pkg/R/getForecast.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 An object of class Forecast
  25 #'
  26 #' @examples
  27 #' data = getData(ts_data="data/pm10_mesures_H_loc.csv", exo_data="data/meteo_extra_noNAs.csv",
  28 #'   input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at=7)
  29 #' pred = getForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
  30 #' \dontrun{#Sketch for real-time mode:
  31 #' data = new("Data", ...)
  32 #' forecaster = new(..., data=data)
  33 #' repeat {
  34 #'   data$append(some_new_data)
  35 #'   pred = forecaster$predict(data$getSize(), ...)
  36 #'   #do_something_with_pred
  37 #' }}
  38 #' @export
  39 getForecast = function(data, indices, forecaster, pjump=NULL,
  40     memory=Inf, horizon=data$getStdHorizon(), ...)
  41 {
  42     # (basic) Arguments sanity checks
  43     horizon = as.integer(horizon)[1]
  44     if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
  45         stop("Horizon too short or too long")
  46     indices = sapply( seq_along(indices), function(i) dateIndexToInteger(indices[i], data) )
  47     if (any(indices<=0 | indices>data$getSize()))
  48         stop("Indices out of range")
  49     indices = sapply(indices, dateIndexToInteger, data)
  50     if (!is.character(forecaster))
  51         stop("forecaster (name) should be of class character") #pjump could be NULL
  52
  53     pred = list()
  54     forecaster = new(paste(forecaster,"Forecaster",sep=""), data=data,
  55         pjump =
  56             if (is.null(pjump))
  57                 function() {}
  58             else
  59                 getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  60     for (today in indices)
  61     {
  62         #pred$append(...) is slow; TODO: use R6 class
  63         pred[[length(pred)+1]] = list(
  64             "serie" = forecaster$predict(today, memory, horizon, ...),
  65             "params" = forecaster$getParameters(),
  66             "index" = today
  67         )
  68     }
  69     new("Forecast",pred=pred)
  70 }