Commit | Line | Data |
---|---|---|
3a38473a BA |
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 | } |