#' 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
+#' @aliases F_Neighbors
#'
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
return (NA)
# Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays = getNoNA2(data, max(today-memory,1), today-1)
+ fdays = .getNoNA2(data, max(today-memory,1), today-1)
# Get optional args
local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
if (local)
{
- # Neighbors: days in "same season"; TODO: 60 == magic number...
+ # TODO: 60 == magic number
fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE,
days_in=fdays_cut)
if (length(fdays) <= 1)
return (NA)
- levelToday = data$getLevel(today)
- distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- #TODO: 2, 10, 3, 12 magic numbers here...
- dist_thresh = 2
- min_neighbs = min(10,length(fdays))
- repeat
- {
- same_pollution = (distances <= dist_thresh)
- nb_neighbs = sum(same_pollution)
- if (nb_neighbs >= min_neighbs) #will eventually happen
- break
- dist_thresh = dist_thresh + 3
- }
- fdays = fdays[same_pollution]
- max_neighbs = 12
- if (nb_neighbs > max_neighbs)
- {
- # Keep only max_neighbs closest neighbors
- fdays = fdays[
- sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
- }
- if (length(fdays) == 1) #the other extreme...
+ # TODO: 10, 12 == magic numbers
+ fdays = .getConstrainedNeighbs(today,data,fdays,min_neighbs=10,max_neighbs=12)
+ if (length(fdays) == 1)
{
if (final_call)
{
mean(delta^2)
})
- sd_dist = sd(distances2)
- if (sd_dist < .25 * sqrt(.Machine$double.eps))
- {
-# warning("All computed distances are very close: stdev too small")
- sd_dist = 1 #mostly for tests... FIXME:
- }
- simils_endo = exp(-distances2/(sd_dist*window_endo^2))
+ simils_endo <- .computeSimils(distances2, window_endo)
}
if (simtype == "exo" || simtype == "mix")
delta %*% sigma_inv %*% delta
})
- sd_dist = sd(distances2)
- if (sd_dist < .25 * sqrt(.Machine$double.eps))
- {
-# warning("All computed distances are very close: stdev too small")
- sd_dist = 1 #mostly for tests... FIXME:
- }
- simils_exo = exp(-distances2/(sd_dist*window_exo^2))
+ simils_exo <- .computeSimils(distances2, window_exo)
}
similarities =
}
)
)
+
+#' getNoNA2
+#'
+#' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
+#'
+#' @inheritParams dateIndexToInteger
+#' @param first First index (included)
+#' @param last Last index (included)
+#'
+.getNoNA2 = function(data, first, last)
+{
+ (first:last)[ sapply(first:last, function(i)
+ !any( is.na(data$getCenteredSerie(i)) | is.na(data$getCenteredSerie(i+1)) )
+ ) ]
+}
+
+#' getConstrainedNeighbs
+#'
+#' Get indices of neighbors of similar pollution level (among same season + day type).
+#'
+#' @param today Index of current day
+#' @param data Object of class Data
+#' @param fdays Current set of "first days" (no-NA pairs)
+#' @param min_neighbs Minimum number of points in a neighborhood
+#' @param max_neighbs Maximum number of points in a neighborhood
+#'
+.getConstrainedNeighbs = function(today, data, fdays, min_neighbs=10, max_neighbs=12)
+{
+ levelToday = data$getLevel(today)
+ distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
+ #TODO: 2, +3 : magic numbers
+ dist_thresh = 2
+ min_neighbs = min(min_neighbs,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 = 12
+ if (nb_neighbs > max_neighbs)
+ {
+ # Keep only max_neighbs closest neighbors
+ fdays = fdays[
+ sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
+ }
+ fdsays
+}
+
+#' compute similarities
+#'
+#' Apply the gaussian kernel on computed squared distances.
+#'
+#' @param distances2 Squared distances
+#' @param window Window parameter for the kernel
+#'
+.computeSimils <- function(distances2, window)
+{
+ sd_dist = sd(distances2)
+ if (sd_dist < .25 * sqrt(.Machine$double.eps))
+ {
+# warning("All computed distances are very close: stdev too small")
+ sd_dist = 1 #mostly for tests... FIXME:
+ }
+ exp(-distances2/(sd_dist*window^2))
+}
projects / talweg.git / blobdiff