The log-signature-based time series Wasserstein generative adversarial network

David Hirnschall, Paul Krühner and Kurt Hornik

Journal of Computational Finance

Abstract: The signature Wasserstein generative adversarial network (SigWGAN) developed by Ni et al in 2021 achieved great results in time series generation using a recurrent neural network in combination with log signatures as the generator, trained to maximize the similarity between signatures given a static loss function. As an alternative to the static loss function on signatures, we propose using neural networks to minimize the Wasserstein distance between log signatures to reduce the target dimension of the generator and increase performance by introducing a learnable discriminator. We validate our proposed model on synthetic and real-world data across several performance evaluation metrics, showcasing its effectiveness for financial time series generation.

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