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.
Link to arXiv version here
Shao, W., Kang, Y., Peng, Z., Xiao, X., Wang, L., Yang, Y., & Salim, F. D. (2024, August). STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2618-2627).
@inproceedings{shao2024stemo,
title={STEMO: Early Spatio-temporal Forecasting with Multi-Objective Reinforcement Learning},
author={Shao, Wei and Kang, Yufan and Peng, Ziyan and Xiao, Xiao and Wang, Lei and Yang, Yuhui and Salim, Flora D},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={2618--2627},
year={2024}
}