I understand the general idea of different time series model fittings, calculations, and model comparison.
However, I am a little confused of understanding the forecast of a time series model. For example, if my model predictions for a forecast horizon such as
h=14 are all above (or lower) than the actual value, does it necessarily mean the model is bad?
I asked because maybe the model are able to capture all the patterns except there's a shift (maybe due to a drift / intercept term), causing the prediction plot is almost parallel to the actual data, except there's a gap in between.
I guess my questions can be summarized in:
1) How does one determined if the model forecasts are good ? For now I am using MAE, Mean, CV for Error to check on the forecast.
2) Consider two models with predictions having same MAE, in which one has all above (or below) predictions and the other having some higher and lower predictions, in a general sense, which one of them would be considered a better forecast?