When evaluating a time series prediction model, if very large errors (outliers) are a big concern, which error metric would you use to highlight them more: Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE)?

When evaluating a time series prediction model and very large errors, also known as outliers, are a significant concern, Root Mean Squared Error (RMSE) would be the preferred error metric to highlight them more over Mean Absolute Error (MAE). Mean Absolute Error (MAE) is calculated as the average of the absolute differences between the predicted values and the actual observed values. The "absolute difference" means that the sign of the error (whether the prediction was too high or too low) is disregarded, and only the magnitude is considered. MAE treats all errors linearly; an error of 10 units has exactly ten tim....