STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning
It addresses timely forecasting for critical applications, but appears incremental as it builds on existing reinforcement learning methods for spatio-temporal tasks.
The paper tackles the trade-off between accuracy and timeliness in spatio-temporal forecasting for applications like wildfires and traffic jams, proposing a multi-objective reinforcement learning model that achieves superior performance on three real-world datasets.
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.