arXiv:2605.24548v1 Announce Type: new Abstract: 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. We propose \textit{Deep ZakaiJ}, a latent-state model for partially observed jump-diffusion systems that embeds the Zakai nonlinear filterin
This development arises from ongoing research at the intersection of classical mathematical modeling and modern deep learning techniques to address complex time series forecasting challenges.
Improved forecasting of unpredictable jump discontinuities in time series, particularly those driven by latent states, has significant implications for various domains requiring high-fidelity predictions.
This new model offers a more robust and flexible approach to handle partially observed jump-diffusion systems, moving beyond rigid parametric forms of classical methods while integrating unobserved latent states.
- · Financial predictive analytics
- · Autonomous systems development
- · Drug discovery and development
- · Logistics and supply chain management
- · Traditional statistical modeling software
- · Prediction models relying solely on observed history
More accurate and resilient predictive models for systems with sudden, unobserved state changes will emerge.
Industries reliant on precise forecasting in dynamic environments will see enhanced decision-making capabilities and risk management.
The broader adoption of such sophisticated hybrid models could accelerate the development of advanced AI agents capable of operating in highly uncertain real-world conditions.
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Read at arXiv cs.LG