Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization

ArXi:2605.04690v1 Announce Type: new Modeling the dynamics of non-stationary stochastic systems requires balancing the representational power of deep learning with the mathematical transparency of classical models. While classical Marko transition operators provide explicit, theoretically grounded rules for system evolution, their empirical estimation collapses due to severe data sparsity when applied to high-resolution, high-noise environments. We explore this statistical barrier using financial time series as a canonical, real-world testbed.