用流模型提升多维时间序列预测的不确定性量化精度
Flow-based Conformal Prediction for Multi-dimensional Time Series
- 引入无分类器引导的流模型，解决时间序列依赖性问题
- 在真实数据集上预测集大小显著更小，且覆盖率达标
- 适合需要高精度不确定性估计的工业时间序列场景
时间序列预测广泛应用于多个科学领域。随着黑箱机器学习模型在时间序列预测中的普及，不确定性量化变得至关重要。尽管分布外检测方法受到关注，但时间序列的分布外检测面临两大挑战：(1) 利用观测值和非一致性得分之间的相关性以克服交换性假设；(2) 构建多维结果的预测集。为此，我们提出一种基于流模型的新型时间序列分布外检测方法，采用无分类器引导。通过建立精确的非渐近边际覆盖和有限样本条件覆盖界，保证了覆盖可靠性。在真实时间序列数据集上的评估表明，该方法生成的预测集显著小于现有方法，同时保持目标覆盖率。
原文摘要 · Abstract (English)
Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for time series prediction highlight the critical need for uncertainty quantification. While conformal prediction has gained attention as a reliable uncertainty quantification method, conformal prediction for time series faces two key challenges: (1) \textbf{leveraging correlations in observations and non-conformity scores to overcome the exchangeability assumption}, and (2) \textbf{constructing prediction sets for multi-dimensional outcomes}. To address these challenges, we propose a novel conformal prediction method for time series using flow with classifier-free guidance. We provide coverage guarantees by establishing exact non-asymptotic marginal coverage and a finite-sample bound on conditional coverage for the proposed method. Evaluations on real-world time series datasets demonstrate that our method constructs significantly smaller prediction sets than existing conformal prediction methods, maintaining target coverage.
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