "tomorrows of neigbors" to
+#' obtain the final prediction.
+#' }
+#'
+#' @docType class
+#' @format R6 class, inherits Forecaster
+#' @alias F_Neighbors
#'
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
fdays = getNoNA2(data, max(today-memory,1), today-1)
# Get optional args
- local = ifelse(hasArg("local"), list(...)$local, FALSE) #same level + season?
- simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
+ local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
+ simtype = ifelse(hasArg("simtype"), list(...)$simtype, "none") #or "endo", or "exo"
if (hasArg("window"))
{
return ( private$.predictShapeAux(data,
cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
days_in=fdays)
- # Optimize h : h |--> sum of prediction errors on last 45 "similar" days
+ # Optimize h : h |--> sum of prediction errors on last N "similar" days
errorOnLastNdays = function(window, simtype)
{
error = 0
return (error / nb_jours)
}
- if (simtype != "endo")
- {
- best_window_exo = optimize(
- errorOnLastNdays, c(0,7), simtype="exo")$minimum
- }
- if (simtype != "exo")
+ # TODO: 7 == magic number
+ if (simtype=="endo" || simtype=="mix")
{
best_window_endo = optimize(
errorOnLastNdays, c(0,7), simtype="endo")$minimum
}
-
- if (simtype == "endo")
+ if (simtype=="exo" || simtype=="mix")
{
- return (private$.predictShapeAux(data, fdays, today, horizon, local,
- best_window_endo, "endo", TRUE))
- }
- if (simtype == "exo")
- {
- return (private$.predictShapeAux(data, fdays, today, horizon, local,
- best_window_exo, "exo", TRUE))
- }
- if (simtype == "mix")
- {
- return(private$.predictShapeAux(data, fdays, today, horizon, local,
- c(best_window_endo,best_window_exo), "mix", TRUE))
+ best_window_exo = optimize(
+ errorOnLastNdays, c(0,7), simtype="exo")$minimum
}
+
+ best_window =
+ if (simtype == "endo")
+ best_window_endo
+ else if (simtype == "exo")
+ best_window_exo
+ else if (simtype == "mix")
+ c(best_window_endo,best_window_exo)
+ else #none: value doesn't matter
+ 1
+
+ return(private$.predictShapeAux(data, fdays, today, horizon, local,
+ best_window, simtype, TRUE))
}
),
private = list(
return (NA)
levelToday = data$getLevel(today)
distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- #TODO: 2, 3, 5, 10 magic numbers here...
+ #TODO: 2, 10, 3, 12 magic numbers here...
dist_thresh = 2
- min_neighbs = min(3,length(fdays))
+ min_neighbs = min(10,length(fdays))
repeat
{
same_pollution = (distances <= dist_thresh)
dist_thresh = dist_thresh + 3
}
fdays = fdays[same_pollution]
- max_neighbs = 10
+ max_neighbs = 12
if (nb_neighbs > max_neighbs)
{
# Keep only max_neighbs closest neighbors
private$.params$indices <- fdays
private$.params$window <- 1
}
- return ( data$getSerie(fdays[1])[1:horizon] ) #what else?!
+ return ( data$getSerie(fdays[1])[1:horizon] )
}
}
else
fdays = fdays_cut #no conditioning
- if (simtype != "exo")
+ if (simtype == "endo" || simtype == "mix")
{
# Compute endogen similarities using given window
window_endo = ifelse(simtype=="mix", window[1], window)
simils_endo = exp(-distances2/(sd_dist*window_endo^2))
}
- if (simtype != "endo")
+ if (simtype == "exo" || simtype == "mix")
{
# Compute exogen similarities using given window
- h_exo = ifelse(simtype=="mix", window[2], window)
+ window_exo = ifelse(simtype=="mix", window[2], window)
M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
simils_exo
else if (simtype == "endo")
simils_endo
- else #mix
+ else if (simtype == "mix")
simils_endo * simils_exo
+ else #none
+ rep(1, length(fdays))
similarities = similarities / sum(similarities)
prediction = rep(0, horizon)
window_endo
else if (simtype=="exo")
window_exo
- else #mix
+ else if (simtype=="mix")
c(window_endo,window_exo)
+ else #none
+ 1
}
return (prediction)
projects / talweg.git / blobdiff