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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).
| Date | Stars |
|---|---|
| 2026-07-31 | 1006 |
| 2026-08-06 | 1006 |
| 2026-08-14 | 1007 |
| 2026-08-18 | 1008 |
| 2026-08-19 | 1007 |
| 2026-08-21 | 1005 |
| 2026-09-02 | 1006 |
| 2026-09-26 | 1007 |
| 2026-10-08 | 1008 |
| 2026-10-16 | 1009 |
| 2026-10-19 | 1010 |
| 2026-10-21 | 1011 |
| 2026-10-22 | 1012 |
| 2026-10-27 | 1013 |
| 2026-11-04 | 1013 |
Today
— stars today
This week
— stars this week
This month
+6 stars this month
Momentum
0.0
growth rate 0.00%/day
# Diffusion Model for Time Series and Spatio-Temporal Data
[](https://awesome.re)
A curated list of **Diffusion Models** for **Time Series**, **SpatioTemporal Data** and **Tabular Data** with awesome resources (paper, code, application, review, survey, etc.), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge.
We will continue to update this list with the newest resources. If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
## A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
🎉 **Our survey has been accepted by ACM Computing Surveys (Impact Factor = 28.0). [<a href="https://dl.acm.org/doi/10.1145/3783986">Link</a>]**
We warmly welcome citations from the community!
<div align="center">
**[<a href="https://arxiv.org/abs/2404.18886">Paper Page</a>]**
**[<a href="https://mp.weixin.qq.com/s/yUo6NDDeMRHAqoKk5RPZdg">专知 中文解读</a>]**
**[<a href="https://mp.weixin.qq.com/s/bWEckT9eXfrr-AWehPOYtw">时序人 中文解读</a>]**
**[<a href="https://zhuanlan.zhihu.com/p/696930711">知乎 中文解读</a>]**
**[[All Summarized and Categorized Papers](https://docs.google.com/spreadsheets/d/1X5ujA-yXCzkzz4Uu9ErP8mu_Jg2zWk4fwZrwrMcqMoI/edit#gid=0)]**
</div>
**Authors**: Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang*, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen*
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|:--:|
| *Figure 1. An overview of diffusion models for time series and spatio-temporal data analysis.* |
🙋 Please let us know if you find a mistake or have any suggestions!
✨ If you found this survey and repository useful, please consider starring this repository and citing our survey paper:
```bibtex
@article{10.1145/3783986,
author = {Yang, Yiyuan and Jin, Ming and Wen, Haomin and Zhang, Chaoli and Liang, Yuxuan and Ma, Lintao and Wang, Yi and Liu, Chenghao and Yang, Bin and Xu, Zenglin and Pan, Shirui and Wen, Qingsong},
title = {A Survey on Diffusion Models for Time Series and Spatio-Temporal Data},
year = {2026},
issue_date = {June 2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {58},
number = {8},
issn = {0360-0300},
url = {https://doi.org/10.1145/3783986},
doi = {10.1145/3783986},
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.1},
journal = {ACM Comput. Surv.},
month = feb,
articleno = {196},
numpages = {39},
keywords = {Diffusion models, time series, spatio-temporal data, generative model, temporal data}
}
```
```bibtex
@article{yang2024survey,
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 Jiang Bian and Shirui Pan and Qingsong Wen},
year={2024},
eprint={2404.18886},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
## Taxonomy Document
✨ **[[Paper List and Taxonomy](httpExcerpt of 154,633 characters
Read on GitHub
Yiyuan Yang · University of Oxford · United Kingdom
158
Qingsong Wen · Squirrel Ai Learning · United States
1
CityMind Lab · The Hong Kong University of Science and Technology (Guangzhou) · China
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e721cacc1d10bd82, name:diffusion model, desc:diffusion model