Summary of Are Synthetic Time-series Data Really Not As Good As Real Data?, by Fanzhe Fu et al.
Are Synthetic Time-series Data Really not as Good as Real Data?
by Fanzhe Fu, Junru Chen, Jing Zhang, Carl Yang, Lvbin Ma, Yang Yang
First submitted to arxiv on: 1 Feb 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 proposed framework, InfoBoost, integrates universal data synthesis methods to improve generalization in time-series data. By introducing a highly versatile cross-domain data synthesizing framework with time series representation learning capability, researchers aim to surpass the performance of models trained with real data. This approach enables model training without relying on real data and achieves superior reconstruction performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time-series data has limitations due to data quality issues, bias, and generalization problems. Researchers introduced InfoBoost, a framework that synthesizes time series data, allowing for better generalization. They trained models using synthetic data, achieving better results than those trained with real data. This approach can be applied to all time-series data. |
Keywords
* Artificial intelligence * Generalization * Representation learning * Synthetic data * Time series
