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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
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Official URL: http://dx.doi.org/10.1063/5.0058439
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 | ||||||||
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| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
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| 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: |
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| 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 | ||||||||
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