X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=12595d998f82d4883069f4c02a32d458b21aaa70;hb=102bcfda4afbb5cfee885cbee0f55545624168fd;hp=c9eda053820615485e19079b493fefa7329b1172;hpb=eef545170c5a76b710184db6b695c15b20759177;p=talweg.git

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index c9eda05..12595d9 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,9 +1,36 @@
-#' @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,
@@ -125,7 +152,7 @@ NeighborsForecaster = R6::R6Class("NeighborsForecaster",
 						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