Commit | Line | Data |
---|---|---|
af3b84f4 | 1 | #' Compute forecast |
3d69ff21 | 2 | #' |
af3b84f4 | 3 | #' Predict time-series curves for the selected days indices (lines in data). |
3d69ff21 | 4 | #' |
2057c793 BA |
5 | #' @param data Object of type \code{Data}, output of \code{getData()} |
6 | #' @param indices Indices where to forecast (the day after); integers relative to the | |
7 | #' beginning of data, or (convertible to) Date objects | |
e030a6e3 | 8 | #' @param forecaster Name of the main forcaster |
3d69ff21 BA |
9 | #' \itemize{ |
10 | #' \item Persistence : use values of last (similar, next) day | |
e030a6e3 | 11 | #' \item Neighbors : use values from the k closest neighbors' tomorrows |
3d69ff21 | 12 | #' \item Average : global average of all the (similar) "tomorrow of past" |
e030a6e3 | 13 | #' \item Zero : just output 0 (benchmarking purpose) |
3d69ff21 | 14 | #' } |
e030a6e3 | 15 | #' @param pjump How to predict the jump at the interface between two days ? |
3d69ff21 BA |
16 | #' \itemize{ |
17 | #' \item Persistence : use last (similar) day values | |
e030a6e3 | 18 | #' \item Neighbors: re-use the weights optimized in corresponding forecaster |
3d69ff21 BA |
19 | #' \item Zero: just output 0 (no adjustment) |
20 | #' } | |
e030a6e3 BA |
21 | #' @param memory Data depth (in days) to be used for prediction |
22 | #' @param horizon Number of time steps to predict | |
ee8b1b4e | 23 | #' @param ncores Number of cores for parallel execution (1 to disable) |
2057c793 | 24 | #' @param ... Additional parameters for the forecasting models |
3d69ff21 | 25 | #' |
a66a84b5 | 26 | #' @return An object of class Forecast |
3d69ff21 BA |
27 | #' |
28 | #' @examples | |
44a9990b BA |
29 | #' ts_data = system.file("extdata","pm10_mesures_H_loc.csv",package="talweg") |
30 | #' exo_data = system.file("extdata","meteo_extra_noNAs.csv",package="talweg") | |
445e7bbc | 31 | #' data = getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7) |
99f83c9a | 32 | #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12) |
3d69ff21 BA |
33 | #' \dontrun{#Sketch for real-time mode: |
34 | #' data = new("Data", ...) | |
e030a6e3 | 35 | #' forecaster = new(..., data=data) |
3d69ff21 BA |
36 | #' repeat { |
37 | #' data$append(some_new_data) | |
e030a6e3 | 38 | #' pred = forecaster$predict(data$getSize(), ...) |
3d69ff21 BA |
39 | #' #do_something_with_pred |
40 | #' }} | |
41 | #' @export | |
25b75559 | 42 | computeForecast = function(data, indices, forecaster, pjump, |
ee8b1b4e | 43 | memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...) |
3d69ff21 | 44 | { |
e030a6e3 | 45 | # (basic) Arguments sanity checks |
3d69ff21 | 46 | horizon = as.integer(horizon)[1] |
72b9c501 | 47 | if (horizon<=0 || horizon>length(data$getCenteredSerie(1))) |
3d69ff21 | 48 | stop("Horizon too short or too long") |
98e958ca | 49 | integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data)) |
a66a84b5 | 50 | if (any(integer_indices<=0 | integer_indices>data$getSize())) |
3d69ff21 | 51 | stop("Indices out of range") |
a66a84b5 BA |
52 | if (!is.character(forecaster) || !is.character(pjump)) |
53 | stop("forecaster (name) and pjump (function) should be of class character") | |
3d69ff21 | 54 | |
98e958ca | 55 | pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) ) |
72b9c501 BA |
56 | forecaster_class_name = getFromNamespace( |
57 | paste(forecaster,"Forecaster",sep=""), "talweg") | |
98e958ca BA |
58 | forecaster = forecaster_class_name$new( #.pjump = |
59 | getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")) | |
5e838b3e | 60 | |
ee8b1b4e | 61 | if (ncores > 1 && requireNamespace("parallel",quietly=TRUE)) |
a866acb3 | 62 | { |
ee8b1b4e | 63 | p <- parallel::mclapply(seq_along(integer_indices), function(i) { |
a866acb3 | 64 | list( |
72b9c501 BA |
65 | "forecast" = forecaster$predictSerie( |
66 | data, integer_indices[i], memory, horizon, ...), | |
a866acb3 BA |
67 | "params"= forecaster$getParameters(), |
68 | "index" = integer_indices[i] ) | |
ee8b1b4e | 69 | }, mc.cores=ncores) |
a866acb3 BA |
70 | } |
71 | else | |
72 | { | |
ee8b1b4e | 73 | p <- lapply(seq_along(integer_indices), function(i) { |
a866acb3 | 74 | list( |
72b9c501 BA |
75 | "forecast" = forecaster$predictSerie( |
76 | data, integer_indices[i], memory, horizon, ...), | |
a866acb3 BA |
77 | "params"= forecaster$getParameters(), |
78 | "index" = integer_indices[i] ) | |
79 | }) | |
80 | } | |
5e838b3e | 81 | |
ee8b1b4e BA |
82 | # TODO: find a way to fill pred in //... |
83 | for (i in seq_along(integer_indices)) | |
84 | { | |
85 | pred$append( | |
72b9c501 BA |
86 | forecast = p[[i]]$forecast, |
87 | params = p[[i]]$params, | |
88 | index_in_data = p[[i]]$index | |
ee8b1b4e BA |
89 | ) |
90 | } | |
25b75559 | 91 | pred |
3d69ff21 | 92 | } |