This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods.

翻译：本文提出了一种新的共形方法，用于生成同时预测带，保证以足够高的概率覆盖新随机轨迹的整个路径。针对运动规划应用中各类物体行为可能或多或少难以预测，需要可靠不确定性估计的实际需求，我们融合了单变量和多变量时间序列在线共形预测的不同技术，以及处理回归中异方差性的思路。该解决方案既有理论依据，能够提供精确的有限样本保证，又十分有效，往往比先前方法产生更具信息量的预测。