论文标题
另一个查看组合的预测修剪：鲁棒性，准确性和多样性
Another look at forecast trimming for combinations: robustness, accuracy and diversity
论文作者
论文摘要
预测组合被广泛认为是优先策略，而不是预测选择，因为它可以减轻与识别单个“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以简单的平均为主，这通常归因于权重估计误差。处理包含大量个人预测的预测池时，这个问题变得更加有问题。在本文中，我们提出了一种新的预测修剪算法，以确定原始预测池中的最佳子集，以进行预测组合任务。与现有方法相反，我们提出的算法同时考虑了预测池的鲁棒性，准确性和多样性问题，而不是隔离这些问题中的每一个。我们还开发了五种预测算法作为基准测试，包括一种无修剪算法和几种修剪算法，可隔离三个关键问题中的每一个。实验结果表明，就点预测和预测间隔而言，我们的算法一般都达到了卓越的预测性能。然而，我们认为在预测修剪中不一定要解决多样性。根据结果，我们提供了一些有关目标系列预测修剪算法的实用准则。
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.
