use R6 class; remove data first and last elements --> simplify
[talweg.git] / pkg / R / computeForecast.R
1 #' @title get Forecast
2 #'
3 #' @description Predict time-series curves for the selected days indices (lines in data).
4 #'
5 #' @param data Dataset, object of type \code{Data} output of \code{getData}
6 #' @param indices Days indices where to forecast (the day after)
7 #' @param forecaster Name of the main forcaster
8 #' \itemize{
9 #' \item Persistence : use values of last (similar, next) day
10 #' \item Neighbors : use values from the k closest neighbors' tomorrows
11 #' \item Average : global average of all the (similar) "tomorrow of past"
12 #' \item Zero : just output 0 (benchmarking purpose)
13 #' }
14 #' @param pjump How to predict the jump at the interface between two days ?
15 #' \itemize{
16 #' \item Persistence : use last (similar) day values
17 #' \item Neighbors: re-use the weights optimized in corresponding forecaster
18 #' \item Zero: just output 0 (no adjustment)
19 #' }
20 #' @param memory Data depth (in days) to be used for prediction
21 #' @param horizon Number of time steps to predict
22 #' @param ... Additional parameters for the forecasting models
23 #'
24 #' @return An object of class Forecast
25 #'
26 #' @examples
27 #' ts_data = system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
28 #' exo_data = system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
29 #' data = getData(ts_data, exo_data, input_tz = "Europe/Paris",
30 #' working_tz="Europe/Paris", predict_at=7)
31 #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
32 #' \dontrun{#Sketch for real-time mode:
33 #' data = new("Data", ...)
34 #' forecaster = new(..., data=data)
35 #' repeat {
36 #' data$append(some_new_data)
37 #' pred = forecaster$predict(data$getSize(), ...)
38 #' #do_something_with_pred
39 #' }}
40 #' @export
41 computeForecast = function(data, indices, forecaster, pjump=NULL,
42 memory=Inf, horizon=data$getStdHorizon(), ...)
43 {
44 # (basic) Arguments sanity checks
45 horizon = as.integer(horizon)[1]
46 if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
47 stop("Horizon too short or too long")
48 indices = sapply( seq_along(indices), function(i) dateIndexToInteger(indices[i], data) )
49 if (any(indices<=0 | indices>data$getSize()))
50 stop("Indices out of range")
51 indices = sapply(indices, dateIndexToInteger, data)
52 if (!is.character(forecaster))
53 stop("forecaster (name) should be of class character") #pjump could be NULL
54
55 pred = list()
56 forecaster = new(paste(forecaster,"Forecaster",sep=""), data=data,
57 pjump =
58 if (is.null(pjump))
59 function() {}
60 else
61 getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
62 for (today in indices)
63 {
64 #pred$append(...) is slow; TODO: use R6 class
65 pred[[length(pred)+1]] = list(
66 "serie" = forecaster$predict(today, memory, horizon, ...),
67 "params" = forecaster$getParameters(),
68 "index" = today
69 )
70 }
71 new("Forecast",pred=pred)
72 }