Quantile-adaptive probabilistic forecast combining

Yu Ke

doi:10.1016/j.ejor.2025.11.031

2025-12-03

PRE

Yu Ke

James W. Taylor

Xiaochun Meng

DOI：10.1016/j.ejor.2025.11.031

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摘要

En 中文

结合累积概率分布（CDFs）的预测可以聚合可用信息以提高准确性。线性意见池（linear opinion pool）是常用的方法，但它可能导致过度分散的分布预测。一种替代方法，可导致更低分散，是平均CDF的分位数，这可以被视为水平CDF平均，而线性意见池中的概率平均则被视为垂直平均。实证结果表明，水平和垂直平均对于CDF的不同部分各有优势。例如，一种方法可能对尾部分位数更优，而另一种方法对中央分位数更优。为解决此问题，我们开发了一种方法，在CDF上实现垂直和水平平均之间的过渡。该方法与角平均（angular averaging）相关，这是一种近期提出的沿一定角度的直线进行聚合的方法。我们的新方法沿具有平滑过渡斜率的直线进行平均。该方法在分位数自适应的意义上，其直线的斜率随分位数变化，或等价地，随概率变化。我们设定这些直线从少量固定点发出，这些点即为方法的参数。将直线视为射线，我们称该方法为径向平均（radial averaging）。我们的理论结果表明，该方法具有生成比水平平均更锐利、比垂直平均更不锐利的CDF预测的灵活性。我们的实证结果支持这一新方法。

期刊

European Journal of Operational Research

IF:

论文数:

2.2W

被引数:

6.4W

机构

university of bath

学者数:

1.1W

论文数: 1.3W

被引数: 13

university of oxford

学者数:

9.8W

论文数: 8.6W

被引数: 137

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