Comparison of TimeGAN and VAE Synthetic Financial Time Series Robustness in LLM Temporal Reasoning Evaluation on TabBench

Abstract

Abstract: Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate realistic synthetic financial return series for portfolio construction and risk modeling applications. Using historical daily returns from the S and P 500 as a benchmark, we generate synthetic datasets under comparable market conditions and eva

Research Question

How do TimeGAN and VAE-generated synthetic financial time series compare in terms of robustness when used to evaluate the temporal reasoning capabilities of LLMs on TabBench, as measured by accuracy under adversarial noise perturbations?

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