Suppose I have a collection of MAE across multiple time series (say, 10), and 3 models. However, MAE cannot be compared across time series.

I compare the errors in this way: assign ranks to models, so that each model gets a rank for every time series with respect to its MAE. As a results, for each model instead of MAE, I have a collection of ranks: 10 ranks for each model, where ranks are from 1 to 3.

Can I analyze this qualitative "rank error"? If I analyze it, what assumptions should I make?

NB. Some information is lost in the calculation of ranks, since ranks 1 and 2 for time series 1 can be divided by 0.1 in MAE (insignificant), while the same ranks can be divided by 1000 in MAE for another time series (which is exceptionally significant).


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.