Robust neural predictor for noisy chaotic time series prediction

Abstract

A robust neural predictor is designed for noisy chaotic time series prediction in this paper. The main idea is based on the consideration of the bounded uncertainty in predictor input, and it is a typical Errors-in-Variables problem. The robust design is based on the linear-in-parameters ESN (Echo State Network) model. By minimizing the worst-case residual induced by the bounded perturbations in the echo state variables, the robust predictor is obtained in coping with the uncertainty in the noisy time series. In the experiment, the classical Mackey-Glass 84-step benchmark prediction task is investigated. The prediction performance is studied for the nominal and robust design of ESN predictors.