pkg/R/computeForecast.R

   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 ... Additional parameters for the forecasting models
  23 #'
  24 #' @return An object of class Forecast
  25 #'
  26 #' @examples
  27 #' ts_data = system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
  28 #' exo_data = system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
  29 #' data = getData(ts_data, exo_data, input_tz = "Europe/Paris",
  30 #'   working_tz="Europe/Paris", predict_at=7)
  31 #' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
  32 #' \dontrun{#Sketch for real-time mode:
  33 #' data = new("Data", ...)
  34 #' forecaster = new(..., data=data)
  35 #' repeat {
  36 #'   data$append(some_new_data)
  37 #'   pred = forecaster$predict(data$getSize(), ...)
  38 #'   #do_something_with_pred
  39 #' }}
  40 #' @export
  41 computeForecast = function(data, indices, forecaster, pjump,
  42     memory=Inf, horizon=data$getStdHorizon(), ...)
  43 {
  44     # (basic) Arguments sanity checks
  45     horizon = as.integer(horizon)[1]
  46     if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
  47         stop("Horizon too short or too long")
  48     integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
  49     if (any(integer_indices<=0 | integer_indices>data$getSize()))
  50         stop("Indices out of range")
  51     if (!is.character(forecaster) || !is.character(pjump))
  52         stop("forecaster (name) and pjump (function) should be of class character")
  53
  54     pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
  55     forecaster_class_name = getFromNamespace(paste(forecaster,"Forecaster",sep=""), "talweg")
  56     forecaster = forecaster_class_name$new( #.pjump =
  57         getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  58
  59 #oo = forecaster$predictSerie(data, integer_indices[1], memory, horizon, ...)
  60 #browser()
  61
  62     library(parallel)
  63     ppp <- parallel::mclapply(seq_along(integer_indices), function(i) {
  64         list(
  65             "forecast" = forecaster$predictSerie(data, integer_indices[i], memory, horizon, ...),
  66             "params"= forecaster$getParameters(),
  67             "index" = integer_indices[i] )
  68         }, mc.cores=3)
  69
  70 #browser()
  71
  72 for (i in seq_along(integer_indices))
  73 {
  74     pred$append(
  75             new_serie = ppp[[i]]$forecast,
  76             new_params = ppp[[i]]$params,
  77             new_index_in_data = ppp[[i]]$index
  78     )
  79 }
  80
  81     pred
  82 }