TODO: unit tests for simil days

[talweg.git] / pkg / R / computeForecast.R

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 1e79118..a4a539a 100644 (file)
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,41 +1,51 @@
 #' 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 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, 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