Nested Spatio-Temporal Time Series Forecasting
YingHao Ai, Yukai Zhou, Ruoxi Jiang, Junyi An, Chao Qu, Zhijian Zhou, Shiyu Wang, Fenglei Cao
Abstract
Spatio-temporal forecasting is critical for real-world applications like traffic management, yet capturing complex interactions under high-noise conditions remains challenging. While current methods have shown improved accuracy using spatial physical priors, they often struggle with evolving temporal correlations and systematic errors. In this work, we propose a nested forecasting framework that couples future macro-level regional trends with micro-level historical observations, enabling top-down guidance from abstract future representations for fine-grained forecasting. Specifically, we construct semantically coherent regions via spectral clustering and design a progressive coarse-to-fine predictor to inject macro-dynamics into node-level forecasting. Extensive experiments on multiple real-world datasets demonstrate that our method consistently outperforms state-of-the-art baselines, validating the effectiveness of future macro-guided nested forecasting.
BibTeX
@inproceedings{
ai2026nested,
title={Nested Spatio-Temporal Time Series Forecasting},
author={YingHao Ai and Yukai Zhou and Ruoxi Jiang and Junyi An and Chao Qu and Zhijian Zhou and Shiyu Wang and Fenglei Cao and Zenglin Xu and Furao Shen and Yuan Qi},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=x5xKTcGemE}
}