pkg/R/computeForecast.R

   1 #' Compute forecast
   2 #'
   3 #' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
   4 #' This function just runs a loop over all requested indices, and stores the individual
   5 #' forecasts into a list which is then turned into a Forecast object.
   6 #'
   7 #' @param data Object of class Data, output of \code{getData()}.
   8 #' @param indices Indices where to forecast (the day after); integers relative to the
   9 #'   beginning of data, or (convertible to) Date objects.
  10 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
  11 #' \itemize{
  12 #'   \item Persistence : use last (similar, next) day
  13 #'   \item Neighbors : weighted tomorrows of similar days
  14 #'   \item Average : average tomorrow of all same day-in-week
  15 #'   \item Zero : just output 0 (benchmarking purpose)
  16 #' }
  17 #' @param pjump Function to predict the jump at the interface between two days;
  18 #'   more details: ?J_<functionname>
  19 #' \itemize{
  20 #'   \item Persistence : use last (similar, next) day
  21 #'   \item Neighbors: re-use the weights from F_Neighbors
  22 #'   \item Zero: just output 0 (no adjustment)
  23 #' }
  24 #' @param memory Data depth (in days) to be used for prediction.
  25 #' @param horizon Number of time steps to predict.
  26 #' @param ncores Number of cores for parallel execution (1 to disable).
  27 #' @param ... Additional parameters for the forecasting models.
  28 #'
  29 #' @return An object of class Forecast
  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="GMT", working_tz="GMT",
  35 #'   predict_at=7, limit=200)
  36 #' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
  37 #'   memory=50, horizon=12, ncores=1)
  38 #' \dontrun{#Sketch for real-time mode:
  39 #' data <- Data$new()
  40 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
  41 #' repeat {
  42 #'   # In the morning 7am+ or afternoon 1pm+:
  43 #'   data$append(
  44 #'     times_from_H+1_yersteday_to_Hnow,
  45 #'     PM10_values_of_last_24h,
  46 #'     exogenous_measures_of_last_24h,
  47 #'     exogenous_predictions_for_next_24h)
  48 #'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
  49 #'   #do_something_with_pred
  50 #' }}
  51 #' @export
  52 computeForecast = function(data, indices, forecaster, pjump, predict_from,
  53     memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
  54 {
  55     # (basic) Arguments sanity checks
  56     predict_from = as.integer(predict_from)[1]
  57     if (! predict_from %in% 1:length(data$getSerie(1)))
  58         stop("predict_from in [1,24] (hours)")
  59     if (hasArg("opera") && !list(...)$opera && memory < Inf)
  60         memory <- Inf #finite memory in training mode makes no sense
  61     horizon = as.integer(horizon)[1]
  62     if (horizon<=predict_from || horizon>length(data$getSerie(1)))
  63         stop("Horizon in [predict_from+1,24] (hours)")
  64     integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
  65     if (any(integer_indices<=0 | integer_indices>data$getSize()))
  66         stop("Indices out of range")
  67     if (!is.character(forecaster) || !is.character(pjump))
  68         stop("forecaster (name) and pjump (function) should be of class character")
  69
  70     pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
  71     forecaster_class_name = getFromNamespace(
  72         paste(forecaster,"Forecaster",sep=""), "talweg")
  73     forecaster = forecaster_class_name$new( #.pjump =
  74         getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
  75
  76     computeOneForecast <- function(i)
  77     {
  78         list(
  79             "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
  80             "params" = forecaster$getParameters(),
  81             "index" = i )
  82     }
  83
  84     p <-
  85         if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
  86             parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
  87         else
  88             lapply(integer_indices, computeOneForecast)
  89
  90     # TODO: find a way to fill pred in //...
  91     for (i in seq_along(integer_indices))
  92     {
  93         pred$append(
  94             forecast = p[[i]]$forecast,
  95             params = p[[i]]$params,
  96             index_in_data = p[[i]]$index
  97         )
  98     }
  99     pred
 100 }