#' Obtain jump forecast by the Neighbors method
#'
#' @inheritParams getForecast
#' @inheritParams getZeroJumpPredict
getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
	first_day = max(1, today-memory)
	filter = params$indices >= first_day
	indices = params$indices[filter]
	weights = params$weights[filter]
	if (any(is.na(weights) | is.na(indices)))
		return (NA)

	gaps = sapply(indices, function(i) {
		data$getSerie(i+1)[1] - tail(data$getSerie(i), 1)
	})
	scal_product = weights * gaps
	norm_fact = sum( weights[!is.na(scal_product)] )
	sum(scal_product, na.rm=TRUE) / norm_fact
}