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#' Compute Mean Absolute Scaled Error (MASE)
#'
#' MASE is computed as \eqn{sum(abs(validation - forecast)) / sum(abs(validation[-1] - validation[-n])) / (n/(n-1))}.
#' @param forecast A numeric vector of forecasted values
#' @param validation A numeric vector of actual (real) values
#' @return A Mean Absolute Scaled Error (MASE)
#' @author Michal Burda
#' @seealso [rmse()], [smape()], [frbe()]
#' @export
mase <- function(forecast, validation) {
.mustBeNumericVector(forecast)
.mustBeNumericVector(validation)
.mustNotHaveNA(forecast)
.mustNotHaveNA(validation)
.mustBe(length(forecast) == length(validation), "Length of 'forecast' and 'validation' must be equal")
.mustBe(length(forecast) > 1, "Length of both 'forecast' and 'validation' must be greater than zero")
n <- length(validation)
#mase <- mean(abs(validation - forecast)) / mean(abs(history[-length(history)] - history[-1]))
mase <- sum(abs(validation - forecast)) / sum(abs(validation[-1] - validation[-n])) / (n/(n-1))
return(mase)
}
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