Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

The paper studies Bayesian ridge regression and conformal prediction intervals for uncertainty quantification in Next Generation Reservoir Computing. It targets memory-driven nonlinear dynamical systems across science and engineering, characterizing when the two uncertainty-estimation approaches agree or differ in their prediction interval widths.