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.

翻译：预测组合因其能够减轻识别单一“最佳”预测所伴随的不确定性，被广泛认为是一种优于预测选择的策略。然而，复杂的组合方法在实证中常常被简单平均法所超越，这通常归因于权重估计误差。当处理包含大量个体预测的预测池时，这一问题变得更加棘手。本文提出了一种新的预测修剪算法，用于在预测组合任务中从原始预测池中识别出一个最优子集。与现有方法不同，我们提出的算法同时考虑了预测池的稳健性、准确性和多样性问题，而不是孤立地处理这些问题。我们还开发了五种预测修剪算法作为基准，包括一种无修剪算法以及几种分别孤立处理三个关键问题之一的修剪算法。实验结果表明，无论是在点预测还是预测区间方面，我们的算法总体上均取得了更优的预测性能。然而，我们认为在预测修剪中并非总是必须处理多样性问题。基于实验结果，我们为针对目标序列选择预测修剪算法提供了一些实用指南。