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Cited by 130Traffic Prediction and Management TechniquesAdvanced Graph Neural NetworksTime Series Analysis and Forecasting

DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models

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Wen, Haomin, Lin, Youfang, Xia, Yutong, Wan, Huaiyu, Wen, Qingsong, Zimmermann, Roger, Liang, Yuxuan·2023·Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
DOI 10.1145/3589132.3625614
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Abstract

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic uncertainties within STG data, which cripples their practicality in downstream tasks for decision-making. To this end, this paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex ST dependencies. In this study, we present the first attempt to generalize the popular de-noising diffusion probabilistic models to STGs, leading to a novel non-autoregressive framework called DiffSTG, along with the first denoising network UGnet for STG in the framework. Our approach combines the spatio-temporal learning capabilities of STGNNs with the uncertainty measurements of diffusion models. Extensive experiments validate that DiffSTG reduces the Continuous Ranked Probability Score (CRPS) by 4%-14%, and Root Mean Squared Error (RMSE) by 2%-7% over existing methods on three real-world datasets.

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Cited by

Sorted by citations

Authors
Wen, H., Lin, Y., Xia, Y., Wan, H., Wen, Q., Zimmermann, R., & Liang, Y.
Affiliations
  • Beijing Jiaotong University (CN)
  • National University of Singapore (SG)
  • Alibaba DAMO Academy (China) (CN)
  • The Hong Kong University of Science and Technology (Guangzhou) (CN)
ORCID
Venue
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
Publisher
ACM
Published
2023-11-13
Pages
1-12
DOI
10.1145/3589132.3625614
OpenAlex
W4390100390
Cited by
130
References
10
Citations by year
2026: 39 · 2025: 60 · 2024: 27 · 2023: 4
FWCI
79.37
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
Guangzhou Municiple Science and Technology Project (2023A03J0011)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
probabilistic forecasting, uncertainty quantification, denoising diffusion probabilistic models, spatio-temporal graph neural networks, continuous ranked probability score

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