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 Object of type \code{Data}, output of \code{getData()}
   6 #' @param indices Indices where to forecast (the day after); integers relative to the
   7 #'   beginning of data, or (convertible to) Date objects
   8 #' @param forecaster Name of the main forcaster
   9 #' \itemize{
  10 #'   \item Persistence : use values of last (similar, next) day
  11 #'   \item Neighbors : use values from the k closest neighbors' tomorrows
  12 #'   \item Average : global average of all the (similar) "tomorrow of past"
  13 #'   \item Zero : just output 0 (benchmarking purpose)
  14 #' }
  15 #' @param pjump How to predict the jump at the interface between two days ?
  16 #' \itemize{
  17 #'   \item Persistence : use last (similar) day values
  18 #'   \item Neighbors: re-use the weights optimized in corresponding forecaster
  19 #'   \item Zero: just output 0 (no adjustment)
  20 #' }
  21 #' @param memory Data depth (in days) to be used for prediction
  22 #' @param horizon Number of time steps to predict
  23 #' @param ncores Number of cores for parallel execution (1 to disable)
  24 #' @param ... Additional parameters for the forecasting models
  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 }