pkg/R/J_Neighbors.R

   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$weights[filter]
  20
  21     if (is.na(indices[1]))
  22         return (NA)
  23
  24     gaps = sapply(indices, function(i) {
  25         if (predict_from >= 2)
  26             data$getSerie(i)[predict_from] - data$getSerie(i)[predict_from-1]
  27         else
  28             head(data$getSerie(i),1) - tail(data$getSerie(i-1),1)
  29     })
  30     scal_product = weights * gaps
  31     norm_fact = sum( weights[!is.na(scal_product)] )
  32     sum(scal_product, na.rm=TRUE) / norm_fact
  33 }