量化波动聚类下的神经网络不确定性

Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi
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引用次数: 0

摘要

具有时变方差的时间序列对不确定性量化(UQ)方法提出了独特的挑战。时变方差,如金融时间序列中的波动性集群,会导致预测不确定性与预测误差之间的巨大不匹配。基于神经网络 UQ 文献的最新进展,我们将深度证据回归和深度集合扩展并简化为一个统一的框架,以处理存在波动率聚类情况下的 UQ。我们表明,规模混合分布是正负伽马先验的一个简单替代方案,它能提供有利的复杂性-准确性权衡。为了说明我们提出的方法的性能,我们将其应用于两组表现出波动性聚类的金融时间序列:加密货币和美国股票。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying neural network uncertainty under volatility clustering
Time-series with time-varying variance pose a unique challenge to uncertainty quantification (UQ) methods. Time-varying variance, such as volatility clustering as seen in financial time-series, can lead to large mismatch between predicted uncertainty and forecast error. Building on recent advances in neural network UQ literature, we extend and simplify Deep Evidential Regression and Deep Ensembles into a unified framework to deal with UQ under the presence of volatility clustering. We show that a Scale Mixture Distribution is a simpler alternative to the Normal-Inverse-Gamma prior that provides favorable complexity-accuracy trade-off. To illustrate the performance of our proposed approach, we apply it to two sets of financial time-series exhibiting volatility clustering: cryptocurrencies and U.S. equities.
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