3 #' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
4 #' This function just runs a loop over all requested indices, and stores the individual
5 #' forecasts into a list which is then turned into a Forecast object.
7 #' @param data Object of class Data, output of \code{getData()}.
8 #' @param indices Indices where to forecast (the day after); integers relative to the
9 #' beginning of data, or (convertible to) Date objects.
10 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
12 #' \item Persistence : use last (similar, next) day
13 #' \item Neighbors : weighted tomorrows of similar days
14 #' \item Average : average tomorrow of all same day-in-week
15 #' \item Zero : just output 0 (benchmarking purpose)
17 #' @param pjump Function to predict the jump at the interface between two days;
18 #' more details: ?J_<functionname>
20 #' \item Persistence : use last (similar, next) day
21 #' \item Neighbors: re-use the weights from F_Neighbors
22 #' \item Zero: just output 0 (no adjustment)
24 #' @param memory Data depth (in days) to be used for prediction.
25 #' @param horizon Number of time steps to predict.
26 #' @param ncores Number of cores for parallel execution (1 to disable).
27 #' @param ... Additional parameters for the forecasting models.
29 #' @return An object of class Forecast
32 #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
33 #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
34 #' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
35 #' predict_at=7, limit=200)
36 #' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
37 #' memory=50, horizon=12, ncores=1)
38 #' \dontrun{#Sketch for real-time mode:
40 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
42 #' # In the morning 7am+ or afternoon 1pm+:
44 #' times_from_H+1_yersteday_to_Hnow,
45 #' PM10_values_of_last_24h,
46 #' exogenous_measures_of_last_24h,
47 #' exogenous_predictions_for_next_24h)
48 #' pred <- forecaster$predictSerie(data, data$getSize(), ...)
49 #' #do_something_with_pred
52 computeForecast = function(data, indices, forecaster, pjump,
53 memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
55 # (basic) Arguments sanity checks
56 horizon = as.integer(horizon)[1]
57 if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
58 stop("Horizon too short or too long")
59 integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
60 if (any(integer_indices<=0 | integer_indices>data$getSize()))
61 stop("Indices out of range")
62 if (!is.character(forecaster) || !is.character(pjump))
63 stop("forecaster (name) and pjump (function) should be of class character")
65 pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
66 forecaster_class_name = getFromNamespace(
67 paste(forecaster,"Forecaster",sep=""), "talweg")
68 forecaster = forecaster_class_name$new( #.pjump =
69 getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
71 if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
73 p <- parallel::mclapply(seq_along(integer_indices), function(i) {
75 "forecast" = forecaster$predictSerie(
76 data, integer_indices[i], memory, horizon, ...),
77 "params"= forecaster$getParameters(),
78 "index" = integer_indices[i] )
83 p <- lapply(seq_along(integer_indices), function(i) {
85 "forecast" = forecaster$predictSerie(
86 data, integer_indices[i], memory, horizon, ...),
87 "params"= forecaster$getParameters(),
88 "index" = integer_indices[i] )
92 # TODO: find a way to fill pred in //...
93 for (i in seq_along(integer_indices))
96 forecast = p[[i]]$forecast,
97 params = p[[i]]$params,
98 index_in_data = p[[i]]$index