Echo State Network with Graph Regularized Autoencoder for Time Series Classification
XU Jian
WANG Liang
KOU Qilong
FANG Tao
YOU Dan
ZHOU Leiyue
LUO Yong
Abstract：Echo state networks(ESNs)can provide effective dynamic solutions for time series problems;however,in most cases,ESN models are mainly used for prediction rather than classification,and the application of ESNs in time series classification tasks has not been fully researched.Traditional ESNs suffer from randomly generated input weights,which do not guarantee optimal performance.These randomly generated weights can disrupt useful features during the feature mapping process.To address these drawbacks,a Graph Regularized Autoencoder based Echo State Network model(GRAE-ESN)for time series classification tasks is proposed.This model uses manifold learning to consider the intrinsic manifold structure of the data,constraining the output weights so that the outputs of similar samples are closer in the new space.Subsequently,the input weights in the ESN structure are replaced by with weights obtained from the decoding layer to learn richer input features.Experiments on benchmark data show that the proposed GRAE method effectively improves the ESN classifier.Compared to multiple mainstream algorithms and deep learning methods,this algorithm exhibits better performance and robustness.
Keywords：echo state networkmanifold learningtime series classificationauto-encoder network
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 68-75 )