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[2509.05779] Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting
Summary
This paper presents ExoST, a novel framework for spatio-temporal forecasting that effectively incorporates exogenous variables, addressing challenges of variable effects and data imbalance.
Why It Matters
The integration of exogenous variables in spatio-temporal forecasting is crucial for improving predictive accuracy in dynamic systems. This research fills a gap in existing methodologies, offering a systematic approach that enhances model robustness and efficiency, which is vital for applications in various fields such as environmental monitoring and urban planning.
Key Takeaways
- ExoST framework addresses the integration of exogenous variables in spatio-temporal forecasting.
- The approach utilizes a 'select, then balance' paradigm to manage variable effects.
- A latent space gated expert module dynamically selects relevant signals from exogenous data.
- The siamese dual-branch architecture captures dynamic patterns for improved forecasting.
- Extensive experiments validate the effectiveness and efficiency of ExoST across real-world datasets.
Computer Science > Machine Learning arXiv:2509.05779 (cs) [Submitted on 6 Sep 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting Authors:Wei Chen, Yuqian Wu, Yuanshao Zhu, Xixuan Hao, Shiyu Wang, Xiaofang Zhou, Yuxuan Liang View a PDF of the paper titled Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecasting, by Wei Chen and 6 other authors View PDF HTML (experimental) Abstract:Spatio-temporal (ST) forecasting is critical for dynamic systems, yet existing methods predominantly rely on modeling a limited set of observed target variables. In this paper, we present the first systematic exploration of exogenous variable modeling for ST forecasting, a topic long overlooked in this field. We identify two core challenges in integrating exogenous variables: the inconsistent effects of distinct variables on the target system and the imbalance effects between historical and future data. To address these, we propose ExoST, a simple yet effective exogenous variable modeling general framework highly compatible with existing ST backbones that follows a "select, then balance" paradigm. Specifically, we design a latent space gated expert module to dynamically select and recompose salient signals from fused exogenous information. Furthermore, a siamese dual-branch backbone architecture captures dynamic patterns from the recomposed past and future represe...