Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful. In applications such as wildfires, crimes, and traffic jams, timely predictions are vital for safeguarding human life and property. Consequently, finding a balance between accuracy and timeliness is crucial. In this paper, we propose a spatio-temporal early prediction model based on Multi-Objective reinforcement learning that can either implement an optimal policy given a preference or infer the preference based on a small number of samples. The model addresses two primary challenges: 1) enhancing the accuracy of early predictions and 2) providing the optimal policy for determining the most suitable prediction time for each area. Our method demonstrates superior performance on three large-scale real-world datasets, surpassing existing methods in early spatio-temporal prediction tasks.

翻译：准确性与及时性在预测任务中常常是相互冲突的目标。过早的预测可能导致较高的误报率，而延迟预测以收集更多信息则可能使预测结果因过迟而失去实用价值。在野火、犯罪和交通拥堵等应用场景中，及时预测对于保障人类生命财产安全至关重要。因此，在准确性与及时性之间寻求平衡具有关键意义。本文提出一种基于多目标强化学习的时空早期预测模型，该模型既能根据给定偏好实施最优策略，也能基于少量样本推断偏好。该模型主要解决两大挑战：1) 提升早期预测的准确性；2) 为每个区域确定最适预测时间提供最优策略。在三个大规模真实数据集上的实验结果表明，本文方法在早期时空预测任务中优于现有方法。