Commit | Line | Data |
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af3b84f4 | 1 | #' Compute forecast |
3d69ff21 | 2 | #' |
102bcfda BA |
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. | |
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> | |
3d69ff21 | 11 | #' \itemize{ |
e169b5d5 BA |
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 | |
e030a6e3 | 15 | #' \item Zero : just output 0 (benchmarking purpose) |
3d69ff21 | 16 | #' } |
e169b5d5 BA |
17 | #' @param pjump Function to predict the jump at the interface between two days; |
18 | #' more details: ?J_<functionname> | |
3d69ff21 | 19 | #' \itemize{ |
e169b5d5 BA |
20 | #' \item Persistence : use last (similar, next) day |
21 | #' \item Neighbors: re-use the weights from F_Neighbors | |
3d69ff21 BA |
22 | #' \item Zero: just output 0 (no adjustment) |
23 | #' } | |
e169b5d5 BA |
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. | |
3d69ff21 | 28 | #' |
a66a84b5 | 29 | #' @return An object of class Forecast |
3d69ff21 BA |
30 | #' |
31 | #' @examples | |
e169b5d5 BA |
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", predict_at=7) | |
35 | #' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero", | |
36 | #' memory=500, horizon=12, ncores=1) | |
3d69ff21 | 37 | #' \dontrun{#Sketch for real-time mode: |
e169b5d5 | 38 | #' data <- Data$new() |
e169b5d5 | 39 | #' forecaster <- MyForecaster$new(myJumpPredictFunc) |
3d69ff21 | 40 | #' repeat { |
e169b5d5 | 41 | #' # In the morning 7am+ or afternoon 1pm+: |
c1be9898 | 42 | #' data$append( |
e169b5d5 BA |
43 | #' times_from_H+1_yersteday_to_Hnow, |
44 | #' PM10_values_of_last_24h, | |
c1be9898 BA |
45 | #' exogenous_measures_of_last_24h, |
46 | #' exogenous_predictions_for_next_24h) | |
102bcfda | 47 | #' pred <- forecaster$predictSerie(data, data$getSize(), ...) |
3d69ff21 BA |
48 | #' #do_something_with_pred |
49 | #' }} | |
50 | #' @export | |
25b75559 | 51 | computeForecast = function(data, indices, forecaster, pjump, |
ee8b1b4e | 52 | memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...) |
3d69ff21 | 53 | { |
e030a6e3 | 54 | # (basic) Arguments sanity checks |
3d69ff21 | 55 | horizon = as.integer(horizon)[1] |
72b9c501 | 56 | if (horizon<=0 || horizon>length(data$getCenteredSerie(1))) |
3d69ff21 | 57 | stop("Horizon too short or too long") |
98e958ca | 58 | integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data)) |
a66a84b5 | 59 | if (any(integer_indices<=0 | integer_indices>data$getSize())) |
3d69ff21 | 60 | stop("Indices out of range") |
a66a84b5 BA |
61 | if (!is.character(forecaster) || !is.character(pjump)) |
62 | stop("forecaster (name) and pjump (function) should be of class character") | |
3d69ff21 | 63 | |
98e958ca | 64 | pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) ) |
72b9c501 BA |
65 | forecaster_class_name = getFromNamespace( |
66 | paste(forecaster,"Forecaster",sep=""), "talweg") | |
98e958ca BA |
67 | forecaster = forecaster_class_name$new( #.pjump = |
68 | getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")) | |
5e838b3e | 69 | |
ee8b1b4e | 70 | if (ncores > 1 && requireNamespace("parallel",quietly=TRUE)) |
a866acb3 | 71 | { |
ee8b1b4e | 72 | p <- parallel::mclapply(seq_along(integer_indices), function(i) { |
a866acb3 | 73 | list( |
72b9c501 BA |
74 | "forecast" = forecaster$predictSerie( |
75 | data, integer_indices[i], memory, horizon, ...), | |
a866acb3 BA |
76 | "params"= forecaster$getParameters(), |
77 | "index" = integer_indices[i] ) | |
ee8b1b4e | 78 | }, mc.cores=ncores) |
a866acb3 BA |
79 | } |
80 | else | |
81 | { | |
ee8b1b4e | 82 | p <- lapply(seq_along(integer_indices), function(i) { |
a866acb3 | 83 | list( |
72b9c501 BA |
84 | "forecast" = forecaster$predictSerie( |
85 | data, integer_indices[i], memory, horizon, ...), | |
a866acb3 BA |
86 | "params"= forecaster$getParameters(), |
87 | "index" = integer_indices[i] ) | |
88 | }) | |
89 | } | |
5e838b3e | 90 | |
ee8b1b4e BA |
91 | # TODO: find a way to fill pred in //... |
92 | for (i in seq_along(integer_indices)) | |
93 | { | |
94 | pred$append( | |
72b9c501 BA |
95 | forecast = p[[i]]$forecast, |
96 | params = p[[i]]$params, | |
97 | index_in_data = p[[i]]$index | |
ee8b1b4e BA |
98 | ) |
99 | } | |
25b75559 | 100 | pred |
3d69ff21 | 101 | } |