提出在线多步时间序列预测的新方法，保证有限样本覆盖率。
Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online
- 动态调整显著性水平，适应非交换数据的在线预测
- 每一步预测和整体误差率均具备有限样本覆盖率
- 可灵活设置不同步长的误差率与学习率，平衡精度与覆盖
本文旨在将经典的自适应共形推断（ACI）算法拓展至在线多步时间序列预测场景，以实现有限样本下的覆盖率保证。ACI 能动态调整显著性水平，即使在非交换数据下仍具有有限样本覆盖率保障。所提出的多步预测 ACI 算法在每一步预测及整体误差率上均继承此性质。该算法可为不同预测步长设置不同的目标误差率与学习率，本文通过基于共形化岭回归的多输入多输出预测实例说明其应用效果，展示了不同步长下变量设置对预测区间宽度与覆盖率的影响，表明可在效率与覆盖之间取得平衡。
原文摘要 · Abstract (English)
The aim of this paper is to propose an adaptation of the well known adaptive conformal inference (ACI) algorithm to achieve finite-sample coverage guarantees in multi-step ahead time-series forecasting in the online setting. ACI dynamically adjusts significance levels, and comes with finite-sample guarantees on coverage, even for non-exchangeable data. Our multi-step ahead ACI procedure inherits these guarantees at each prediction step, as well as for the overall error rate. The multi-step ahead ACI algorithm can be used with different target error and learning rates at different prediction steps, which is illustrated in our numerical examples, where we employ a version of the confromalised ridge regression algorithm, adapted to multi-input multi-output forecasting. The examples serve to show how the method works in practice, illustrating the effect of variable target error and learning rates for different prediction steps, which suggests that a balance may be struck between efficiency (interval width) and coverage.t
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