#'
#' Predict time-series curves for the selected days indices (lines in data).
#'
-#' @param data Object of type \code{Data}, output of \code{getData()}
+#' @param data Object of type \code{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 forcaster
+#' 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 ncores Number of cores for parallel execution (1 to disable)
-#' @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="GMT", working_tz="GMT", 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)
+#' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
+#' memory=500, horizon=12, ncores=1)
#' \dontrun{#Sketch for real-time mode:
-#' data = new("Data", ...)
-#' forecaster = new(..., data=data)
+#' data <- Data$new()
+#' # Initialize: first day has no predictions attached
+#' data$initialize()
+#' forecaster <- MyForecaster$new(myJumpPredictFunc)
#' repeat {
-#' data$append(some_new_data)
-#' pred = forecaster$predict(data$getSize(), ...)
+#' # During the night between days j and j+1:
+#' data$appendExoHat(exogenous_predictions)
+#' # In the morning 7am+ or afternoon 1pm+:
+#' data$setMeasures(
+#' data$getSize()-1,
+#' times_from_H+1_yersteday_to_Hnow,
+#' PM10_values_of_last_24h,
+#' exogenous_measures_for_yersteday)
+#' pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
#' #do_something_with_pred
#' }}
#' @export