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

#' getNeighborsJumpPredict
#'
#' Apply optimized weights on gaps observed on selected neighbors.
#' This jump prediction method can only be used in conjunction with the Neighbors
#' Forecaster, because it makes use of the optimized parameters to re-apply the weights
#' on the jumps observed at days interfaces of the past neighbors.
#'
#' @inheritParams computeForecast
#' @inheritParams getZeroJumpPredict
#'
#' @aliases J_Neighbors
#'
getNeighborsJumpPredict = function(data, today, memory, predict_from, horizon,
	params, ...)
{
	first_day = max(1, today-memory)
	filter = (params$indices >= first_day)
	indices = params$indices[filter]
	weights = params$weights[filter]

	gaps = sapply(indices, function(i) {
		if (predict_from >= 2)
			data$getSerie(i)[predict_from] - data$getSerie(i)[predict_from-1]
		else
			head(data$getSerie(i),1) - tail(data$getSerie(i-1),1)
	})
	scal_product = weights * gaps
	norm_fact = sum( weights[!is.na(scal_product)] )
	sum(scal_product, na.rm=TRUE) / norm_fact
}
Commit	Line	Data
	1	#' getNeighborsJumpPredict
	2	#'
	3	#' Apply optimized weights on gaps observed on selected neighbors.
	4	#' This jump prediction method can only be used in conjunction with the Neighbors
	5	#' Forecaster, because it makes use of the optimized parameters to re-apply the weights
	6	#' on the jumps observed at days interfaces of the past neighbors.
	7	#'
	8	#' @inheritParams computeForecast
	9	#' @inheritParams getZeroJumpPredict
	10	#'
	11	#' @aliases J_Neighbors
	12	#'
	13	getNeighborsJumpPredict = function(data, today, memory, predict_from, horizon,
	14	params, ...)
	15	{
	16	first_day = max(1, today-memory)
	17	filter = (params$indices >= first_day)
	18	indices = params$indices[filter]
	19	weights = params$weights[filter]
	20
	21	gaps = sapply(indices, function(i) {
	22	if (predict_from >= 2)
	23	data$getSerie(i)[predict_from] - data$getSerie(i)[predict_from-1]
	24	else
	25	head(data$getSerie(i),1) - tail(data$getSerie(i-1),1)
	26	})
	27	scal_product = weights * gaps
	28	norm_fact = sum( weights[!is.na(scal_product)] )
	29	sum(scal_product, na.rm=TRUE) / norm_fact
	30	}