| Full text | |
| Author(s): |
Valle, Joao
;
Bruno, Odemir Martinez
Total Authors: 2
|
| Document type: | Journal article |
| Source: | CHAOS SOLITONS & FRACTALS; v. 192, p. 9-pg., 2025-03-01. |
| Abstract | |
The complexity and sensitivity to initial conditions are the main characteristics of chaotic dynamical systems, making long-term forecasting a significant challenge. Deep learning, however, is a powerful technique that can potentially improve forecasting in chaotic time series. In this study, we explored the performance of modern neural network architectures in forecasting chaotic time series with different Lyapunov exponents. To accomplish this, we created a robust dataset composed of chaotic orbits with Lyapunov exponents ranging from 0.019 to 1.253 and used state-of-the-art neural network models for time series forecasting, including recurrent-based and transformer-based architectures. Our results show that LSTNet presents the best results in one-step-ahead and the recursive one-step-ahead forecasting for the majority of the time series in our dataset, enabling the prediction of chaotic time series with high Lyapunov exponent. Additionally, we observed that the sensitivity to initial conditions and complexity still affects the performance of the neural networks, decaying predictive power in time series with larger Lyapunov exponent. (AU) | |
| FAPESP's process: | 21/08325-2 - An analysis of network automata as models for biological and natural processes |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 22/01935-2 - Patterns and pseudorandomness in iterative maps in a chaotic regime |
| Grantee: | João Pedro do Valle Alvarenga |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 18/22214-6 - Towards a convergence of technologies: from sensing and biosensing to information visualization and machine learning for data analysis in clinical diagnosis |
| Grantee: | Osvaldo Novais de Oliveira Junior |
| Support Opportunities: | Research Projects - Thematic Grants |