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Data for Stochastic approach for assessing the predictability of chaotic time-series using reservoir computing
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Khovanov, I. A. (2021) Data for Stochastic approach for assessing the predictability of chaotic time-series using reservoir computing. [Dataset]
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Archive (ZIP) (Dataset file)
WRAP_dataset_153069.zip - Published Version Available under License Creative Commons Attribution 4.0. Download (16Mb) |
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Plain Text (Readme file)
readme.txt - Published Version Available under License Creative Commons Attribution 4.0. Download (1660b) |
Official URL: http://wrap.warwick.ac.uk/153069/
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: | Dataset | ||||||||
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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 | ||||||||
Type of Data: | Simulation Data | ||||||||
Library of Congress Subject Headings (LCSH): | Chaotic behavior in systems, Machine learning, Neural networks (Computer science), Industry 4.0 | ||||||||
Publisher: | University of Warwick, School of Engineering | ||||||||
Official Date: | 20 July 2021 | ||||||||
Dates: |
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Status: | Not Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Media of Output (format): | ASCII format | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Copyright Holders: | University of Warwick | ||||||||
Description: | Dataset consists of a set of folders for figures shown |
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Date of first compliant deposit: | 20 July 2021 | ||||||||
Date of first compliant Open Access: | 20 July 2021 | ||||||||
Related URLs: | |||||||||
Contributors: |
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