I am using the "forecast" package in R to forecast time series data. I am programming some time series cross validation based off of reading resources from Rob J Hyndman.
The last paragraph on page 7 in Hyndman "Measuring forecast accuracy" states:
"To calculate the MASE (sic, mean absolute scaled error) we need to compute the scaling statistic Q, but we do not want the value of Q to change with each training set. One approach is to compute Q using all the available data"
I interpret this as holding the mean absolute error (MAE) calculation for the naive forecast constant across all MASE Calculations. (Note: $\text{MASE}=\text{MAE}/Q$)
The accuracy
function in the "forecast" package calculates MASE for a forecast and states (on page 4 of the manual):
By default, the MASE calculation is scaled using MAE of in-sample naive forecasts for nonseasonal time series, in-sample seasonal naive forecasts for seasonal time series and in-sample mean forecasts for non-time series data.
As I roll my forecast origin forward, can I run the accuracy
function to calculate the MASE? Or do I have to manually calculate the MASE because the accuracy
function does not use "all the available data" and will change for each training data set?