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

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.

Affiliated Institutions

• Carnegie Mellon University US
• Fudan University CN
• Zhejiang Normal University CN
• University of Oxford GB
• Griffith University AU
• Hong Kong University of Science and Technology HK
• University of Hong Kong HK
• East China Normal University CN
• Antea Group (France) FR
• Salesforce (United States) US

Related Publications