  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
forecasting are vital for safeguarding human life and property. Consequently,
finding a balance between accuracy and timeliness is crucial. In this paper, we
propose an early spatio-temporal forecasting 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
forecasting 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 forecasting tasks.
