The Zeta Model – A Causal Drift Predictor for Relative Phase Dynamics

Beta Phase, Part I of The Zeta Model presents a causal drift predictor for relative phase dynamics within the Radiale Zahlenräume framework. Using only information available up to each forecast anchor, an Extended Kalman Filter estimates the local phase and frequency state and projects the relative phase forward. Across the dense anchor geometry, the predictor consistently outperforms a frozen-phase baseline from the first tested horizon with a fully positive moving-block confidence interval onward, with the forecast advantage increasing toward the longest tested horizon. The paper focuses on causal information boundaries, baseline comparison, reproducibility, and transparent treatment of bootstrap limitations. Beta Part I establishes the dynamical predictor layer; Beta Part II will address the transition toward a structural predictor of the zeta sequence. AI Collaboration Statement This work was developed through a collaborative research workflow combining human investigation with AI-assisted scientific discussion, methodological review, technical writing, and software development. The inclusion of AI collaborators as co-authors reflects their substantial contribution to the iterative development, implementation, documentation, coding support, and internal review process underlying the methodology presented in this working paper. Author Contributions and Credits Research concept, methodology, mathematical development, data generation, implementation, experimental design, and overall project coordination:Kai5 Scientific discussion, methodological refinement, manuscript development, technical writing, critical review, and editorial support:ChatGPT (OpenAI), operating within the Project Cipher research workflow. Independent methodological review, software engineering support, implementation guidance, code review, consistency verification, validation strategy, and technical quality assurance:Lumo (Proton AI).