pkg/R/computeForecast.R

   1 #' Compute forecast
   2 #'
   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.
   6 #'
   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>
  11 #' \itemize{
  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)
  16 #' }
  17 #' @param pjump Function to predict the jump at the interface between two days;
  18 #'   more details: ?J_<functionname>
  19 #' \itemize{
  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)
  23 #' }
  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.
  28 #'
  29 #' @return An object of class Forecast
  30 #'
  31 #' @examples
  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)
  37 #' \dontrun{#Sketch for real-time mode:
  38 #' data <- Data$new()
  39 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
  40 #' repeat {
  41 #'   # In the morning 7am+ or afternoon 1pm+:
  42 #'   data$append(
  43 #'     times_from_H+1_yersteday_to_Hnow,
  44 #'     PM10_values_of_last_24h,
  45 #'     exogenous_measures_of_last_24h,
  46 #'     exogenous_predictions_for_next_24h)
  47 #'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
  48 #'   #do_something_with_pred
  49 #' }}
  50 #' @export
  51 computeForecast = function(data, indices, forecaster, pjump,
  52     memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
  53 {
  54     # (basic) Arguments sanity checks
  55     horizon = as.integer(horizon)[1]
  56     if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
  57         stop("Horizon too short or too long")
  58     integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
  59     if (any(integer_indices<=0 | integer_indices>data$getSize()))
  60         stop("Indices out of range")
  61     if (!is.character(forecaster) || !is.character(pjump))
  62         stop("forecaster (name) and pjump (function) should be of class character")
  63
  64     pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
  65     forecaster_class_name = getFromNamespace(
  66         paste(forecaster,"Forecaster",sep=""), "talweg")
  67     forecaster = forecaster_class_name$new( #.pjump =
  68         getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  69
  70     if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
  71     {
  72         p <- parallel::mclapply(seq_along(integer_indices), function(i) {
  73             list(
  74                 "forecast" = forecaster$predictSerie(
  75                     data, integer_indices[i], memory, horizon, ...),
  76                 "params"= forecaster$getParameters(),
  77                 "index" = integer_indices[i] )
  78             }, mc.cores=ncores)
  79     }
  80     else
  81     {
  82         p <- lapply(seq_along(integer_indices), function(i) {
  83             list(
  84                 "forecast" = forecaster$predictSerie(
  85                     data, integer_indices[i], memory, horizon, ...),
  86                 "params"= forecaster$getParameters(),
  87                 "index" = integer_indices[i] )
  88             })
  89     }
  90
  91     # TODO: find a way to fill pred in //...
  92     for (i in seq_along(integer_indices))
  93     {
  94         pred$append(
  95             forecast = p[[i]]$forecast,
  96             params = p[[i]]$params,
  97             index_in_data = p[[i]]$index
  98         )
  99     }
 100     pred
 101 }