Forecast accuracy: no MASE with two vectors as arguments
The MASE requires the historical data to compute the scaling factor. It is not computed from the future data as in the answer by @FBE. So if you don't pass the historical data to accuracy(), the MASE cannot be computed. For example,
> library(forecast)> fcast <- snaive(window(USAccDeaths,end=1977.99))> accuracy(fcast$mean,USAccDeaths) ME RMSE MAE MPE MAPE ACF1 225.1666667 341.1639391 259.5000000 2.4692164 2.8505546 0.3086626 Theil's U 0.4474491 But if you pass the whole fcast object (which includes the historical data), you get
> accuracy(fcast,USAccDeaths) ME RMSE MAE MPE MAPE MASE 225.1666667 341.1639391 259.5000000 2.4692164 2.8505546 0.5387310 ACF1 Theil's U 0.3086626 0.4474491
The paper on MASE clearly explains how to find it (even for non time-series data)
computeMASE <- function(forecast,train,test,period){ # forecast - forecasted values # train - data used for forecasting .. used to find scaling factor # test - actual data used for finding MASE.. same length as forecast # period - in case of seasonal data.. if not, use 1 forecast <- as.vector(forecast) train <- as.vector(train) test <- as.vector(test) n <- length(train) scalingFactor <- sum(abs(train[(period+1):n] - train[1:(n-period)])) / (n-period) et <- abs(test-forecast) qt <- et/scalingFactor meanMASE <- mean(qt) return(meanMASE)}
To help myself a little bit, I created a function to calculate the MASE, as described by Hyndman et al in "Another look at measures of forecast accuracy" (2006).
calculateMASE <- function(f,y) { # f = vector with forecasts, y = vector with actuals if(length(f)!=length(y)){ stop("Vector length is not equal") } n <- length(f) return(mean(abs((y - f) / ((1/(n-1)) * sum(abs(y[2:n]-y[1:n-1]))))))}For reference, see: