#' 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 Dataset, object of type \code{Data} output of \code{getData}
-#' @param indices Days indices where to forecast (the day after)
-#' @param forecaster Name of the main forcaster
+#' @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>
#' \itemize{
-#' \item Persistence : use values of last (similar, next) day
-#' \item Neighbors : use values from the k closest neighbors' tomorrows
-#' \item Average : global average of all the (similar) "tomorrow of past"
+#' \item Persistence : use last (similar, next) day
+#' \item Neighbors : weighted tomorrows of similar days
+#' \item Average : average tomorrow of all same day-in-week
#' \item Zero : just output 0 (benchmarking purpose)
#' }
-#' @param pjump How to predict the jump at the interface between two days ?
+#' @param pjump Function to predict the jump at the interface between two days;
+#' more details: ?J_<functionname>
#' \itemize{
-#' \item Persistence : use last (similar) day values
-#' \item Neighbors: re-use the weights optimized in corresponding forecaster
+#' \item Persistence : use last (similar, next) day
+#' \item Neighbors: re-use the weights from F_Neighbors
#' \item Zero: just output 0 (no adjustment)
#' }
-#' @param memory Data depth (in days) to be used for prediction
-#' @param horizon Number of time steps to predict
-#' @param ... Additional parameters for the forecasting models
+#' @param memory Data depth (in days) to be used for prediction.
+#' @param horizon Number of time steps to predict.
+#' @param ncores Number of cores for parallel execution (1 to disable).
+#' @param ... Additional parameters for the forecasting models.
#'
#' @return An object of class Forecast
#'
#' @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 = "Europe/Paris",
-#' working_tz="Europe/Paris", predict_at=7)
-#' pred = computeForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
+#' 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, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#' memory=50, horizon=12, ncores=1)
#' \dontrun{#Sketch for real-time mode:
-#' data = new("Data", ...)
-#' forecaster = new(..., data=data)
+#' data <- Data$new()
+#' forecaster <- MyForecaster$new(myJumpPredictFunc)
#' repeat {
-#' data$append(some_new_data)
-#' pred = forecaster$predict(data$getSize(), ...)
+#' # In the morning 7am+ or afternoon 1pm+:
+#' data$append(
+#' times_from_H+1_yersteday_to_Hnow,
+#' PM10_values_of_last_24h,
+#' exogenous_measures_of_last_24h,
+#' exogenous_predictions_for_next_24h)
+#' pred <- forecaster$predictSerie(data, data$getSize(), ...)
#' #do_something_with_pred
#' }}
#' @export
-computeForecast = function(data, indices, forecaster, pjump,
- memory=Inf, horizon=data$getStdHorizon(), ...)
+computeForecast = function(data, indices, forecaster, pjump, predict_from,
+ memory=Inf, horizon=length(data$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$getCenteredSerie(2)))
- stop("Horizon too short or too long")
+ if (horizon<=predict_from || horizon>length(data$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$getSize()))
stop("Indices out of range")
stop("forecaster (name) and pjump (function) should be of class character")
pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
- forecaster_class_name = getFromNamespace(paste(forecaster,"Forecaster",sep=""), "talweg")
+ forecaster_class_name = getFromNamespace(
+ paste(forecaster,"Forecaster",sep=""), "talweg")
forecaster = forecaster_class_name$new( #.pjump =
getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
- for (today in integer_indices)
+
+ computeOneForecast <- function(i)
+ {
+ list(
+ "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
+ "params" = forecaster$getParameters(),
+ "index" = i )
+ }
+
+ p <-
+ if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+ parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
+ else
+ lapply(integer_indices, computeOneForecast)
+
+ # TODO: find a way to fill pred in //...
+ for (i in seq_along(integer_indices))
{
pred$append(
- new_serie = forecaster$predictSerie(data, today, memory, horizon, ...),
- new_params = forecaster$getParameters(),
- new_index_in_data = today
+ forecast = p[[i]]$forecast,
+ params = p[[i]]$params,
+ index_in_data = p[[i]]$index
)
}
pred
projects / talweg.git / blobdiff