This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR} that enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable. Moreover, the resulting prediction intervals, besides being empirically valid along the forecast horizon, do not neglect heteroscedasticity. AEnbMIMOCQR is designed to be robust to distribution shifts, which means that its prediction intervals remain reliable over an unlimited period of time, without entailing retraining or imposing unrealistic strict assumptions on the data-generating process. Through methodically experimentation, we demonstrate that our approach outperforms other competitive methods on both real-world and synthetic datasets. The code used in the experimental part and a tutorial on how to use AEnbMIMOCQR can be found at the following GitHub repository: https://github.com/Quilograma/AEnbMIMOCQR.

翻译：本文提出了一种新颖的模型无关算法，称为自适应集成批量多输入多输出共形分位数回归（AEnbMIMOCQR），该算法使预测者能够以无分布方式生成具有固定预设误覆盖率的多步超前预测区间。我们的方法基于共形预测原理，但无需数据分割，即使在数据不可交换的情况下也能提供接近精确的覆盖。此外，所得到的预测区间除了在整个预测视界内经验有效外，还不会忽略异方差性。AEnbMIMOCQR设计为对分布漂移具有鲁棒性，这意味着其预测区间可在无限长的时间段内保持可靠，而无需重新训练或对数据生成过程施加不切实际的严格假设。通过系统性实验，我们证明该方法在真实数据集和合成数据集上均优于其他竞争方法。实验部分使用的代码及AEnbMIMOCQR使用教程可访问以下GitHub仓库获取：https://github.com/Quilograma/AEnbMIMOCQR。