Conformal prediction (CP) is a robust framework for distribution-free uncertainty quantification, but it requires exchangeable data to ensure valid prediction sets at a user-specified significance level. When this assumption is violated, as in time-series or other structured data, the validity guarantees of CP no longer hold. Adaptive conformal inference (ACI) was introduced to address this limitation by adjusting the significance level dynamically, ensuring finite-sample coverage guarantees even for non-exchangeable data. In this paper, we show that ACI does not require the use of conformal predictors; instead, it can be implemented with the more general confidence predictors, which are computationally simpler and still maintain the crucial property of nested prediction sets. Through experiments on synthetic and real-world data, we demonstrate that confidence predictors can perform comparably to, or even better than, conformal predictors, particularly in terms of computational efficiency. These findings suggest that confidence predictors represent a viable and efficient alternative to conformal predictors in non-exchangeable data settings, although further studies are needed to identify when one method is superior.

翻译：共形预测（CP）是一种用于无分布不确定性量化的鲁棒框架，但其要求数据满足可交换性，以确保在用户指定的显著性水平下产生有效的预测集。当这一假设被违反时（如时间序列或其他结构化数据），CP的有效性保证不再成立。自适应共形推断（ACI）通过动态调整显著性水平来解决这一局限，即使对于非可交换数据也能确保有限样本覆盖保证。本文证明，ACI并不需要使用共形预测器；相反，它可以基于更一般的置信度预测器实现，后者计算更简单且仍保持嵌套预测集的关键性质。通过在合成数据和真实数据上的实验，我们证明置信度预测器的性能可与共形预测器相媲美甚至更优，尤其在计算效率方面。这些发现表明，在非可交换数据场景中，置信度预测器是共形预测器的一种可行且高效的替代方案，尽管仍需进一步研究以确定何种情况下某一方法更具优势。