#' 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.
+#' serie, and then calls the "jump prediction" function if it's provided -- see "field"
+#' section -- to adjust it based on the last observed values. The main method in derived
+#' forecasters is \code{predictShape()}; see 'Methods' section.
#'
#' @usage # Forecaster$new(pjump) #warning: predictShape() is unimplemented
#'
#' @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.
+#' \item data: object output of \code{getData()},
+#' \item today: index of the current day in data (known until predict_from-1),
+#' \item memory: number of days to use in the past (including today),
+#' \item predict_from: first time step to predict (in [1,24])
+#' \item horizon: last time step to predict (in [predict_from,24]),
+#' \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.}
-#' \item{\code{predictShape(today,memory,horizon,...)}}{
-#' Predict a new shape of \code{horizon} values at day index \code{today} using
+#' Initialize a Forecaster object with a Data object and a jump prediction function,
+#' or NULL if \code{predictShape()} returns an adjusted curve.}
+#' \item{\code{predictSerie(data,today,memory,predict_from,horizon,...)}}{
+#' Predict the next curve (at index today) from predict_from to horizon (hours), using
#' \code{memory} days in the past.}
+#' \item{\code{predictShape(data,today,memory,predict_from,horizon,...)}}{
+#' Predict the shape of the next curve (at index today) from predict_from to horizon
+#' (hours), using \code{memory} days in the past.}
#' \item{\code{getParameters()}}{
#' Return (internal) parameters.}
#' }
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$predictShape(data,today,memory,predict_from,horizon,...)
+ predicted_delta <-
+ if (is.null(private$.pjump))
+ NULL
+ else
+ private$.pjump(data,today,memory,predict_from,horizon,private$.params,...)
+
+ # Predicted shape is aligned on the end of current day + jump (if jump!=NULL)
+ c( data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()],
+ predicted_shape + ifelse( is.null(private$.pjump),
+ 0,
+ predicted_delta - predicted_shape[1] +
+ 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()