发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统探索深度序列模型在时间序列保形预测中的应用，提出三种方法并给出理论保证与实验验证。
AI 中文摘要
深度学习在时间序列预测方面的最新进展，放大了对可靠不确定性量化的需求。保形预测作为一种无分布框架，因构建具有覆盖保证的预测区间而受到关注。然而，其覆盖保证依赖于数据可交换性，这一假设在时间序列中通常不成立。积极的研究聚焦于开发克服这一局限性的时间序列保形预测方法。尽管深度序列模型（如循环神经网络和Transformer）常被用于时间序列的保形预测，但有限的工作系统性地研究了如何将深度序列模型应用于时间序列的保形预测。在本工作中，我们通过三种方法系统性地研究了深度序列模型在时间序列保形预测中的应用：条件分位数回归、条件分位数函数估计和局部保形预测。我们提供了理论分析，在适当假设下为所有三种方法建立了渐近条件覆盖保证。通过在真实世界数据集上的综合实验，我们证明了将深度序列模型用于时间序列保形预测的有效性。
英文摘要
Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has gained attention as a distribution-free framework for constructing prediction intervals with coverage guarantees. However, its coverage guarantees rely on data exchangeability, an assumption generally violated in time series. Active research has focused on developing conformal prediction methods for time series that overcome this limitation. While deep sequence models, such as recurrent neural networks and Transformers, have often been used in conformal prediction for time series, limited work has systematically studied how deep sequence models can be utilized in conformal prediction for time series. In this work, we systematically investigate the use of deep sequence models in conformal prediction for time series through three approaches: conditional quantile regression, conditional quantile function estimation, and localized conformal prediction. We provide a theoretical analysis establishing asymptotic conditional coverage guarantees for all three approaches under suitable assumptions. Through comprehensive experiments on real-world datasets, we demonstrate the effectiveness of leveraging deep sequence models into conformal prediction for time series.
