Classification of chaotic time series with deep learning
File(s)
Accepted version
Author(s)
Boullé, Nicolas
Dallas, Vassilios
Nakatsukasa, Yuji
Samaddar, D
Type
Journal Article
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 more intricate or high-dimensional phase space. We illustrate this generalisation approach using the logistic map, the sine-circle map, the Lorenz system, and the Kuramoto–Sivashinsky equation. We observe that a convolutional neural network without batch normalisation layers outperforms state-of-the-art neural networks for time series classification and is able to generalise and classify time series as chaotic or not with high accuracy.
Date Issued
2020-02
Date Acceptance
2019-11-08
Citation
Physica D: Nonlinear Phenomena, 2020, 403
ISSN
0167-2789
Publisher
Elsevier BV
Journal / Book Title
Physica D: Nonlinear Phenomena
Volume
403
Copyright Statement
Copyright © Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://dx.doi.org/10.1016/j.physd.2019.132261
Publication Status
Published
Article Number
132261
Date Publish Online
2019-12-04