Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely on the assumption of exchangeability. This assumption is fundamentally violated in time series data, where temporal dependence and distributional shifts are pervasive. As a result, classical split-conformal methods may yield prediction intervals that fail to maintain nominal validity. This review unifies recent advances in conformal forecasting methods specifically designed to address nonexchangeable data. We first present a theoretical foundation, deriving finite-sample guarantees for split-conformal prediction under mild weak-dependence conditions. We then survey and classify state-of-the-art approaches that mitigate serial dependence by reweighting calibration data, dynamically updating residual distributions, or adaptively tuning target coverage levels in real time. Finally, we present a comprehensive simulation study that compares these techniques in terms of empirical coverage, interval width, and computational cost, highlighting practical trade-offs and open research directions.

翻译：共形预测是一种用于不确定性量化的强大后处理框架，提供无分布覆盖保证。然而，这些保证关键依赖于可交换性假设。在时间序列数据中，该假设从根本上被违背，因为时间依赖性和分布偏移普遍存在。因此，经典的分割共形方法可能产生无法维持名义有效性的预测区间。本文综述了专门针对非可交换数据设计的共形预测方法的最新进展。我们首先建立理论基础，在温和的弱依赖条件下推导分割共形预测的有限样本保证。随后，我们系统梳理并分类了通过重新加权校准数据、动态更新残差分布或实时自适应调整目标覆盖水平来缓解序列依赖性的前沿方法。最后，我们通过一项全面的模拟研究，从经验覆盖度、区间宽度和计算成本等方面比较这些技术，突出实际权衡与开放研究方向。