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 at https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model.
Cite
@article{arxiv.2404.18886,
title = {A Survey on Diffusion Models for Time Series and Spatio-Temporal Data},
author = {Yiyuan Yang and Ming Jin and Haomin Wen and Chaoli Zhang and Yuxuan Liang and Lintao Ma and Yi Wang and Chenghao Liu and Bin Yang and Zenglin Xu and Shirui Pan and Qingsong Wen},
journal= {arXiv preprint arXiv:2404.18886},
year = {2025}
}
Comments
Accepted by ACM Computing Surveys; 37 pages; Github Repo: https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Model