#' @title get Forecast
#'
#' @description Predict time-series curves for the selected days indices (lines in data).
#'   Run the forecasting task described by \code{delta_forecaster_name} and
#'   \code{shape_forecaster_name} on data obtained with \code{getData}
#'
#' @param data Dataset, object of type \code{Data} output of \code{getData}
#' @param indices Days indices where to forecast (the day after)
#' @param memory Data depth (in days) to be used for prediction
#' @param horizon Number of time steps to predict
#' @param shape_forecaster_name Name of the shape forcaster
#' \itemize{
#'   \item Persistence : use values of last (similar, next) day
#'   \item Neighbors : use PM10 from the k closest neighbors' tomorrows
#'   \item Average : global average of all the (similar) "tomorrow of past"
#' }
#' @param delta_forecaster_name Name of the delta forecaster
#' \itemize{
#'   \item Persistence : use last (similar) day values
#'   \item Neighbors: re-use the weights optimized in corresponding shape forecaster
#'   \item Zero: just output 0 (no adjustment)
#' }
#' @param ... Additional parameters for the forecasting models
#'
#' @return An object of class Forecast
#'
#' @examples
#' data = getData(ts_data="data/pm10_mesures_H_loc.csv", exo_data="data/meteo_extra_noNAs.csv",
#'   input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at="07")
#' pred = getForecast(data, 2200:2230, Inf, 12, "Persistence", "Persistence")
#' \dontrun{#Sketch for real-time mode:
#' data = new("Data", ...)
#' repeat {
#'   data$append(some_new_data)
#'   pred = getForecast(data, ...)
#'   #do_something_with_pred
#' }}
#' @export
getForecast = function(data, indices, memory, horizon,
	shape_forecaster_name, delta_forecaster_name, ...)
{
	horizon = as.integer(horizon)[1]
	if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
		stop("Horizon too short or too long")
	indices = as.integer(indices)
	if (any(indices<=0 | indices>data$getSize()))
		stop("Indices out of range")
	indices = sapply(indices, dateIndexToInteger, data)

	#NOTE: some assymetry here...
	shape_forecaster = new(paste(shape_forecaster_name,"ShapeForecaster",sep=""), data=data)
	#A little bit strange, but match.fun() and get() fail
	delta_forecaster = getFromNamespace(
		paste("get",delta_forecaster_name,"DeltaForecast",sep=""), "talweg")

	pred = list()
	for (today in indices)
	{
		#NOTE: To estimate timing...
#		print(paste("Predict until index",today))

		#shape always predicted first (on centered series, no scaling taken into account),
		#with side-effect: optimize some parameters (h, weights, ...)
		predicted_shape = shape_forecaster$predict(today, memory, horizon, ...)
		#then, delta prediction can re-use some variables optimized previously (like neighbors infos)
		predicted_delta = delta_forecaster(data, today, memory, horizon,
			shape_forecaster$getParameters(), ...)

		#TODO: this way is faster than a call to append(); why ?
		pred[[length(pred)+1]] = list(
			# Predict shape and align it on end of current day
			serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1],
				predicted_delta, #add predicted jump
			params = shape_forecaster$getParameters(),
			index = today )
	}
	new("Forecast",pred=pred)
}