Using information-theory metrics to detect regime changes in dynamical systems
- 1School of Science and Engineering, Tecnologico de Monterrey, Ciudad de Mexico, Mexico (javier.amezcua@tec.mx)
- 2Department of Meteorology, University of Reading, Reading, United Kingdom (j.amezcua@reading.ac.uk)
- 3School of Computing, Mathematics and Data Science, Coventry University, Coventry (ae0221@coventry.ac.uk)
Dynamical systems can display a range of dynamical regimes (e.g. attraction to, fixed points, limit cycles, intermittency, chaotic behaviour) depending on the values of parameters in the system. In this work we demonstrate how non-parametric entropy estimation codes (in particular NPEET) based on the Kraskov method can be applied to find regime transitions in a 3D chaotic model (the Lorenz 1963 system) when varying the values of the parameters. These infromation-theory-based methods are simpler and cheaper to apply than more traditional metrics from dynamical systems (e.g. computation of Lyapunov exponents). The non-parametric nature of the method allows for handling long time series without a prohibitive computational burden.
How to cite: Amezcua, J. and Chakraborty, N.: Using information-theory metrics to detect regime changes in dynamical systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6151, https://doi.org/10.5194/egusphere-egu24-6151, 2024.
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