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

This paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex ST dependencies. We present the first attempt to generalize the popular denoising 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.

Paper, Code

Citation:

@misc{wen2023diffstg,
      title={DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models}, 
      author={Haomin Wen and Youfang Lin and Yutong Xia and Huaiyu Wan and Roger Zimmermann and Yuxuan Liang},
      year={2023},
      eprint={2301.13629},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}