Summary of Towards Controllable Time Series Generation, by Yifan Bao et al.
Towards Controllable Time Series Generation
by Yifan Bao, Yihao Ang, Qiang Huang, Anthony K. H. Tung, Zhiyong Huang
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 explores the development of Controllable Time Series Generation (CTSG) models. CTSG aims to generate synthetic time series that can adapt to different external conditions, addressing the challenge of data scarcity in rare or unique scenarios. To achieve this, the authors propose a novel approach that leverages advancements in Time Series Generation (TSG). The proposed method has the potential to improve the efficacy of TSG models by producing high-quality synthetic data that mirrors real-world time series. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series generation is an important technique for creating fake data that looks like real-world data. This helps us with many tasks, such as predicting what might happen in the future or identifying patterns in large datasets. However, making this fake data requires a lot of training data. When we don’t have enough data, it’s hard to make good predictions. To solve this problem, researchers are working on ways to control how the fake data is generated so that it matches different situations. This paper looks at one way to do this and could help us make better use of limited data in the future. |
Keywords
* Artificial intelligence * Synthetic data * Time series
