Summary of Leveraging Priors Via Diffusion Bridge For Time Series Generation, by Jinseong Park et al.
Leveraging Priors via Diffusion Bridge for Time Series Generation
by Jinseong Park, Seungyun Lee, Woojin Jeong, Yujin Choi, Jaewook Lee
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes TimeBridge, a framework for generating time series data using diffusion models with diverse prior distributions. The authors address the limitations of standard Gaussian priors for time series generation, which may not capture unique characteristics like fixed time order and data scaling. TimeBridge leverages diffusion bridges to learn the transport between chosen prior and data distributions, enabling flexible synthesis for unconditional and conditional time series generation tasks. Experimental results demonstrate state-of-the-art performance in both scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series generation is a powerful tool used in many real-world applications. Recently, special kinds of computer models called diffusion models have become popular for generating time series data. However, these models can be limited because they often use a standard prior distribution that may not work well for all types of time series data. In this paper, the authors introduce a new framework called TimeBridge that allows for more flexible generation of time series data using different prior distributions. This framework uses something called diffusion bridges to learn how to transform between these different priors and the actual data. The authors show that their method performs better than existing methods in both unconditional and conditional time series generation tasks. |
Keywords
» Artificial intelligence » Diffusion » Time series
