AI RESEARCH
SBBTS: A Unified Schr\"odinger-Bass Framework for Synthetic Financial Time Series
arXiv CS.LG
•
ArXi:2604.07159v1 Announce Type: new We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existing approaches typically fail to jointly model drift and stochastic volatility, as diffusion-based methods fix the volatility while martingale transport models ignore drift. We