Regensburg 2022 – scientific programme
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 6: Data Analytics for Complex Systems (joint session DY/SOE)
SOE 6.8: Talk
Monday, September 5, 2022, 17:00–17:15, H18
Distinguishing noise from high-dimensional chaos — •Inga Kottlarz1,2 and Ulrich Parlitz1,2 — 1Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany — 2Institute for Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany
The ordinal pattern-based Complexity-Entropy Plane is a popular tool in nonlinear dynamics for distinguishing noise from chaos. While successful attempts to do so have been documented for low-dimensional maps and continuous-time systems, high-dimensional systems have been somewhat neglected so far. To address the question in which way time series from highdimensional chaotic attractors can be characterized by their location in the Complexity-Entropy Plane we analyze data from the high-dimensional continuous-time Lorenz-96 system, the discrete generalized Hénon map and the Mackey-Glass equation as a delay system and discuss the crucial role of the lag and the pattern length or the ordinal pattern, and the length of the available time series.