Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting

ArXi:2605.24548v1 Announce Type: new Time series driven by unobserved latent states frequently exhibit abrupt jump discontinuities whose timing and magnitude cannot be predicted from observed history alone. Classical jump-diffusion models offer a principled mathematical framework but assume rigid parametric forms, while recent neural jump models operate on fully observed trajectories without inferring the hidden states that govern the dynamics.