Liao, Shujian;
Ni, Hao;
Sabate‐Vidales, Marc;
Szpruch, Lukasz;
Wiese, Magnus;
Xiao, Baoren;
(2023)
Sig-Wasserstein GANs for conditional time series generation.
Mathematical Finance
10.1111/mafi.12423.
(In press).
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Abstract
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high-dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig-WGAN framework by integrating Wasserstein-GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time-series model. In particular, we develop the conditional Sig-W1 metric that captures the conditional joint law of time series models and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators, which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.
| Type: | Article |
|---|---|
| Title: | Sig-Wasserstein GANs for conditional time series generation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/mafi.12423 |
| Publisher version: | https://doi.org/10.1111/mafi.12423 |
| Language: | English |
| Additional information: | © 2023 The Authors. Mathematical Finance published by Wiley Periodicals LLC This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | conditional generative adversarial networks, generative adversarial networks, rough path theory, time series modeling, 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/10181357 |
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