  Forecast combination is widely recognized as a preferred strategy over
forecast selection due to its ability to mitigate the uncertainty associated
with identifying a single "best" forecast. Nonetheless, sophisticated
combinations are often empirically dominated by simple averaging, which is
commonly attributed to the weight estimation error. The issue becomes more
problematic when dealing with a forecast pool containing a large number of
individual forecasts. In this paper, we propose a new forecast trimming
algorithm to identify an optimal subset from the original forecast pool for
forecast combination tasks. In contrast to existing approaches, our proposed
algorithm simultaneously takes into account the robustness, accuracy and
diversity issues of the forecast pool, rather than isolating each one of these
issues. We also develop five forecast trimming algorithms as benchmarks,
including one trimming-free algorithm and several trimming algorithms that
isolate each one of the three key issues. Experimental results show that our
algorithm achieves superior forecasting performance in general in terms of both
point forecasts and prediction intervals. Nevertheless, we argue that diversity
does not always have to be addressed in forecast trimming. Based on the
results, we offer some practical guidelines on the selection of forecast
trimming algorithms for a target series.
