X-Git-Url: https://git.auder.net/game/current/git-logo.png?a=blobdiff_plain;f=pkg%2FR%2FcomputeForecast.R;h=e50e63dc950ef13679bd8e8ed4962b008bd5663a;hb=HEAD;hp=e1b29b6b9c9a800e946031f663589937963e1cdf;hpb=af718fd5a9a330b13b331e78824a47407a3479ae;p=talweg.git

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index e1b29b6..e50e63d 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -3,6 +3,8 @@
 #' 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.
+#' Note: in training stage ts_hat(day+1) = f(ts(day), exo(day+1)),
+#' and in production ts_hat(day+1) = f(ts(day), exo_hat(day+1))
 #'
 #' @param data Object of class Data, output of \code{getData()}.
 #' @param indices Indices where to forecast (the day after); integers relative to the
@@ -19,10 +21,9 @@
 #'   \itemize{
 #'     \item Persistence : use last (similar) day
 #'     \item Neighbors: re-use the weights from F_Neighbors
-#'     \item Zero: just output 0 (no adjustment)
+#'     \item LastValue: start serie with last observed value
+#'     \item Zero: no adjustment => use shape prediction only
 #'   }
-#'   If pjump=NULL, then no adjustment is performed (output of \code{predictShape()} is
-#'   used directly).
 #' @param predict_from First time step to predict.
 #' @param memory Data depth (in days) to be used for prediction.
 #' @param horizon Last time step to predict.
@@ -33,10 +34,10 @@
 #' @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, limit=200)
-#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#' ts_data <- system.file("extdata","intraday_measures.csv",package="talweg")
+#' exo_data <- system.file("extdata","daily_exogenous.csv",package="talweg")
+#' data <- getData(ts_data, exo_data, date_format="%Y-%m-%d %H:%M:%S", limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "LastValue",
 #'   predict_from=8, memory=50, horizon=12, ncores=1)
 #' \dontrun{
 #' #Sketch for real-time mode:
@@ -77,15 +78,14 @@ computeForecast = function(data, indices, forecaster, pjump, predict_from,
 		stop("Indices out of range")
 	if (!is.character(forecaster))
 		stop("forecaster (name): character")
-	if (!is.null(pjump) && !is.character(pjump))
-		stop("pjump (function): character or NULL")
+	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")
 
-	if (!is.null(pjump))
-		pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")
+	pjump <- getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg")
 	forecaster = forecaster_class_name$new(pjump)
 
 	computeOneForecast <- function(i)