X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=R%2FgetForecast.R;h=fde8e4501deb934929d43331196f005ecdef50d2;hp=e126946eec7b32215e8d262bf0aa822470303daf;hb=e030a6e31232332b73187eda25870e843152c174;hpb=31f7d913d4a99d0a4db9bcfe40e31cebf90b22e6

diff --git a/R/getForecast.R b/R/getForecast.R
index e126946..fde8e45 100644
--- a/R/getForecast.R
+++ b/R/getForecast.R
@@ -1,44 +1,46 @@
 #' @title get Forecast
 #'
 #' @description Predict time-series curves for the selected days indices (lines in data).
-#'   Run the forecasting task described by \code{delta_forecaster_name} and
-#'   \code{shape_forecaster_name} on data obtained with \code{getData}
 #'
 #' @param data Dataset, object of type \code{Data} output of \code{getData}
 #' @param indices Days indices where to forecast (the day after)
-#' @param memory Data depth (in days) to be used for prediction
-#' @param horizon Number of time steps to predict
-#' @param shape_forecaster_name Name of the shape forcaster
+#' @param forecaster Name of the main forcaster
 #' \itemize{
 #'   \item Persistence : use values of last (similar, next) day
-#'   \item Neighbors : use PM10 from the k closest neighbors' tomorrows
+#'   \item Neighbors : use values from the k closest neighbors' tomorrows
 #'   \item Average : global average of all the (similar) "tomorrow of past"
+#'   \item Zero : just output 0 (benchmarking purpose)
+#'   \item Level : output a flat serie repeating the last observed level
 #' }
-#' @param delta_forecaster_name Name of the delta forecaster
+#' @param pjump How to predict the jump at the interface between two days ?
 #' \itemize{
 #'   \item Persistence : use last (similar) day values
-#'   \item Neighbors: re-use the weights optimized in corresponding shape forecaster
+#'   \item Neighbors: re-use the weights optimized in corresponding forecaster
 #'   \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
 #'
 #' @return An object of class Forecast
 #'
 #' @examples
 #' data = getData(ts_data="data/pm10_mesures_H_loc.csv", exo_data="data/meteo_extra_noNAs.csv",
-#'   input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at="07")
-#' pred = getForecast(data, 2200:2230, Inf, 12, "Persistence", "Persistence")
+#'   input_tz = "Europe/Paris", working_tz="Europe/Paris", predict_at=7)
+#' pred = getForecast(data, 2200:2230, "Persistence", "Persistence", 500, 12)
 #' \dontrun{#Sketch for real-time mode:
 #' data = new("Data", ...)
+#' forecaster = new(..., data=data)
 #' repeat {
 #'   data$append(some_new_data)
-#'   pred = getForecast(data, ...)
+#'   pred = forecaster$predict(data$getSize(), ...)
 #'   #do_something_with_pred
 #' }}
 #' @export
-getForecast = function(data, indices, memory, horizon,
-	shape_forecaster_name, delta_forecaster_name, ...)
+getForecast = function(data, indices, forecaster, pjump,
+	memory=Inf, horizon=data$getStdHorizon(), ...)
 {
+	# (basic) Arguments sanity checks
 	horizon = as.integer(horizon)[1]
 	if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
 		stop("Horizon too short or too long")
@@ -46,30 +48,19 @@ getForecast = function(data, indices, memory, horizon,
 	if (any(indices<=0 | indices>data$getSize()))
 		stop("Indices out of range")
 	indices = sapply(indices, dateIndexToInteger, data)
-
-	#NOTE: some assymetry here...
-	shape_forecaster = new(paste(shape_forecaster_name,"ShapeForecaster",sep=""), data=data)
-	#A little bit strange, but match.fun() and get() fail
-	delta_forecaster = getFromNamespace(
-		paste("get",delta_forecaster_name,"DeltaForecast",sep=""), "talweg")
+	if (!is.character(forecaster) || !is.character(pjump))
+		stop("forecaster and pjump should be of class character")
 
 	pred = list()
+	forecaster = new(paste(forecaster,"Forecaster",sep=""), data=data,
+		pjump = getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
 	for (today in indices)
 	{
-		#shape always predicted first (on centered series, no scaling taken into account),
-		#with side-effect: optimize some parameters (h, weights, ...)
-		predicted_shape = shape_forecaster$predict(today, memory, horizon, ...)
-		#then, delta prediction can re-use some variables optimized previously (like neighbors infos)
-		predicted_delta = delta_forecaster(data, today, memory, horizon,
-			shape_forecaster$getParameters(), ...)
-
-		#TODO: this way is faster than a call to append(); why ?
 		pred[[length(pred)+1]] = list(
-			# Predict shape and align it on end of current day
-			serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1] +
-				predicted_delta, #add predicted jump
-			params = shape_forecaster$getParameters(),
-			index = today )
+			"serie" = forecaster$predict(today, memory, horizon, ...),
+			"params" = forecaster$getParameters(),
+			"index" = today
+		)
 	}
 	new("Forecast",pred=pred)
 }