We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate substantially improved practical properties: in particular, when the distribution is stable, the coverage is close to the desired level for every time point, not just on average over the observed sequence.

翻译：本文提出了一种采用衰减步长的在线共形预测方法。与既有方法类似，本方法对任意序列具有回顾性的覆盖保证。然而，区别于先前方法，我们能够在总体分位数存在时对其进行同步估计。理论与实验结果表明，本方法具有显著提升的实用特性：特别地，当数据分布保持稳定时，每个时间点的覆盖概率均接近期望水平，而不仅是在观测序列上的平均覆盖效果。