X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FForecaster.R;h=52587602ee9b6c3533c70fc1245b3bb0dc4616e4;hb=d2ab47a744d8fb29c03a76a7ca2368dae53f9a57;hp=da8579b7312bfcc520d8e2a8504202bb68ff2cbd;hpb=98e958cab563866f8e00886b54336018a2e8bc97;p=talweg.git diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R index da8579b..5258760 100644 --- a/pkg/R/Forecaster.R +++ b/pkg/R/Forecaster.R @@ -1,25 +1,46 @@ #' Forecaster #' -#' Forecaster (abstract class, implemented by all forecasters) +#' Forecaster (abstract class, implemented by all forecasters). #' -#' @docType class -#' @importFrom R6 R6Class +#' A Forecaster object encapsulates parameters (which can be of various kinds, for +#' example "Neighbors" method stores informations about the considered neighborhood for +#' the current prediction task) and one main function: \code{predictSerie()}. This last +#' function (by default) calls \code{predictShape()} to get a forecast of a centered +#' serie, and then calls the "jump prediction" function -- see "field" section -- to +#' adjust it based on the last observed values. +#' +#' @usage # Forecaster$new(pjump) #warning: predictShape() is unimplemented #' -#' @field .params List of computed parameters, for post-run analysis (dev) -#' @field .pjump Function: how to predict the jump at day interface ? +#' @field .params List of computed parameters (if applicable). +#' @field .pjump Function: how to predict the jump at day interface? The arguments of +#' this function are -- in this order: +#' \itemize{ +#' \item data : object output of \code{getData()}, +#' \item today : index (integer or date) of the last known day in data, +#' \item memory : number of days to use in the past (including today), +#' \item horizon : number of time steps to predict, +#' \item params : optimized parameters in the main method \code{predictShape()}, +#' \item ... : additional arguments. +#' } +#' .pjump returns an estimation of the jump after the last observed value. #' #' @section Methods: #' \describe{ #' \item{\code{initialize(data, pjump)}}{ #' Initialize a Forecaster object with a Data object and a jump prediction function.} #' \item{\code{predictSerie(today,memory,horizon,...)}}{ -#' Predict a new serie of \code{horizon} values at day index \code{today} -#' using \code{memory} days in the past.} +#' Predict a new serie of \code{horizon} values at day index \code{today} using +#' \code{memory} days in the past.} #' \item{\code{predictShape(today,memory,horizon,...)}}{ -#' Predict a new shape of \code{horizon} values at day index \code{today} -#' using \code{memory} days in the past.} +#' Predict a new shape of \code{horizon} values at day index \code{today} using +#' \code{memory} days in the past.} #' \item{\code{getParameters()}}{ -#' Return (internal) parameters.}} +#' Return (internal) parameters.} +#' } +#' +#' @docType class +#' @format R6 class +#' Forecaster = R6::R6Class("Forecaster", private = list( .params = list(), @@ -31,15 +52,20 @@ Forecaster = R6::R6Class("Forecaster", private$.pjump <- pjump invisible(self) }, - predictSerie = function(data, today, memory, horizon, ...) + predictSerie = function(data, today, memory, predict_from, horizon, ...) { # Parameters (potentially) computed during shape prediction stage - predicted_shape = self$predictShape(data, today, memory, horizon, ...) - predicted_delta = private$.pjump(data,today,memory,horizon,private$.params,...) - # Predicted shape is aligned it on the end of current day + jump - predicted_shape+tail(data$getSerie(today),1)-predicted_shape[1]+predicted_delta + predicted_shape = self$predictShape(data,today,memory,predict_from,horizon,...) + predicted_delta = private$.pjump(data, today, memory, predict_from, horizon, + private$.params, ...) + + # Predicted shape is aligned on the end of current day + jump + c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()], + predicted_shape - predicted_shape[1] + predicted_delta + + ifelse(predict_from>=2, + data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) ) }, - predictShape = function(data, today, memory, horizon, ...) + predictShape = function(data, today, memory, predict_from, horizon, ...) NULL #empty default implementation: to implement in inherited classes , getParameters = function()