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Indexed by:期刊论文

Date of Publication:2018-10-01

Journal:INFORMATION SCIENCES

Included Journals:SCIE

Volume:465

Page Number:439-458

ISSN No.:0020-0255

Key Words:Echo state networks; Wavelet denoising algorithm; Multivariate time series; Prediction

Abstract:Motivated by the idea of 'decomposition and ensemble', this paper proposes a novel method based on the wavelet-denoising algorithm and multiple echo state networks to improve the prediction accuracy of noisy multivariate time series. The noisy time series is first denoised by a wavelet soft thresholding algorithm and decomposed into a set of well-behaved constitutive series. Each constitutive series is then predicted by a separate echo state network with proper parameters that match the specified dynamics. Finally, the overall prediction is achieved by a linear combination of the constitutive series. For each constitutive series, we use the correlation integral method to select the phase-reconstruction parameters and to construct the appropriate input. Two sets of multivariate time series are investigated using the proposed model and some other related work. The simulation results demonstrate the effectiveness of the proposed method. (C) 2018 Published by Elsevier Inc.