Chaotic time series prediction using echo state network based on selective opposition grey wolf optimizer

2021 ◽  
Author(s):  
Hao-Chang Chen ◽  
Du-Qu Wei
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Grey Wolf Optimizer ◽  
Echo State Network ◽  
Grey Wolf ◽  
Chaotic Time Series Prediction

Chaotic Time Series Prediction using Three-Dimensional FOA and Echo State Network

2021 ◽  
Author(s):  
Yanhu Wang ◽  
Shanshan Xu ◽  
Yuling Luo ◽  
Shunsheng Zhang ◽  
Min Su ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Three Dimensional ◽  
Chaotic Time Series ◽  
Echo State Network ◽  
Chaotic Time Series Prediction

WOA-Based Echo State Network for Chaotic Time Series Prediction

2020 ◽  
Vol 76 (5) ◽  
pp. 384-391
Author(s):  
Minghui Zhang ◽  
Baozhu Wang ◽  
Yatong Zhou ◽  
Haoxuan Sun
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Echo State Network ◽  
Chaotic Time Series Prediction

Chaotic Time Series Prediction Using a Novel Echo State Network Model with Input Reconstruction, Bayesian Ridge Regression and Independent Component Analysis

2019 ◽  
Vol 34 (06) ◽  
pp. 2051008
Author(s):  
Hoang Minh Nguyen ◽  
Gaurav Kalra ◽  
Taejoon Jun ◽  
Daeyoung Kim
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Ridge Regression ◽  
Time Series Prediction ◽  
Component Analysis ◽  
Chaotic Time Series ◽  
Independent Component ◽  
Echo State Network ◽  
Chaotic Time Series Prediction ◽  
Input Reconstruction

This paper presents a novel Echo State Network (ESN) model for chaotic time series prediction, which consists of three steps including input reconstruction, dimensionality reduction and regression. First, phase-space reconstruction is used to reconstruct the original ‘attractor’ of the input time series. Then, Independent Component Analysis (ICA) is used to identify independent components, reduce dimensionality and overcome multicollinearity problem of the reconstructed input matrix. Finally, Bayesian Ridge Regression provides accurate predictions thanks to its regularization effect to avoid over-fitting and its robustness to noise owing to its probabilistic strategy. Our experimental results show that our model significantly outperforms other ESN models in predicting both artificial and real-world chaotic time series.

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