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

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 goal: 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?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.