getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
first_day = max(1, today-memory)
- filter = params$indices >= first_day
+ filter = (params$indices >= first_day)
indices = params$indices[filter]
weights = params$weights[filter]
return (NA)
gaps = sapply(indices, function(i) {
- 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)] )