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README.md

Generative-Adversarial-Networks-for-financial-time-series-generation

This is the code I used for my master thesis at the University of Cambridge. It generates artificial financial time series using Recurrent Generative Adversarial Networks. For more details, please refer to chapter 5 of my thesis available at https://www.researchgate.net/project/Generative-Adversarial-Networks-4

For a tutorial in form of iPython notebook, please refer to appendix E of my master thesis available at https://www.researchgate.net/project/Generative-Adversarial-Networks-4

Both the theoretical approach and the implementation are based on the method of Hyland et al. for generative modelling of time series. Their research paper is available at https://arxiv.org/abs/1706.02633 and their code at https://github.com/ratschlab/RGAN