Towards Foundation Model for Spatiotemporal Data Analysis
Name
3748777.3748811.pdf
Size
1.31 MB
Format
Adobe PDF
Checksum (MD5)
8a994325ad5bc0a3b0dfb0c2c59587f2
Author(s)
Wu, Yuankai
Chen, Xinyu
Zhuang, Dingyi
Date Issued
October 14, 2025
Publisher
ACM|19th International Symposium on Spatial and Temporal Data
Citation
Yuankai Wu, Xinyu Chen, and Dingyi Zhuang. 2025. Towards Foundation Model for Spatiotemporal Data Analysis. In Proceedings of the 19th International Symposium on Spatial and Temporal Data (SSTD '25). Association for Computing Machinery, New York, NY, USA, 192–196.
Version
Final published version
Abstract
Spatiotemporal data modeling has long been a fundamental task
across disciplines such as climate & environmental science, and
transportation engineering. A typical goal is to estimate unknown
information at specific spatiotemporal points based on partially
observed data—for example, interpolating weather conditions at
unmeasured locations, reconstructing missing historical records, or
forecasting the future trajectories of financial markets. These are
all core tasks within the broader scope of spatiotemporal modeling.
This tutorial (1 hours) introduces a cohesive view of spatiotemporal data modeling, tracing the evolution from traditional statistical
approaches to modern deep learning paradigms. We begin by revisiting Kriging and time series decomposition to highlight the essential
assumptions and strengths of these classical methods. Next, we explore low-rank matrix and tensor completion techniques, which
leverage the structured patterns of spatiotemporal data. We then
elaborate on spatiotemporal graph neural networks, which characterize complex dependencies by integrating graph structures with
dynamic temporal features. Finally, we discuss recent advances in
applying large foundation models to spatiotemporal tasks, including their capabilities and current limitations.
Throughout the tutorial, we emphasize how lessons from traditional methods—such as the importance of locality, periodicity, and
smoothness priors—can inspire new directions for developing and
fine-tuning foundation models in the spatiotemporal domain. We
conclude by outlining key challenges and opportunities in bridging
classical wisdom with emerging AI capabilities.
Description
SSTD ’25, Osaka, Japan
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3748777.3748811
