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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]

[img] Archive (ZIP) (Dataset file)
WRAP_dataset_153069.zip - Published Version
Available under License Creative Commons Attribution 4.0.

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[img] Plain Text (Readme file)
readme.txt - Published Version
Available under License Creative Commons Attribution 4.0.

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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: Dataset
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
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: School of Engineering, University of Warwick
Official Date: 20 July 2021
Dates:
DateEvent
25 May 2021Created
20 July 2021Available
20 July 2021Published
Status: Not Peer Reviewed
Publication Status: Published
Media of Output (format): ASCII format
Access rights to Published version: Open Access
Copyright Holders: University of Warwick
Description:

Dataset consists of a set of folders for figures shown
in the paper. Data are included in ASCII format.
Each folder contains file "comment.txt" with relevant
to each figure information.
File 'comment_on_numerical_methods.pdf' is a brief
description of the used numerical methods.

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Contributors:
ContributionNameContributor ID
DepositorKhovanov, I. A.29260

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