A Novel Echo State Network Model Using Bayesian Ridge Regression and Independent Component Analysis

Abstract

We propose a novel Bayesian Ridge Echo State Network (BRESN) model for nonlinear time series prediction, based on Bayesian Ridge Regression and Independent Component Analysis. BRESN has a regularization effect to avoid over-fitting, at the same time being robust to noise owing to its probabilistic strategy. In BRESN we also use Independent Component Analysis (ICA) for dimensionality reduction, and show that ICA improves the model's accuracy more than other reduction techniques. Furthermore, we evaluate the proposed model on both synthetic and real-world datasets to compare its accuracy with twelve combinations of four other regression models and three different choices of dimensionality reduction techniques, and measure its running time. Experimental results show that our model significantly outperforms other state-of-the-art ESN prediction models while maintaining a satisfactory running time.