#'
#' Forecaster (abstract class, implemented by all forecasters)
#'
-#' @field params List of computed parameters, for post-run analysis (dev)
-#' @field data Dataset, object of class Data
-#' @field pjump Function: how to predict the jump at day interface ?
-#'
#' @docType class
#' @importFrom R6 R6Class
+#'
+#' @field .params List of computed parameters, for post-run analysis (dev)
+#' @field .pjump Function: how to predict the jump at day interface ?
+#'
+#' @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 \code{memory} days in the past.}
+#' \item{\code{getParameters()}}{
+#' Return (internal) parameters.}}
Forecaster = R6::R6Class("Forecaster",
private = list(
- .params = "list",
- .data = "Data",
- .pjump = "function"
+ .params = list(),
+ .pjump = NULL
),
public = list(
- initialize = function(data, pjump)
- initialize(self, private, data, pjump)
- ,
- predictSerie = function(today, memory, horizon, ...)
- predictSerie(private, today, memory, horizon, ...)
- ,
- predictShape = function(today, memory, horizon, ...)
- predictShape(private, today, memory, horizon, ...)
+ initialize = function(pjump)
+ {
+ private$.pjump <- pjump
+ invisible(self)
+ },
+ predictSerie = function(data, today, memory, horizon, ...)
+ {
+ # Parameters (potentially) computed during shape prediction stage
+ predicted_shape = self$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$getSerie(today),1)-predicted_shape[1]+predicted_delta
+ },
+ predictShape = function(data, today, memory, horizon, ...)
+ NULL #empty default implementation: to implement in inherited classes
,
getParameters = function()
- getParameters(private)
+ private$.params
)
)
-
-#' Initialize (generic) Forecaster object
-#'
-#' @param o Object of class Forecaster
-#' @param private List of private members in o
-#' @param data Object of class Data
-#' @param pjump Function to predict jump
-initialize = function(o, private, data, pjump)
-{
- .params <<- list()
- .data <<- data
- .pjump <<- pjump
- invisible(o)
-}
-
-#' Obtain a new forecasted time-serie
-#'
-#' @inheritParams initialize
-#' @param today Index for current prediction
-#' @param memory Depth in data (in days)
-#' @param horizon Number of hours to forecast
-predictSerie = function(private, today, memory, horizon, ...)
-{
- # Parameters (potentially) computed during shape prediction stage
- predicted_shape = predictShape(today, memory, horizon, ...)
- predicted_delta = private$.pjump(private$.data, today, memory, horizon, params, ...)
- # Predicted shape is aligned it on the end of current day + jump
- predicted_shape + tail(private$.data$getSerie(today),1) - predicted_shape[1] + predicted_delta
-}
-
-#' Shape prediction (centered curve)
-#'
-#' @inheritParams predictSerie
-predictShape = function(private, today, memory, horizon, ...)
- #empty default implementation: to implement in inherited classes
-
-#' Get parameters list
-#'
-#' @inheritParams initialize
-getParameters = function(private)
- private$.params