面向时间序列的检索修正共形预测
Retrieval-Corrected Conformal Prediction for Time Series
- Ulsan National Institute of Science and Technology（蔚山科学技术院）
- LinqAlpha（林克阿尔法）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出RCCP方法，通过选择相似过去残差为局部证据并修正检索导致的覆盖率误差，在时间序列预测中实现目标覆盖率、低失误与开销，提升不确定性量化效果。
AI中文摘要：
共形预测（CP）为固定预测器提供无分布的预测区间，但其标准校准程序对时间序列数据往往效率低下，因为时间序列的预测误差存在时间依赖性，且会随时间和运行条件变化。近期的时间序列共形预测方法利用近期、加权或局部化的残差改进局部校准，但局部校准仍可能不够直接，因为广泛的残差加权或额外的适配程序可能会稀释与当前预测最相关的证据。这一问题促使了一种简单的检索修正策略，该策略选择相似的过去残差作为局部证据，然后修正由检索导致的覆盖率误差。本文提出了检索修正共形预测（RCCP），一种用于时间序列预测区间的检索增强校准方法。RCCP从检索到的单侧残差构建非对称区间，并通过标量共形修正校准其归一化检索误差，因此检索提供局部残差证据，而共形修正确定覆盖率所需的最终尺度。我们基于归一化检索误差分布的稳定性提供了覆盖率间隙界。在标准基准和骨干预测器上，RCCP在所有设置中均达到目标覆盖率，且取得最低的温克勒分数，同时严重失误更少。RCCP还实现了低校准和推理开销，表明检索修正校准是时间序列预测中不确定性量化的有效且可扩展的方法。代码可在此处的URL获取。
英文摘要：
Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions. Recent time series CP methods improve local calibration using recent, weighted, or localized residuals. Yet local calibration can remain indirect, since broad residual weighting or additional adaptation procedures may dilute the evidence most relevant to the current prediction. This motivates a simple retrieval and correction strategy that selects similar past residuals as local evidence and then corrects the coverage error left by retrieval. In this paper, we propose Retrieval--Corrected Conformal Prediction (RCCP), a retrieval-augmented calibration method for time series prediction intervals. RCCP builds an asymmetric interval from retrieved one-sided residuals and calibrates its normalized retrieval error with a scalar conformal correction. Thus, retrieval provides local residual evidence, while conformal correction determines the final scale needed for coverage. We provide a coverage-gap bound based on the stability of the normalized retrieval error distribution. Across standard benchmarks and backbone forecasters, RCCP attains the target coverage in every setting and achieves the lowest Winkler scores, with fewer severe misses. RCCP also achieves low calibration and inference overhead, showing that retrieval-corrected calibration is an effective and scalable approach to uncertainty quantification in time series forecasting. Code is available at https://github.com/jinsaaang/rccp.
