Commit | Line | Data |
---|---|---|
af3b84f4 | 1 | #' Compute forecast |
3d69ff21 | 2 | #' |
4f3fdbb8 BA |
3 | #' Predict time-series curves ("today" from predict_from to horizon) at the selected days |
4 | #' indices ("today" from 1am to predict_from-1). This function just runs a loop over all | |
5 | #' requested indices, and stores the individual forecasts into a Forecast object. | |
c4c329f6 | 6 | #' |
102bcfda | 7 | #' @param data Object of class Data, output of \code{getData()}. |
2057c793 | 8 | #' @param indices Indices where to forecast (the day after); integers relative to the |
e169b5d5 BA |
9 | #' beginning of data, or (convertible to) Date objects. |
10 | #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername> | |
4f3fdbb8 BA |
11 | #' \itemize{ |
12 | #' \item Persistence : use last (similar) day | |
13 | #' \item Neighbors : weighted similar days | |
14 | #' \item Average : average curve of all same day-in-week | |
15 | #' \item Zero : just output 0 (benchmarking purpose) | |
16 | #' } | |
e169b5d5 BA |
17 | #' @param pjump Function to predict the jump at the interface between two days; |
18 | #' more details: ?J_<functionname> | |
4f3fdbb8 BA |
19 | #' \itemize{ |
20 | #' \item Persistence : use last (similar) day | |
21 | #' \item Neighbors: re-use the weights from F_Neighbors | |
8f5671db BA |
22 | #' \item LastValue: start serie with last observed value |
23 | #' \item Zero: no adjustment => use shape prediction only | |
4f3fdbb8 | 24 | #' } |
4f3fdbb8 | 25 | #' @param predict_from First time step to predict. |
e169b5d5 | 26 | #' @param memory Data depth (in days) to be used for prediction. |
4f3fdbb8 | 27 | #' @param horizon Last time step to predict. |
e169b5d5 | 28 | #' @param ncores Number of cores for parallel execution (1 to disable). |
8ab64202 | 29 | #' @param verbose TRUE to print basic traces (runs beginnings) |
e169b5d5 | 30 | #' @param ... Additional parameters for the forecasting models. |
3d69ff21 | 31 | #' |
a66a84b5 | 32 | #' @return An object of class Forecast |
3d69ff21 BA |
33 | #' |
34 | #' @examples | |
e169b5d5 BA |
35 | #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg") |
36 | #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg") | |
4f3fdbb8 | 37 | #' data <- getData(ts_data, exo_data, limit=200) |
8f5671db | 38 | #' pred <- computeForecast(data, 100:130, "Persistence", "LastValue", |
4f3fdbb8 BA |
39 | #' predict_from=8, memory=50, horizon=12, ncores=1) |
40 | #' \dontrun{ | |
41 | #' #Sketch for real-time mode: | |
e169b5d5 | 42 | #' data <- Data$new() |
e169b5d5 | 43 | #' forecaster <- MyForecaster$new(myJumpPredictFunc) |
3d69ff21 | 44 | #' repeat { |
4f3fdbb8 | 45 | #' # As soon as daily predictions are available: |
c1be9898 | 46 | #' data$append( |
4f3fdbb8 BA |
47 | #' level_hat=predicted_level, |
48 | #' exo_hat=predicted_exogenous) | |
49 | #' # When a day ends: | |
50 | #' data$append( | |
51 | #' level=observed_level, | |
52 | #' exo=observed_exogenous) | |
53 | #' # And, at every hour: | |
54 | #' data$append( | |
55 | #' time=current_hour, | |
56 | #' value=current_PM10) | |
57 | #' # Finally, a bit before predict_from hour: | |
102bcfda | 58 | #' pred <- forecaster$predictSerie(data, data$getSize(), ...) |
3d69ff21 | 59 | #' #do_something_with_pred |
4f3fdbb8 | 60 | #' } } |
3d69ff21 | 61 | #' @export |
d2ab47a7 | 62 | computeForecast = function(data, indices, forecaster, pjump, predict_from, |
8ab64202 | 63 | memory=Inf, horizon=length(data$getSerie(1)), ncores=3, verbose=FALSE, ...) |
3d69ff21 | 64 | { |
e030a6e3 | 65 | # (basic) Arguments sanity checks |
d2ab47a7 BA |
66 | predict_from = as.integer(predict_from)[1] |
67 | if (! predict_from %in% 1:length(data$getSerie(1))) | |
68 | stop("predict_from in [1,24] (hours)") | |
638f27f4 BA |
69 | if (hasArg("opera") && !list(...)$opera && memory < Inf) |
70 | memory <- Inf #finite memory in training mode makes no sense | |
3d69ff21 | 71 | horizon = as.integer(horizon)[1] |
d2ab47a7 BA |
72 | if (horizon<=predict_from || horizon>length(data$getSerie(1))) |
73 | stop("Horizon in [predict_from+1,24] (hours)") | |
98e958ca | 74 | integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data)) |
a66a84b5 | 75 | if (any(integer_indices<=0 | integer_indices>data$getSize())) |
3d69ff21 | 76 | stop("Indices out of range") |
4f3fdbb8 BA |
77 | if (!is.character(forecaster)) |
78 | stop("forecaster (name): character") | |
8f5671db BA |
79 | if (!is.character(pjump)) |
80 | stop("pjump (function): character") | |
3d69ff21 | 81 | |
98e958ca | 82 | pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) ) |
72b9c501 BA |
83 | forecaster_class_name = getFromNamespace( |
84 | paste(forecaster,"Forecaster",sep=""), "talweg") | |
4f3fdbb8 | 85 | |
8f5671db | 86 | pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg") |
4f3fdbb8 | 87 | forecaster = forecaster_class_name$new(pjump) |
5e838b3e | 88 | |
1e8327df | 89 | computeOneForecast <- function(i) |
a866acb3 | 90 | { |
8ab64202 BA |
91 | if (verbose) |
92 | print(paste("Index",i)) | |
1e8327df BA |
93 | list( |
94 | "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...), | |
95 | "params" = forecaster$getParameters(), | |
96 | "index" = i ) | |
a866acb3 | 97 | } |
5e838b3e | 98 | |
1e8327df BA |
99 | p <- |
100 | if (ncores > 1 && requireNamespace("parallel",quietly=TRUE)) | |
101 | parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores) | |
102 | else | |
103 | lapply(integer_indices, computeOneForecast) | |
104 | ||
ee8b1b4e BA |
105 | # TODO: find a way to fill pred in //... |
106 | for (i in seq_along(integer_indices)) | |
107 | { | |
108 | pred$append( | |
72b9c501 BA |
109 | forecast = p[[i]]$forecast, |
110 | params = p[[i]]$params, | |
111 | index_in_data = p[[i]]$index | |
ee8b1b4e BA |
112 | ) |
113 | } | |
25b75559 | 114 | pred |
3d69ff21 | 115 | } |