arXiv：cs.LG· Junghwan Lee, Jonghyeok Lee, Yao Xie·· 3 小时前AI 评分33
深度序列模型如何用于时间序列的共形预测
Conformal Prediction for Time Series with Deep Sequence Models
AI 导读
研究系统考察了深度序列模型在时间序列共形预测中的三种用法：条件分位数回归、条件分位数函数估计与局部化共形预测，并给出三者渐近条件覆盖保证的理论分析。在真实数据集上的实验显示，将 RNN、Transformer 等深度序列模型引入共形预测可有效提升时间序列的不确定性量化。
正文
Abstract: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.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02357 [stat.ML] |
| (or arXiv:2610.02357v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02357 arXiv-issued DOI via DataCite |
Submission history
From: Junghwan Lee [view email]
[v1]
Thu, 1 Oct 2026 18:34:49 UTC (1,488 KB)
来源：arXiv：cs.LG · arxiv.org