- Forward filling missing short periods of missing values. Why keep this in when you explictly mention this is not normal? Either remove it all or don't impute anything
- Claiming superiority over classic models and then not mentioning any in the results table
- Or let's not forget, the cardinal sin of using MAPE as an evaluation metric
Long answer: Is the metric for people with subject-matter knowledge? Then (Weighted)RMSSE, or the MASE alternative for a median forecast. WRMSSE is is very nice, it can deal with zeroes, is scale-invariant and symmetrical in penalizing under/over-forecasting.
The above metrics are completely uninterpretable to people outside of the forecasting sphere though. For those cases i tend to just stick with raw errors; if a percentage metric is really necessary then a Weighted MAPE/RMSE, the weighing is still graspable for most, and it doesn't explode with zeroes.
I've also been exploring FVA (Forecast Value Added), compared against a second decent forecast. FVA is very intuitive, if your base-measures are reliable at least. Aside from that i always look at forecast plots. It's tedious but they often tell you a lot that gets lost in the numbers.
RMSLE i havent used much. From what i read it looks interesting, though more for very specific scenarios (many outliers, high variance, nonlinear data?)
RMSLE gives proportional error (so, scale-invariant) without MAPE's systematic under-prediction bias. It does require all-positive values, for the logarithm step.