A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866909946840350720 |
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| author | Yang, Yiyuan Jin, Ming Wen, Haomin Zhang, Chaoli Liang, Yuxuan Ma, Lintao Wang, Yi Liu, Chenghao Yang, Bin Xu, Zenglin Pan, Shirui Wen, Qingsong |
| author_facet | Yang, Yiyuan Jin, Ming Wen, Haomin Zhang, Chaoli Liang, Yuxuan Ma, Lintao Wang, Yi Liu, Chenghao Yang, Bin Xu, Zenglin Pan, Shirui Wen, Qingsong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Diffusion Models for Time Series and Spatio-Temporal Data Yang, Yiyuan Jin, Ming Wen, Haomin Zhang, Chaoli Liang, Yuxuan Ma, Lintao Wang, Yi Liu, Chenghao Yang, Bin Xu, Zenglin Pan, Shirui Wen, Qingsong Machine Learning Artificial Intelligence 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. |
| title | A Survey on Diffusion Models for Time Series and Spatio-Temporal Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.18886 |