| Full text | |
| Author(s): |
Andreani, Alexandre C.
;
Boaretto, Bruno R. R.
;
Macau, Elbert E. N.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS; v. N/A, p. 11-pg., 2025-09-15. |
| 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 H & eacute;non 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. (AU) | |
| FAPESP's process: | 21/09839-0 - The role of synaptic current in synchronizing of neuronal networks |
| Grantee: | Bruno Rafael Reichert Boaretto |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 23/16273-8 - Investigating Emergent Synchronization Patterns and Information Processing of Neural Networks using Data Analysis |
| Grantee: | Bruno Rafael Reichert Boaretto |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 24/05700-5 - Non linear dynamics |
| Grantee: | Iberê Luiz Caldas |
| Support Opportunities: | Research Projects - Thematic Grants |