#' Forecaster
#'
#' Forecaster (abstract class, implemented by all forecasters)
#'
#' @docType class
#' @importFrom R6 R6Class
#'
#' @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 ?
#'
#' @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 = NULL,
		.pjump = NULL
	),
	public = list(
		initialize = function(data, pjump)
		{
			private$.data <- data
			private$.pjump <- pjump
			invisible(self)
		},
		predictSerie = function(today, memory, horizon, ...)
		{
			# Parameters (potentially) computed during shape prediction stage
			predicted_shape = self$predictShape(today, memory, horizon, ...)
			predicted_delta = private$.pjump(
				private$.data, today, memory, horizon, private$.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
		},
		predictShape = function(today, memory, horizon, ...)
			NULL #empty default implementation: to implement in inherited classes
		,
		getParameters = function()
			private$.params
	)
)