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Stochastic approach for assessing the predictability of chaotic time series using reservoir computing

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Khovanov, I. A. (2021) Stochastic approach for assessing the predictability of chaotic time series using reservoir computing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31 (8). 083105. doi:10.1063/5.0058439 ISSN 1054-1500.

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Official URL: http://dx.doi.org/10.1063/5.0058439

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Abstract

The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data used in the training stage. Chaotic time series obtained by numerically solving ordinary differential equations embed a complicated noise of the applied numerical scheme. Such a dependence of the solution on the numeric scheme leads to an inadequate representation of the real chaotic system. A stochastic approach for generating training time series and characterizing their predictability is suggested to address this problem. The approach is applied for analyzing two chaotic systems with known properties, the Lorenz system and the Anishchenko–Astakhov generator. Additionally, the approach is extended to critically assess a reservoir computing model used for chaotic time series prediction. Limitations of reservoir computing for surrogate modeling of chaotic systems are highlighted.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Chaotic behavior in systems., Machine learning, Neural networks (Computer science), Industry 4.0
Journal or Publication Title: Chaos: An Interdisciplinary Journal of Nonlinear Science
Publisher: American Institute of Physics
ISSN: 1054-1500
Official Date: 2021
Dates:
DateEvent
2021Published
2 August 2021Available
12 July 2021Accepted
Volume: 31
Number: 8
Article Number: 083105
DOI: 10.1063/5.0058439
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in I. A. Khovanov , "Stochastic approach for assessing the predictability of chaotic time series using reservoir computing", Chaos 31, 083105 (2021) and may be found at https://doi.org/10.1063/5.0058439
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 14 September 2021
Date of first compliant Open Access: 16 September 2021
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