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Classification of chaotic time series with deep learning

Abstract:

We use standard deep neural networks to classify univariate time series generated by discrete and continuous dynamical systems based on their chaotic or non-chaotic behaviour. Our approach to circumvent the lack of precise models for some of the most challenging real-life applications is to train different neural networks on a data set from a dynamical system with a basic or low-dimensional phase space and then use these networks to classify univariate time series of a dynamical system with m...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.physd.2019.132261

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
Publisher:
Elsevier
Journal:
Physica D: Nonlinear Phenomena More from this journal
Volume:
403
Article number:
132261
Publication date:
2019-12-04
Acceptance date:
2019-11-08
DOI:
ISSN:
0167-2789
Language:
English
Keywords:
Pubs id:
pubs:1046397
UUID:
uuid:1f0649ee-0a21-4cf4-88b8-fb6d5314aa00
Local pid:
pubs:1046397
Source identifiers:
1046397
Deposit date:
2019-11-10

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