Despite being easy to calculate and understand, exponential smoothing is excessively affected by outliers and thus performs poorly when the data has a non-Gaussian probability distribution, such as a mixture or fat-tailed distribution. Is there a robust alternative or variation?
Regression also has the same problems with outliers so would not be an alternative, nor would forecasting methods that include regression such as ARIMA or GARCH.
I imagine that another option might be somehow pre-treating the data to make it more Gaussian. Supplementary question, would taking logs do this?
Trying to adapt the idea of, for example, the trimmed mean for exponentially weighted moving averages would be complicated and probably work poorly.