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.

中文翻译：

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再看组合的预测调整：稳健性、准确性和多样性

预测组合被广泛认为是优于预测选择的首选策略，因为它能够减轻与识别单个“最佳”预测相关的不确定性。尽管如此，复杂的组合通常在经验上由简单的平均主导，这通常归因于权重估计误差。在处理包含大量单个预测的预测池时，这个问题变得更加棘手。在本文中，我们提出了一种新的预测修剪算法，用于从原始预测池中识别出一个最佳子集，用于预测组合任务。与现有方法相比，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，而不是孤立这些问题中的每一个。我们还开发了五种预测修整算法作为基准，包括一种免修整算法和几种将三个关键问题中的每一个隔离开的修整算法。实验结果表明，我们的算法在点预测和预测区间方面总体上实现了优越的预测性能。尽管如此，我们认为多样性并不总是必须在预测调整中得到解决。根据结果​​，我们提供了一些关于为目标序列选择预测修剪算法的实用指南。实验结果表明，我们的算法在点预测和预测区间方面总体上实现了优越的预测性能。尽管如此，我们认为多样性并不总是必须在预测调整中得到解决。根据结果​​，我们提供了一些关于为目标序列选择预测修剪算法的实用指南。实验结果表明，我们的算法在点预测和预测区间方面总体上实现了优越的预测性能。尽管如此，我们认为多样性并不总是必须在预测调整中得到解决。根据结果​​，我们提供了一些关于为目标序列选择预测修剪算法的实用指南。