Multichannel Coherence and Lead–Lag Structure in Predictive-Regime Monitoring: Beyond Uniform Consensus

Supervisory monitoring systems for predictive-regime transitions typically aggregate multichannel evidence using uniform consensus rules: if a sufficient number of channels independently detect nonlinearity, the system reports coherent onset. This paper extends the predictive-regime monitoring framework beyond uniform consensus by studying the temporal ordering, reliability weighting, and adaptive gating of per-channel regime diagnostics. Using coupled logistic maps, multi-component Lorenz systems, and coupled Rössler oscillatorswith controlled coupling delays, we demonstrate that (i) different measurement channels detect regime transitions at systematically different times, producingreproducible lead–lag structure; (ii) weighting channels by signal quality or reliability improves regime detection accuracy over uniform consensus, particularly when channels differ in noise levels; (iii) adaptive consensus rules that scale the agreement threshold under channel dropout maintain detection sensitivity with lower false-positive rates than fixed alternatives; and (iv) precursor channels—those that consistently detect transitions earliest—can be identified and rankedwith bootstrap-validated confidence. These results elevate multichannel coherence from a simple consensus filter to an informative diagnostic layer that encodes transition ordering, channel reliability, and system-level robustness.