In this paper, we consider the problem of predicting unknown targets from data. We propose Online Residual Learning (ORL), a method that combines online adaptation with offline-trained predictions. At a lower level, we employ multiple offline predictions generated before or at the beginning of the prediction horizon. We augment every offline prediction by learning their respective residual error concerning the true target state online, using the recursive least squares algorithm. At a higher level, we treat the augmented lower-level predictors as experts, adopting the Prediction with Expert Advice framework. We utilize an adaptive softmax weighting scheme to form an aggregate prediction and provide guarantees for ORL in terms of regret. We employ ORL to boost performance in the setting of online pedestrian trajectory prediction. Based on data from the Stanford Drone Dataset, we show that ORL can demonstrate best-of-both-worlds performance.

翻译：本文研究了基于数据预测未知目标的问题。我们提出了在线残差学习（ORL）方法，该方法将在线自适应与离线训练预测相结合。在底层，我们采用多个在预测时域开始前或开始时生成的离线预测。通过递归最小二乘算法在线学习每个离线预测相对于真实目标状态的残差误差，从而增强每个离线预测。在高层，我们将增强后的底层预测器视为专家，采用专家建议预测框架。我们采用自适应softmax加权方案形成聚合预测，并为ORL提供了遗憾界保证。我们将ORL应用于在线行人轨迹预测场景以提升性能。基于斯坦福无人机数据集的数据，我们证明ORL能够实现优势互补的性能表现。