Reservoir computing for forecasting non-autonomous dynamics with hidden regime variations

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.