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, horizon, params, ...)
  14 {
  15     first_day = max(1, today-memory)
  16     filter = (params$indices >= first_day)
  17     indices = params$indices[filter]
  18     weights = params$weights[filter]
  19
  20     gaps = sapply(indices, function(i) {
  21         head( data$getSerie(i+1),1 ) - tail( data$getSerie(i),1 )
  22     })
  23     scal_product = weights * gaps
  24     norm_fact = sum( weights[!is.na(scal_product)] )
  25     sum(scal_product, na.rm=TRUE) / norm_fact
  26 }