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Approach to Predicting Extreme Events in Time Series of Chaotic Dynamical Systems Using Machine Learning Techniques.

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Title: Approach to Predicting Extreme Events in Time Series of Chaotic Dynamical Systems Using Machine Learning Techniques.
Authors: Andreani, Alexandre C.1 (AUTHOR) acandreani@unifesp.br, Boaretto, Bruno R. R.2,3 (AUTHOR) bruno.boaretto@unifesp.br, Macau, Elbert E. N.2 (AUTHOR) elbert.macau@unifesp.br
Source: International Journal of Bifurcation & Chaos in Applied Sciences & Engineering. Dec2025, Vol. 35 Issue 15, p1-11. 11p.
Subjects: Machine learning, Convolutional neural networks, Chaos theory, Time series analysis, Prediction models, Nonlinear dynamical systems
Abstract: This work proposes an innovative approach using machine learning to predict extreme events in time series of chaotic dynamical systems. The research focuses on the time series of the Hénon map, a two-dimensional model known for its chaotic behavior. The method consists of identifying time windows that anticipate extreme events, using convolutional neural networks to classify the system states. By reconstructing attractors and classifying (normal and transitional) regimes, the model shows high accuracy in predicting normal regimes, although forecasting transitional regimes remains challenging, particularly for longer intervals and rarer events. The method presents a result above 80% of success for predicting the transition regime up to three steps before the occurrence of the extreme event. Despite limitations posed by the chaotic nature of the system, the approach opens avenues for further exploration of alternative neural network architectures and broader datasets to enhance forecasting capabilities. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 189861563
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PubTypeId: academicJournal
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  Data: Approach to Predicting Extreme Events in Time Series of Chaotic Dynamical Systems Using Machine Learning Techniques.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Chaos+theory%22">Chaos theory</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+dynamical+systems%22">Nonlinear dynamical systems</searchLink>
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  Data: This work proposes an innovative approach using machine learning to predict extreme events in time series of chaotic dynamical systems. The research focuses on the time series of the Hénon map, a two-dimensional model known for its chaotic behavior. The method consists of identifying time windows that anticipate extreme events, using convolutional neural networks to classify the system states. By reconstructing attractors and classifying (normal and transitional) regimes, the model shows high accuracy in predicting normal regimes, although forecasting transitional regimes remains challenging, particularly for longer intervals and rarer events. The method presents a result above 80% of success for predicting the transition regime up to three steps before the occurrence of the extreme event. Despite limitations posed by the chaotic nature of the system, the approach opens avenues for further exploration of alternative neural network architectures and broader datasets to enhance forecasting capabilities. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1142/S0218127425300319
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      – Code: eng
        Text: English
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        PageCount: 11
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    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Chaos theory
        Type: general
      – SubjectFull: Time series analysis
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Nonlinear dynamical systems
        Type: general
    Titles:
      – TitleFull: Approach to Predicting Extreme Events in Time Series of Chaotic Dynamical Systems Using Machine Learning Techniques.
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            NameFull: Andreani, Alexandre C.
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            NameFull: Boaretto, Bruno R. R.
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            NameFull: Macau, Elbert E. N.
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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