Predicting Multivariate Time Series Using Subspace Echo State Network

Abstract

Echo state network (ESN) is a novel kind of recurrent neural networks, where a reservoir is generated randomly and only readout layer is adaptable. It outperforms conventional recurrent neural networks in the field of multivariate time series prediction. Often ESN works beautifully. But sometimes it works poorly because of ill-posed problem. To solve it, we propose a new model on the basis of ESNs, termed as fast subspace decomposition echo state network (FSDESN). The core of the model is to utilize fast subspace decomposition algorithm for extracting a compact subspace out of a redundant large-scale reservoir matrix in order to remove approximate collinear components, overcome the ill-posed problem, and improve generalization performance. Experimental results on two multivariate benchmark datasets substantiate the effectiveness and characteristics of FSDESN.