Reservoir computing for forecasting non-autonomous dynamics with hidden regime variations.
TL;DR
A multi-regime RC framework in which multiple readouts are trained under different dynamical conditions and combined through a short observation window to form a trajectory-dependent linear readout enables both regime identification and adaptation to unseen or intermediate dynamics.
Abstract
Accurate prediction of nonlinear dynamical systems becomes particularly challenging when the evolution of the dynamics depends on hidden, time-varying factors that are not directly observable. Although reservoir computing (RC) provides an efficient framework for modeling complex dynamics, standard approaches based on a single trained readout often experience reduced accuracy in such non-autonomous settings. We propose a multi-regime RC framework in which multiple readouts are trained under different dynamical conditions and combined through a short observation window to form a trajectory-dependent linear readout. This enables both regime identification and adaptation to unseen or intermediate dynamics. The method is evaluated on a Duffing oscillator with a time-varying forcing input and a Rössler system driven by chaotic forcing from a Chen system. The results show improved prediction accuracy compared to both regime-specific and single global models trained on data aggregated from multiple regimes.