#' Compute forecast
#'
-#' 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.
+#' Predict time-series curves ("today" from predict_from to horizon) at the selected days
+#' indices ("today" from 1am to predict_from-1). This function just runs a loop over all
+#' requested indices, and stores the individual forecasts into a Forecast object.
#'
#' @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 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)
-#' }
+#' \itemize{
+#' \item Persistence : use last (similar) day
+#' \item Neighbors : weighted similar days
+#' \item Average : average curve of all same day-in-week
+#' \item Zero : just output 0 (benchmarking purpose)
+#' }
#' @param pjump Function to predict the jump at the interface between two days;
#' more details: ?J_<functionname>
-#' \itemize{
-#' \item Persistence : use last (similar, next) day
-#' \item Neighbors: re-use the weights from F_Neighbors
-#' \item Zero: just output 0 (no adjustment)
-#' }
+#' \itemize{
+#' \item Persistence : use last (similar) day
+#' \item Neighbors: re-use the weights from F_Neighbors
+#' \item LastValue: start serie with last observed value
+#' \item Zero: no adjustment => use shape prediction only
+#' }
+#' @param predict_from First time step to predict.
#' @param memory Data depth (in days) to be used for prediction.
-#' @param horizon Number of time steps to predict.
+#' @param horizon Last time step to predict.
#' @param ncores Number of cores for parallel execution (1 to disable).
+#' @param verbose TRUE to print basic traces (runs beginnings)
#' @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="GMT", working_tz="GMT", predict_at=7)
-#' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
-#' memory=500, horizon=12, ncores=1)
-#' \dontrun{#Sketch for real-time mode:
+#' data <- getData(ts_data, exo_data, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "LastValue",
+#' predict_from=8, memory=50, horizon=12, ncores=1)
+#' \dontrun{
+#' #Sketch for real-time mode:
#' data <- Data$new()
#' forecaster <- MyForecaster$new(myJumpPredictFunc)
#' repeat {
-#' # In the morning 7am+ or afternoon 1pm+:
+#' # As soon as daily predictions are available:
#' data$append(
-#' times_from_H+1_yersteday_to_Hnow,
-#' PM10_values_of_last_24h,
-#' exogenous_measures_of_last_24h,
-#' exogenous_predictions_for_next_24h)
+#' level_hat=predicted_level,
+#' exo_hat=predicted_exogenous)
+#' # When a day ends:
+#' data$append(
+#' level=observed_level,
+#' exo=observed_exogenous)
+#' # And, at every hour:
+#' data$append(
+#' time=current_hour,
+#' value=current_PM10)
+#' # Finally, a bit before predict_from hour:
#' pred <- forecaster$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$getSerie(1)), ncores=3, verbose=FALSE, ...)
{
# (basic) Arguments sanity checks
+ predict_from = as.integer(predict_from)[1]
+ if (! predict_from %in% 1:length(data$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")
- if (!is.character(forecaster) || !is.character(pjump))
- stop("forecaster (name) and pjump (function) should be of class character")
+ if (!is.character(forecaster))
+ stop("forecaster (name): character")
+ if (!is.character(pjump))
+ stop("pjump (function): character")
pred = Forecast$new( sapply(indices, function(i) integerIndexToDate(i,data)) )
forecaster_class_name = getFromNamespace(
paste(forecaster,"Forecaster",sep=""), "talweg")
- forecaster = forecaster_class_name$new( #.pjump =
- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
- if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
- {
- 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
+ pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")
+ forecaster = forecaster_class_name$new(pjump)
+
+ computeOneForecast <- function(i)
{
- p <- lapply(seq_along(integer_indices), function(i) {
- list(
- "forecast" = forecaster$predictSerie(
- data, integer_indices[i], memory, horizon, ...),
- "params"= forecaster$getParameters(),
- "index" = integer_indices[i] )
- })
+ if (verbose)
+ print(paste("Index",i))
+ list(
+ "forecast" = forecaster$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))
{