The Journal of Financial Data Science

Journal: The Journal of Financial Data Science

Portfolio Management Research

2640-3943
2640-3951

Publications 1 - 1 of 1

Comparative Study of Two Generative AI Models for Long Time-Series Generation
Martinez Ibarra, Hugo (2025)
The Journal of Financial Data Science
The generation of artificial financial time series is an active area of research focused on creating alternative methods to synthesize data, particularly when they are scarce or difficult to access. This article compares and evaluates two recent machine learning approaches—generative artificial intelligence (AI) models specifically designed for generating historical stock price time series—against a baseline model. The first model, conditional signature Wasserstein generative adversarial networks (Sig-CWGANs), combines WGAN with the mathematical framework of path signatures, offering a more manageable optimization process and reduced training times, with additional metrics during training. The second model, TransFusion, employs a transformer architecture combined with a diffusion model to facilitate long time-series generation. Both models, along with the baseline GARCH model, are tuned and trained to generate extended time series, reaching over 700 time stamps. Their effectiveness is assessed by calculating common stylized empirical features, or characteristic statistical properties of financial time series, where Sig-CWGAN demonstrates superior performance, particularly in areas where GARCH falls short.

Publications 1 - 1 of 1