MoMaR+- A Hybrid Multisource Signal Intelligence Platform for Explainable Decision Support in Noisy Time-Series Environments

MoMaR+ is a hybrid decision intelligence platform designed to extract reliable operational signals from heterogeneous, noisy, asynchronous, and partially contradictory data streams.It combines a deterministic rule engine with machine learning and sequential neural models, using the symbolic layer as a structural prior and the statistical layer as controlled reinforcement.The platform implements multi-source feature engineering, rigorous anti-leakage validation, continuous benchmarking, walk-forward analysis, feature pruning, calibration, and robustness testing.A private-cloud LLM supports the system as a semantic filter, relevance classifier, and contextual commentary layer, without replacing the explainable numerical core.The entire project is conceived as a reproducible operational infrastructure, with remote compute orchestration, logging, auditability, fallback paths, hardware monitoring, and safeguarded decision mechanisms.