将分阶段模型的不确定性分解，精准定位误差来源。
Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling
- 分阶段拆解预测残差，识别各环节贡献的不确定性
- 在供应链与股市数据上，覆盖率达95%以上且更稳健
- 适合需要可解释性与鲁棒性诊断的工业级建模场景
置信预测在极少假设下提供有限样本覆盖率保证。然而现有方法将整个建模过程视为黑箱，忽视了模块化结构带来的机会。本文提出一种针对两阶段序列模型的置信预测框架，上游预测器生成中间表示供下游模型使用。通过将整体预测残差分解为阶段特异性分量，我们的方法使从业者能够将不确定性归因于具体流程阶段。我们设计了一种基于族错误率（FWER）控制的风险可控参数选择程序，以校准各阶段缩放参数，并引入适应非平稳环境的扩展方法。在合成分布偏移及真实世界供应链和股票市场数据上的实验表明，相比标准置信方法，该方法在结构性、阶段性偏移下仍能保持更高覆盖率（>95%），并准确识别阶段误差贡献。该框架具备诊断优势与更强鲁棒性，是标准置信方法所缺乏的。
原文摘要 · Abstract (English)
Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit and understand modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practitioners to attribute uncertainty to specific pipeline stages. We develop a risk-controlled parameter selection procedure using family-wise error rate (FWER) control to calibrate stage-wise scaling parameters, and introduce an adaptive extension for non-stationary settings. Experiments on synthetic distribution shifts, as well as real-world supply chain and stock market data, demonstrate that our approach improves coverage under structural, stage-wise shifts compared to standard conformal methods, while identifying stage-wise error contribution. This framework offers diagnostic advantages and robust coverage that standard conformal methods lack.
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