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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]
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Database: Engineering Source
Description
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]
ISSN:02181274
DOI:10.1142/S0218127425300319