Sig‐Wasserstein GANs for conditional time series generation

DOI10.1111/mafi.12423arXiv2006.05421MaRDI QIDQ6196300

Magnus Wiese, Lukasz Szpruch, Unnamed Author, Marc Sabate Vidales, Hao Ni, Unnamed Author

Publication date: 14 March 2024

Published in: Mathematical Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.05421

zbMATH Keywords

rough path theorytime series modelinggenerative adversarial networksWasserstein generative adversarial networksconditional generative adversarial networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Financial applications of other theories (91G80) Rough paths (60L20)

Cites Work

• Unnamed Item
• Unnamed Item
• Characteristic functions of measures on geometric rough paths
• The uniqueness of signature problem in the non-Markov setting
• Expected signature of Brownian motion up to the first exit time from a bounded domain
• Uniqueness for the signature of a path of bounded variation and the reduced path group
• Functional linear regression with truncated signatures
• Expected signature of stopped Brownian motion on \(d\)-dimensional \(C^{2, \alpha }\)-domains has finite radius of convergence everywhere: \(2 \leq d \leq 8\)
• On the signature and cubature of the fractional Brownian motion for \(H > \frac{1}{2}\)
• A continuous-time GARCH process driven by a Lévy process: stationarity and second-order behaviour
• The expected signature of Brownian motion stopped on the boundary of a circle has finite radius of convergence
• Generative adversarial networks for financial trading strategies fine-tuning and combination
• Developing the Path Signature Methodology and Its Application to Landmark- Based Human Action Recognition
• Analysis of Financial Time Series

This page was built for publication: Sig‐Wasserstein GANs for conditional time series generation