Fin-GAN: forecasting and classifying financial time series via generative adversarial networks-Reference-Cited by-同舟云学术

Fin-GAN: forecasting and classifying financial time series via generative adversarial networks

Published:2024-01-31 Issue:2 Volume:24 Page:175-199
ISSN:1469-7688
Container-title:Quantitative Finance
language:en
Short-container-title:Quantitative Finance

Author:

Vuletić Milena¹²,Prenzel Felix¹,Cucuringu Mihai¹²³⁴

Affiliation:

1. Mathematical Institute, University of Oxford, Andrew Wiles Building, Woodstock Rd, Oxford, OX2 6GG, UK

2. Oxford-Man Institute of Quantitative Finance, University of Oxford, Eagle House, Walton Well Rd, Oxford, UK

3. Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK

4. The Alan Turing Institute, 96 Euston Rd, London, NW1 2DB, UK

Funder

EPSRC Centre for Doctoral Training in Mathematics of Random Systems: Analysis, Modelling and Simulation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/14697688.2023.2299466

Reference59 articles.

1. Arjovsky M. and Bottou L. Towards principled methods for training generative adversarial networks. In International Conference on Learning Representations 2017.

2. Arjovsky M. Chintala S. and Bottou L. Wasserstein generative adversarial networks. In International Conference on Machine Learning pp. 214–223 2017 (PMLR).

3. Assefa S.A. Dervovic D. Mahfouz M. Tillman R.E. Reddy P. and Veloso M. Generating synthetic data in finance: Opportunities challenges and pitfalls. In Proceedings of the First ACM International Conference on AI in Finance pp. 1–8 2020.

4. ExGAN: Adversarial Generation of Extreme Samples

5. Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., Time Series Analysis: Forecasting and Control, 2015 (John Wiley & Sons: Hoboken, NJ).

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