| 1 | #' Compute forecast |
| 2 | #' |
| 3 | #' 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 ncores Number of cores for parallel execution (1 to disable) |
| 23 | #' @param ... Additional parameters for the forecasting models; |
| 24 | #' In particular, realtime=TRUE to use predictions instead of measurements |
| 25 | #' |
| 26 | #' @return An object of class Forecast |
| 27 | #' |
| 28 | #' @examples |
| 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") |
| 31 | #' data = getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7) |
| 32 | #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12) |
| 33 | #' \dontrun{#Sketch for real-time mode: |
| 34 | #' data = new("Data", ...) |
| 35 | #' forecaster = new(..., data=data) |
| 36 | #' repeat { |
| 37 | #' data$append(some_new_data) |
| 38 | #' pred = forecaster$predict(data$getSize(), ...) |
| 39 | #' #do_something_with_pred |
| 40 | #' }} |
| 41 | #' @export |
| 42 | computeForecast = function(data, indices, forecaster, pjump, |
| 43 | memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...) |
| 44 | { |
| 45 | # (basic) Arguments sanity checks |
| 46 | horizon = as.integer(horizon)[1] |
| 47 | if (horizon<=0 || horizon>length(data$getCenteredSerie(1))) |
| 48 | stop("Horizon too short or too long") |
| 49 | integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data)) |
| 50 | if (any(integer_indices<=0 | integer_indices>data$getSize())) |
| 51 | stop("Indices out of range") |
| 52 | if (!is.character(forecaster) || !is.character(pjump)) |
| 53 | stop("forecaster (name) and pjump (function) should be of class character") |
| 54 | |
| 55 | pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) ) |
| 56 | forecaster_class_name = getFromNamespace( |
| 57 | paste(forecaster,"Forecaster",sep=""), "talweg") |
| 58 | forecaster = forecaster_class_name$new( #.pjump = |
| 59 | getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")) |
| 60 | |
| 61 | if (ncores > 1 && requireNamespace("parallel",quietly=TRUE)) |
| 62 | { |
| 63 | p <- parallel::mclapply(seq_along(integer_indices), function(i) { |
| 64 | list( |
| 65 | "forecast" = forecaster$predictSerie( |
| 66 | data, integer_indices[i], memory, horizon, ...), |
| 67 | "params"= forecaster$getParameters(), |
| 68 | "index" = integer_indices[i] ) |
| 69 | }, mc.cores=ncores) |
| 70 | } |
| 71 | else |
| 72 | { |
| 73 | p <- lapply(seq_along(integer_indices), function(i) { |
| 74 | list( |
| 75 | "forecast" = forecaster$predictSerie( |
| 76 | data, integer_indices[i], memory, horizon, ...), |
| 77 | "params"= forecaster$getParameters(), |
| 78 | "index" = integer_indices[i] ) |
| 79 | }) |
| 80 | } |
| 81 | |
| 82 | # TODO: find a way to fill pred in //... |
| 83 | for (i in seq_along(integer_indices)) |
| 84 | { |
| 85 | pred$append( |
| 86 | forecast = p[[i]]$forecast, |
| 87 | params = p[[i]]$params, |
| 88 | index_in_data = p[[i]]$index |
| 89 | ) |
| 90 | } |
| 91 | pred |
| 92 | } |