Metrics of Forecast Accuracy if Actuals are close to zero or have large outliers Some measures of forecast accuracy, such as the mean absolute percentage error (MAPE), are "distorted" or are not defined, it the actual realization of the variable is close to zero, or equal to zero, respectively. This often happens with regard to growth rates, where for example GDP growth or Inflation is close to zero.
Moreover, some metrics, such as the RMSE, penalize outliers so much that for some purposes the metric becomes almost useless. Take for example the RMSE over 40 quarter rolling one year ahead forecasts. None of the major forecasters surveyed by Bloomberg anticipated the pandemic at the end of 2019 (for good reason), and once 2020 is included in the sample, the errors explode.
Are there any metrics in use that address either of these issues systematically, maybe even both? I am aware of the two symmetric mean absolute percentage errors (SMAPE1, SMAPE2) and SMASE. Are there others?
Note that I am not interested in model based forecast evaluation. Knowledge of how the forecasts are derived should not be required.

Relative Total Absolute Error: mean of all absolute errors devided by the mean of the absolute actuals.
This measure is a MAPE where the sum is moved into the fraction. An advantage is that the RTAE can deal with zero actuals.
That said, SMAPE and the RTAE are useful when there are a few zeroes in your time series. If there a lot of zeroes; close to 50% or more, then these measures are not useful to evaluate the forecast. In such a case the best forecast would be zero according to these measures. Here is a reference for error measures when having time series with a lot of zeroes: https://www.lancaster.ac.uk/pg/waller/pdfs/Intermittent_Demand_Forecasting.pdf .
