can someone give me an explanation on how one would interpret the result of a scaled error measure. For example the Mean Absolute Scaled Measure (MASE). The numerator is the mean absolute error and the denominator the mean absolute error of a benchmarking method.
While I know that a MASE < 1 means that the forecasting method is better than the naive one, and MASE > 1 that it is worse, I still have a difficult time trying to interpret the results.
Does the result say nothing about how the forecast performed in regards to the actual result? Does it only compare the result to a benchmarking method?
Let's say you are working for a company that makes sales predictions, now you want to present your performance to a customer. What would a e.g. 0,8 MASE say about your overall performance? Does it just say you're better than the naive method because it is < 1?
The MAPE for example would say that you are off by 80% if the MAPE were 0,8, but then again the mape is heavily unreliable, this is why I opted for scaled measures. I just find it so difficult to interpret the result in a 'customer friendly' way.
Thanks for your help!