#' Compute error
#'
-#' Obtain the errors between forecast and data
+#' Compute the errors between forecasted and measured series.
#'
-#' @param data Dataset, object of class \code{Data} output of \code{getData}
-#' @param forecast Forecast object, class \code{Forecast} output of \code{computeForecast}
-#' @param horizon Horizon where to compute the error (<= horizon used in \code{computeForecast})
+#' @param data Object of class \code{Data} output of \code{getData}
+#' @param pred Object of class \code{Forecast} output of \code{computeForecast}
+#' @param horizon Horizon where to compute the error
+#' (<= horizon used in \code{computeForecast})
#'
-#' @return A list (abs,MAPE) of lists (day,indices)
+#' @return A list (abs,MAPE) of lists (day,indices). The "indices" slots contain series
+#' of size L where L is the number of predicted days; i-th value is the averaged error
+#' (absolute or MAPE) on day i. The "day" slots contain curves of errors, for each time
+#' step, averaged on the L forecasting days.
#'
#' @export
-computeError = function(data, forecast, horizon=data$getStdHorizon())
+computeError = function(data, pred, horizon=data$getStdHorizon())
{
- L = forecast$getSize()
+ L = pred$getSize()
mape_day = rep(0, horizon)
abs_day = rep(0, horizon)
mape_indices = rep(NA, L)
nb_no_NA_data = 0
for (i in seq_len(L))
{
- index = forecast$getIndexInData(i)
+ index = pred$getIndexInData(i)
serie = data$getSerie(index+1)[1:horizon]
- pred = forecast$getSerie(i)[1:horizon]
- if (!any(is.na(serie)) && !any(is.na(pred)))
+ forecast = pred$getForecast(i)[1:horizon]
+ if (!any(is.na(serie)) && !any(is.na(forecast)))
{
nb_no_NA_data = nb_no_NA_data + 1
- mape_increment = abs(serie - pred) / serie
+ mape_increment = abs(serie - forecast) / serie
mape_increment[is.nan(mape_increment)] = 0. # 0 / 0
mape_increment[!is.finite(mape_increment)] = 1. # >0 / 0
mape_day = mape_day + mape_increment
- abs_increment = abs(serie - pred)
+ abs_increment = abs(serie - forecast)
abs_day = abs_day + abs_increment
mape_indices[i] = mean(mape_increment)
abs_indices[i] = mean(abs_increment)