fix bugs

[talweg.git] / pkg / R / Forecaster.R

diff --git a/pkg/R/Forecaster.R b/pkg/R/Forecaster.R
index 5258760..ef270b1 100644 (file)
--- a/pkg/R/Forecaster.R
+++ b/pkg/R/Forecaster.R
@@ -6,8 +6,9 @@
 #' 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
 #'
@@ -15,25 +16,26 @@
 #' @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
+#' \item{\code{initialize(pjump)}}{
+#'   Initialize a Forecaster object with a jump prediction function.}
+#' \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.}
 #' }
@@ -55,15 +57,20 @@ Forecaster = R6::R6Class("Forecaster",
                predictSerie = function(data, today, memory, predict_from, horizon, ...)
                {
                        # Parameters (potentially) computed during shape prediction stage
-                       predicted_shape = self$predictShape(data,today,memory,predict_from,horizon,...)
-                       predicted_delta = private$.pjump(data, today, memory, predict_from, horizon,
-                               private$.params, ...)
+                       predicted_shape <- self$predictShape(data,today,memory,predict_from,horizon,...)
+
+                       if (is.na(predicted_shape[1]))
+                               return (NA)
+
+                       predicted_delta <- private$.pjump(data, today, memory, predict_from,
+                                       horizon, private$.params, first_pred=predicted_shape[1], ...)
 
                        # 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$getSerie(today-1),1)) )
+                               (predicted_shape - predicted_shape[1]) + #shape with first_pred = 0
+                               ifelse(predict_from>=2, #last observed value
+                                       data$getSerie(today)[predict_from-1], tail(data$getSerie(today-1),1)) +
+                               predicted_delta ) #jump
                },
                predictShape = function(data, today, memory, predict_from, horizon, ...)
                        NULL #empty default implementation: to implement in inherited classes