A Multivariable Time Series Classification Approach Based on Improved Functional Echo State Network

Abstract

Functional echo state network (FESN) is a new kind of recurrent neural network which has been successfully used for time series classification. In order to make FESN more suitable for multi-variable time series data classification task, we present a novel FESN model by modifying the output layer of original FESN with softmax regression, and the L-BFGS algorithm is employed to train such proposed model. Moreover, the genetic algorithm is used to determine the hyper-parameter of the improved FESN. The experimental results show that the proposed approach can achieve better accuracy than classical classifiers such as support vector machine, Long Short-Term Memory neural network and original FESN, in the context of multi-variable series data classification.