#'
#' @aliases J_Neighbors
#'
-getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
+getNeighborsJumpPredict = function(data, today, memory, predict_from, horizon,
+ params, ...)
{
first_day = max(1, today-memory)
filter = (params$indices >= first_day)
weights = params$weights[filter]
gaps = sapply(indices, function(i) {
- head( data$getSerie(i+1),1 ) - tail( data$getSerie(i),1 )
+ if (predict_from >= 2)
+ data$getSerie(i+1)[predict_from] - data$getSerie(i+1)[predict_from-1]
+ else
+ head(data$getSerie(i+1),1) - tail(data$getSerie(i),1)
})
scal_product = weights * gaps
norm_fact = sum( weights[!is.na(scal_product)] )