Please refer to the paper by Rob J Hyndman, who is a pioneer in time series and has contributed forecast package in R: Another look at measures of forecast accuracy
As per the conclusion in the paper:
We propose that scaled errors become the standard measure for forecast accuracy, where the forecast error is scaled by the in-sample mean absolute error obtained using the naıve forecasting method. This is widely applicable and is always defined and finite except in irrelevant cases where all historical data are equal. This new measure is also easily interpretable: values of MASE greater than one indicate the forecasts are worse, on average than in-sample one-step forecasts from the naive method.
Of course, there will be situations where some of the existing measures may still be preferred. For example, if all series are on the same scale, then the MAE may be preferred because it is simpler to explain. If all data are positive and much greater than zero, the MAPE may still be preferred for reasons of simplicity. However, in situations where there are very different scales including data which are close to zero or negative, we suggest the MASE is the best available measure of forecast accuracy.