Conditional Sig-Wasserstein GANs for Time Series Generation

• URL: http://arxiv.org/abs/2006.05421v2
• Date: Wed, 11 Oct 2023 20:39:34 GMT
• Title: Conditional Sig-Wasserstein GANs for Time Series Generation
• Authors: Shujian Liao, Hao Ni, Lukasz Szpruch, Magnus Wiese, Marc Sabate-Vidales and Baoren Xiao
• Abstract summary: Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. These methods struggle to capture the temporal dependence of joint probability distributions induced by time-series data. Long time-series data streams hugely increase the dimension of the target space, which may render generative modelling infeasible. We propose a generic conditional Sig-WGAN framework by integrating Wasserstein-GANs with mathematically principled and efficient path feature extraction.
• Score: 8.593063679921109
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 modelling 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 characterises the law of the time-series model. In particular, we develop the conditional Sig-$W_1$ 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.

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