#' Compute forecast
#'
-#' Predict time-series curves for the selected days indices (lines in data).
+#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
+#' This function just runs a loop over all requested indices, and stores the individual
+#' forecasts into a list which is then turned into a Forecast object.
#'
-#' @param data Object of type \code{Data}, output of \code{getData()}.
+#' @param data Object of class Data, output of \code{getData()}.
#' @param indices Indices where to forecast (the day after); integers relative to the
#' beginning of data, or (convertible to) Date objects.
#' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
#' @examples
#' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
#' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
-#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7)
-#' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
-#' memory=500, horizon=12, ncores=1)
+#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
+#' predict_at=7, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#' memory=50, horizon=12, ncores=1)
#' \dontrun{#Sketch for real-time mode:
#' data <- Data$new()
#' forecaster <- MyForecaster$new(myJumpPredictFunc)
#' PM10_values_of_last_24h,
#' exogenous_measures_of_last_24h,
#' exogenous_predictions_for_next_24h)
-#' pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
+#' pred <- forecaster$predictSerie(data, data$getSize(), ...)
#' #do_something_with_pred
#' }}
#' @export
-computeForecast = function(data, indices, forecaster, pjump,
- memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
+computeForecast = function(data, indices, forecaster, pjump, predict_from,
+ memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
{
# (basic) Arguments sanity checks
+ predict_from = as.integer(predict_from)[1]
+ if (! predict_from %in% 1:length(data$getSerie(1)))
+ stop("predict_from in [1,24] (hours)")
+ if (hasArg("opera") && !list(...)$opera && memory < Inf)
+ memory <- Inf #finite memory in training mode makes no sense
horizon = as.integer(horizon)[1]
- if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
- stop("Horizon too short or too long")
+ if (horizon<=predict_from || horizon>length(data$getSerie(1)))
+ stop("Horizon in [predict_from+1,24] (hours)")
integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
if (any(integer_indices<=0 | integer_indices>data$getSize()))
stop("Indices out of range")
forecaster = forecaster_class_name$new( #.pjump =
getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
- if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+ computeOneForecast <- function(i)
{
- p <- parallel::mclapply(seq_along(integer_indices), function(i) {
- list(
- "forecast" = forecaster$predictSerie(
- data, integer_indices[i], memory, horizon, ...),
- "params"= forecaster$getParameters(),
- "index" = integer_indices[i] )
- }, mc.cores=ncores)
- }
- else
- {
- p <- lapply(seq_along(integer_indices), function(i) {
- list(
- "forecast" = forecaster$predictSerie(
- data, integer_indices[i], memory, horizon, ...),
- "params"= forecaster$getParameters(),
- "index" = integer_indices[i] )
- })
+ list(
+ "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
+ "params" = forecaster$getParameters(),
+ "index" = i )
}
+ p <-
+ if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+ parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
+ else
+ lapply(integer_indices, computeOneForecast)
+
# TODO: find a way to fill pred in //...
for (i in seq_along(integer_indices))
{