Summary of A Survey Of Transformer Enabled Time Series Synthesis, by Alexander Sommers et al.
A Survey of Transformer Enabled Time Series Synthesis
by Alexander Sommers, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure, Thomas Arnold
First submitted to arxiv on: 4 Jun 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 This paper surveys the application of generative AI models, particularly transformer-based architectures, to time series generation. The authors identify a gap in this area and review existing works that use GANs, diffusion models, state space models, and autoencoders alongside transformers. While the reviewed papers show great variety in approach and have not yet converged on a solution, they provide suggestive findings and recommendations for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to create artificial time series data using special AI models called generative transformers. These models are usually used for images and language, but we need them for time series too! The researchers found that many different approaches have been tried, like using GANs or autoencoders with transformers. They didn’t find a single best way to do it yet, but they think some ideas might be useful for future research. |
Keywords
» Artificial intelligence » Time series » Transformer
