Ni, H;
Szpruch, L;
Sabate-Vidales, M;
Xiao, B;
Wiese, M;
Liao, S;
(2021)
Sig-Wasserstein GANs for Time Series Generation.
In:
Proceedings of the 2nd ACM International Conference on AI in Finance.
Association for Computing Machinery (ACM)
(In press).
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Abstract
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity time-series generators, the SigWGAN, by combining continuous-time stochastic models with the newly proposed signature W1 metric. The former are the Logsig-RNN models based on the stochastic differential equations, whereas the latter originates from the universal and principled mathematical features to characterize the measure induced by time series. SigWGAN allows turning computationally challenging GAN min-max problem into supervised learning while generating high fidelity samples. We validate the proposed model on both synthetic data generated by popular quantitative risk models and empirical financial data. Codes are available at https://github.com/SigCGANs/Sig-WassersteinGANs.git
| Type: | Proceedings paper |
|---|---|
| Title: | Sig-Wasserstein GANs for Time Series Generation |
| Event: | The 2nd ACM International Conference on AI in Finance |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ai-finance.org/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | generative modelling, neural networks, expected signature, log-signature, rough path theory, Wasserstein generative adversarial networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10138577 |
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