#' @inheritParams computeForecast
#' @inheritParams getZeroJumpPredict
#'
-#' @alias J_Neighbors
+#' @aliases J_Neighbors
#'
getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
indices = params$indices[filter]
weights = params$weights[filter]
- if (any(is.na(weights) | is.na(indices)))
- return (NA)
-
gaps = sapply(indices, function(i) {
- head( data$getSerie(i+1), 1) - tail( data$getSerie(i), 1)
+ head( data$getSerie(i+1),1 ) - tail( data$getSerie(i),1 )
})
scal_product = weights * gaps
norm_fact = sum( weights[!is.na(scal_product)] )