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

#' Compute error
#'
#' Compute the errors between forecasted and measured series.
#'
#' @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). 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, pred, horizon=data$getStdHorizon())
{
	L = pred$getSize()
	mape_day = rep(0, horizon)
	abs_day = rep(0, horizon)
	mape_indices = rep(NA, L)
	abs_indices = rep(NA, L)

	nb_no_NA_data = 0
	for (i in seq_len(L))
	{
		index = pred$getIndexInData(i)
		serie = data$getSerie(index+1)[1:horizon]
		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 - 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 - forecast)
			abs_day = abs_day + abs_increment
			mape_indices[i] = mean(mape_increment)
			abs_indices[i] = mean(abs_increment)
		}
	}

	list(
		"abs" = list(
			"day" = abs_day / nb_no_NA_data,
			"indices" = abs_indices),
		"MAPE" = list(
			"day" = mape_day / nb_no_NA_data,
			"indices" = mape_indices) )
}
Commit	Line	Data
	1	#' Compute error
	2	#'
	3	#' Compute the errors between forecasted and measured series.
	4	#'
	5	#' @param data Object of class \code{Data} output of \code{getData}
	6	#' @param pred Object of class \code{Forecast} output of \code{computeForecast}
	7	#' @param horizon Horizon where to compute the error
	8	#' (<= horizon used in \code{computeForecast})
	9	#'
	10	#' @return A list (abs,MAPE) of lists (day,indices). The "indices" slots contain series
	11	#' of size L where L is the number of predicted days; i-th value is the averaged error
	12	#' (absolute or MAPE) on day i. The "day" slots contain curves of errors, for each time
	13	#' step, averaged on the L forecasting days.
	14	#'
	15	#' @export
	16	computeError = function(data, pred, horizon=data$getStdHorizon())
	17	{
	18	L = pred$getSize()
	19	mape_day = rep(0, horizon)
	20	abs_day = rep(0, horizon)
	21	mape_indices = rep(NA, L)
	22	abs_indices = rep(NA, L)
	23
	24	nb_no_NA_data = 0
	25	for (i in seq_len(L))
	26	{
	27	index = pred$getIndexInData(i)
	28	serie = data$getSerie(index+1)[1:horizon]
	29	forecast = pred$getForecast(i)[1:horizon]
	30	if (!any(is.na(serie)) && !any(is.na(forecast)))
	31	{
	32	nb_no_NA_data = nb_no_NA_data + 1
	33	mape_increment = abs(serie - forecast) / serie
	34	mape_increment[is.nan(mape_increment)] = 0. # 0 / 0
	35	mape_increment[!is.finite(mape_increment)] = 1. # >0 / 0
	36	mape_day = mape_day + mape_increment
	37	abs_increment = abs(serie - forecast)
	38	abs_day = abs_day + abs_increment
	39	mape_indices[i] = mean(mape_increment)
	40	abs_indices[i] = mean(abs_increment)
	41	}
	42	}
	43
	44	list(
	45	"abs" = list(
	46	"day" = abs_day / nb_no_NA_data,
	47	"indices" = abs_indices),
	48	"MAPE" = list(
	49	"day" = mape_day / nb_no_NA_data,
	50	"indices" = mape_indices) )
	51	}