提出在线局部化置信预测，提升时间序列预测的准确性与效率。
Online Localized Conformal Prediction
- 结合在线学习与协变量局部化，动态适应数据异质性。
- 在真实数据和模拟实验中，置信集宽度更窄且长期覆盖有效。
- 适合需要高精度不确定性估计的在线预测场景。
置信预测是一种为一般模型提供有效不确定性量化的方法，适用于交换性数据。然而，在在线学习和时间序列设置中，交换性不成立。现有的在线置信方法（如自适应置信推断，ACI）虽能实现长期有效性，但在协变量异质性下仍效率低下，因其依赖全局校准。本文提出在线局部化置信预测（OLCP），融合在线适应与协变量相关局部化，更好反映数据异质性。为降低对局部化带宽的敏感性，进一步提出OLCP-Hedge，将带宽选择建模为受限在线凸优化框架下的专家聚合问题。重要的是，本文为两种算法提供了覆盖保证，并通过模拟和真实数据实验表明，所提方法在保持长期覆盖有效性的前提下，预测集宽度显著优于现有基线。
原文摘要 · Abstract (English)
Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online learning and time-series settings, exchangeability is not satisfied. Existing online conformal methods, such as adaptive conformal inference (ACI), can achieve long-run validity, yet they remain inefficient under covariate heterogeneity because they rely on global calibration. We propose \emph{Online Localized Conformal Prediction (OLCP)}, which combines online adaptation with covariate-dependent localization to better reflect heterogeneity. To reduce sensitivity to the localization bandwidth, we further develop \emph{OLCP-Hedge}, which performs bandwidth selection as an online expert aggregation problem using a constrained online convex optimization framework. Importantly, we provide coverage guarantees for both algorithms and demonstrate through simulations and real-data experiments that the proposed methods attain valid long-run coverage with narrower prediction sets than existing baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。