Restoring Latent Dynamics in Financial Time-Series via ODE-Inspired State Space Constraints

This paper presents a non-predictive, domain-agnostic architectural charter for state-space refinement under limited observation.

This paper proposes SS (State System), a conceptual architectural framework for constructing and refining a space of possible system states under observable constraints. SS is not designed as a predictive model and makes no empirical, benchmark, or performance claims. Rather than forecasting future values, SS iteratively reconstructs admissible state structures using only observable indicators, progressively increasing structural consistency with actual observations. The framework operates through a multi-layered refinement process that separates state reconstruction, transition admissibility, structural evaluation, non-normal regime handling, and global logical closure enforced through retry. SS does not assume a unified objective function, nor does it evolve through loss minimization or incremental parameter optimization. Structural failure is addressed through full reconstitution of the state structure rather than localized correction. This work contributes not a specific implementation, but a minimal architectural charter that specifies the irreducible structural conditions required to preserve state-space consistency across heterogeneous evaluative regimes. This framework is presented without reference to higher-level conceptual systems that motivated its initial development, and is conceptually independent of the author’s other publications; it should therefore be evaluated as a standalone architectural proposal. Continuous-time dynamics, including ODE-based formulations, are employed only as optional constraint mechanisms for enforcing temporal admissibility, not as predictive models or core learning objectives.