Simple robust averages of forecasts: Some empirical results

Abstract

An extensive body of literature has shown that combining forecasts can improve forecast accuracy, and that a simple average of the forecasts (the mean) often does better than more complex combining schemes. The fact that the mean is sensitive to extreme values suggests that deleting such values or reducing their extremity might be worthwhile. We study the performance of two simple robust methods, trimmed and Winsorized means, which are easy to use and understand. For the data sets we consider, they provide forecasts which are slightly more accurate than the mean, and reduce the risk of high errors. Our results suggest that moderate trimming of 10–30% or Winsorizing of 15–45% of the forecasts can provide improved combined forecasts, with more trimming or Winsorizing being indicated when there is more variability among the individual forecasts. There are some differences in the performance of the trimmed and Winsorized means, but overall such differences are not large.