Predicting transitions in intermittent chaos using reservoir computing

We propose a prediction method based on reservoir computing for capturing transitions between local attractors in time series of intermittent chaotic systems. We investigate the prediction performance focusing on whether the predicted signal accurately captures the timing of transitions. The results in this paper show that the prediction signal can successfully predict the transition behavior of the target signal in single-step-ahead predictions, while increasing the prediction steps leads to delayed or failed transitions. To address this, we introduce a criterion of permissible delay step, which permits for small timing errors when prediction precision is evaluated. This approach significantly improves the transition prediction precision, especially in cases with intermediate prediction steps. Our findings provide insights into the potential of reservoir computing for predicting critical transition events in nonlinear dynamical systems.