TimeGAN
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
About
TimeGAN provides implementations for generating synthetic time-series data, suitable for various applications. It supports training on both synthetic and real-world datasets, including stock and energy data. Users can customize the model architecture and parameters to fit different datasets, and the framework includes tools for evaluating the quality of the generated data through various metrics and visualizations.
Open Source Health
- Stars
- 1,059
- Forks
- 325
- License
- Not stated
- Last commit
- 8 months ago
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Jinsung Yoon
Staff Research Scientist at Google Cloud AI
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