一种自适应生成时间序列预测区间的高效校准框架
Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series
- 融合概率预测器与无得分依赖校准，动态生成可信区间
- 在真实与合成数据上均实现高精度且覆盖率达标
- 模块化设计适合快速测试不同组合，适合高风险场景
我们提出分布感知的共形预测（DCP），一种统一框架，将蒙特卡洛丢弃、深度集成和分位数回归等概率预测器与无得分依赖的共形校准相结合，生成有效且高效的预测区间。通过数值反解方法构建区间边界，DCP 可适配任意分布生成预测器与非一致性评分组合。在合成及真实时间序列数据上的基准测试表明，DCP 能在不同不确定性环境下自适应校准预测区间。关键优势在于其模块化设计，支持即插即用的预测器-评分组合实验，并通过新提出的改进温克勒评分量化有效性与效率，明确惩罚覆盖不足。相较于共形分位数回归和共形蒙特卡洛方法，DCP 实现了泛化与扩展，为动态环境与高风险应用中的不确定性量化奠定基础。
原文摘要 · Abstract (English)
We present Distribution-aware Conformal Prediction (DCP), a unified framework integrating probabilistic predictors like Monte Carlo dropout, deep ensembles, and quantile regression with score-agnostic conformal calibration to produce valid and efficient prediction intervals. Leveraging a numerical inversion approach to construct interval bounds, DCP accommodates arbitrary combinations of distribution generating predictors and nonconformity scores. Benchmark analysis on synthetic and real-world time series data demonstrate DCP's ability to adaptively calibrate prediction intervals under varying uncertainty regimes. Crucially, DCP's modular design facilitates plug-and-play experimentation with different predictor-score pairings, quantitatively supported by a newly introduced modified Winkler score that balances validity and efficiency by explicitly penalizing undercoverage. While DCP generalizes and extends existing approaches like Conformalized Quantile Regression and Conformalized Monte Carlo, its modular design allows further extensions, setting a foundation for advancing uncertainty quantification in dynamic environments and high-risk applications.
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