A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

Yiyuan Yang; Ming Jin; Haomin Wen; Chaoli Zhang; Yuxuan Liang; Lintao Ma; Yi Wang; Chenghao Liu; Bin Yang; Zenglin Xu; Shirui Pan; Qingsong Wen

arXiv · 2404.18886

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

Yiyuan Yang·Ming Jin·Haomin Wen·Chaoli Zhang·Yuxuan Liang·Lintao Ma·Yi Wang·Chenghao Liu·Bin Yang·Zenglin Xu·Shirui Pan·Qingsong Wen

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Abstract

Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across diverse fields such as healthcare, recommendation, climate, energy, audio, and traffic. By separating applications for time series and spatio-temporal data, we offer a structured perspective on model category, task type, data modality, and practical application domain. This study aims to provide a solid foundation for researchers and practitioners, inspiring future innovations that tackle traditional challenges and foster novel solutions in diffusion model-based data mining tasks and applications. For more detailed information, we have open-sourced a repository at https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model.

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Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Shirui Pan, Qingsong Wen. 2025-12-06. A Survey on Diffusion Models for Time Series and Spatio-Temporal Data. https://arxiv.org/abs/2404.18886

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