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，称为自适应集成批处理多输入多输出共形分位数回归（AEnbMIMOCQR），该算法使预测器能够以无分布方式生成具有固定预设错误覆盖率的超前多步预测区间。我们的方法基于共形预测原理，但无需数据分割，即使数据不可交换也能提供接近精确的覆盖率。此外，所得预测区间在预测范围内不仅经验有效，而且未忽略异方差性。AEnbMIMOCQR设计为对分布偏移具有鲁棒性，这意味着其预测区间可在无限时间范围内保持可靠，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使用教程可在以下GitHub仓库获取：https://github.com/Quilograma/AEnbMIMOCQR。