The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems. In this article, we propose a data-driven framework combining Reservoir Computing and Normalizing Flow to study this issue, which mimics error modeling to improve the traditional Reservoir Computing performance and takes advantage of both approaches. This model-free method successfully predicts the long-term evolution of stochastic dynamical systems and replicates dynamical behaviors. With few assumptions about the underlying stochastic dynamical systems, we deal with Markov/non-Markov and stationary/non-stationary stochastic processes defined by linear/nonlinear stochastic differential equations or stochastic delay differential equations. We verify the effectiveness of the proposed framework in five experiments, including the Ornstein-Uhlenbeck process, Double-Well system, El Ni\~no Southern Oscillation simplified model, and stochastic Lorenz system. Additionally, we explore the noise-induced tipping phenomena and the replication of the strange attractor.
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