pkg/R/computeForecast.R

   1 #' @title get Forecast
   2 #'
   3 #' @description 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 ... Additional parameters for the forecasting models
  23 #'
  24 #' @return A list with the following items
  25 #' \itemize{
  26 #'   \item serie: forecasted serie
  27 #'   \item params: corresponding list of parameters (weights, neighbors...)
  28 #'   \item index: corresponding index in data object
  29 #' }
  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 = "Europe/Paris",
  35 #'   working_tz="Europe/Paris", predict_at=7)
  36 #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
  37 #' \dontrun{#Sketch for real-time mode:
  38 #' data = new("Data", ...)
  39 #' forecaster = new(..., data=data)
  40 #' repeat {
  41 #'   data$append(some_new_data)
  42 #'   pred = forecaster$predict(data$getSize(), ...)
  43 #'   #do_something_with_pred
  44 #' }}
  45 #' @export
  46 computeForecast = function(data, indices, forecaster, pjump,
  47     memory=Inf, horizon=data$getStdHorizon(), ...)
  48 {
  49     # (basic) Arguments sanity checks
  50     horizon = as.integer(horizon)[1]
  51     if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
  52         stop("Horizon too short or too long")
  53     indices = sapply( seq_along(indices), function(i) dateIndexToInteger(indices[i], data) )
  54     if (any(indices<=0 | indices>data$getSize()))
  55         stop("Indices out of range")
  56     indices = sapply(indices, dateIndexToInteger, data)
  57     if (!is.character(forecaster))
  58         stop("forecaster (name) should be of class character") #pjump could be NULL
  59
  60     pred = Forecast$new()
  61     forecaster_class_name = getFromNamespace(paste(forecaster,"Forecaster",sep=""), "talweg")
  62     forecaster = forecaster_class_name$new(data=data,
  63         pjump = getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  64     for (today in indices)
  65     {
  66         pred$append(
  67             new_serie = forecaster$predictSerie(today, memory, horizon, ...),
  68             new_params = forecaster$getParameters(),
  69             new_index = today
  70         )
  71     }
  72     pred
  73 }