https://github.com/arash-shahmansoori/generative-adversarial-networks-for-financial-time-series-generation
Science Score: 10.0%
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Low similarity (4.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: arash-shahmansoori
- License: mit
- Language: Python
- Default Branch: master
- Size: 9.77 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
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
Owner
- Login: arash-shahmansoori
- Kind: user
- Location: Ireland
- Repositories: 1
- Profile: https://github.com/arash-shahmansoori