Dynamics of reservoir computing for crises prediction

Abstract

Reservoir computing has emerged as a powerful framework for time series modelling and forecasting including the prediction of discontinuous transitions. However, the mechanism behind its success is not yet fully understood. This letter elucidates the functioning of reservoir computing by examining its successful prediction of boundary and attractor merging crises. We investigate in detail how reservoirs's internal dynamics mimic the actual system, that enables it to accurately reproduce the scaling exponent near boundary crisis. We establish this across distinct systems, exemplified by the logistic and Gauss maps. The study contributes to the broader understanding of the internal dynamics that enable learning algorithms to anticipate critical transitions.