Trajectories generation with TimeGAN for modeling anomalous diffusion in financial markets

These findings highlight the potential of synthetic data for training models in markets with limited historical records, offering a scalable approach to financial simulations, however, limitations in replicating volatility underscore the need for refined generative techniques to fully capture complex market dynamics.