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 forecaster; more details: ?F_<forecastername>
   9 #' \itemize{
  10 #'   \item Persistence : use last (similar, next) day
  11 #'   \item Neighbors : weighted tomorrows of similar days
  12 #'   \item Average : average tomorrow of all same day-in-week
  13 #'   \item Zero : just output 0 (benchmarking purpose)
  14 #' }
  15 #' @param pjump Function to predict the jump at the interface between two days;
  16 #'   more details: ?J_<functionname>
  17 #' \itemize{
  18 #'   \item Persistence : use last (similar, next) day
  19 #'   \item Neighbors: re-use the weights from F_Neighbors
  20 #'   \item Zero: just output 0 (no adjustment)
  21 #' }
  22 #' @param memory Data depth (in days) to be used for prediction.
  23 #' @param horizon Number of time steps to predict.
  24 #' @param ncores Number of cores for parallel execution (1 to disable).
  25 #' @param ... Additional parameters for the forecasting models.
  26 #'
  27 #' @return An object of class Forecast
  28 #'
  29 #' @examples
  30 #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
  31 #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
  32 #' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7)
  33 #' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
  34 #'   memory=500, horizon=12, ncores=1)
  35 #' \dontrun{#Sketch for real-time mode:
  36 #' data <- Data$new()
  37 #' # Initialize: first day has no predictions attached
  38 #' data$initialize()
  39 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
  40 #' repeat {
  41 #'   # During the night between days j and j+1:
  42 #'   data$appendExoHat(exogenous_predictions)
  43 #'   # In the morning 7am+ or afternoon 1pm+:
  44 #'   data$setMeasures(
  45 #'     data$getSize()-1,
  46 #'     times_from_H+1_yersteday_to_Hnow,
  47 #'     PM10_values_of_last_24h,
  48 #'     exogenous_measures_for_yersteday)
  49 #'   pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
  50 #'   #do_something_with_pred
  51 #' }}
  52 #' @export
  53 computeForecast = function(data, indices, forecaster, pjump,
  54     memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
  55 {
  56     # (basic) Arguments sanity checks
  57     horizon = as.integer(horizon)[1]
  58     if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
  59         stop("Horizon too short or too long")
  60     integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
  61     if (any(integer_indices<=0 | integer_indices>data$getSize()))
  62         stop("Indices out of range")
  63     if (!is.character(forecaster) || !is.character(pjump))
  64         stop("forecaster (name) and pjump (function) should be of class character")
  65
  66     pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
  67     forecaster_class_name = getFromNamespace(
  68         paste(forecaster,"Forecaster",sep=""), "talweg")
  69     forecaster = forecaster_class_name$new( #.pjump =
  70         getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  71
  72     if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
  73     {
  74         p <- parallel::mclapply(seq_along(integer_indices), function(i) {
  75             list(
  76                 "forecast" = forecaster$predictSerie(
  77                     data, integer_indices[i], memory, horizon, ...),
  78                 "params"= forecaster$getParameters(),
  79                 "index" = integer_indices[i] )
  80             }, mc.cores=ncores)
  81     }
  82     else
  83     {
  84         p <- lapply(seq_along(integer_indices), function(i) {
  85             list(
  86                 "forecast" = forecaster$predictSerie(
  87                     data, integer_indices[i], memory, horizon, ...),
  88                 "params"= forecaster$getParameters(),
  89                 "index" = integer_indices[i] )
  90             })
  91     }
  92
  93     # TODO: find a way to fill pred in //...
  94     for (i in seq_along(integer_indices))
  95     {
  96         pred$append(
  97             forecast = p[[i]]$forecast,
  98             params = p[[i]]$params,
  99             index_in_data = p[[i]]$index
 100         )
 101     }
 102     pred
 103 }