Complex network approach for recurrence analysis of time series

2009/07/31 by N. Marwan, Norbert Marwan, J. F. Donges +7 · 5 citations
Computer Science · Economics, Econometrics and Finance · Physics and Astronomy · #Chaos control and synchronization #Complex Systems and Time Series Analysis #Nonlinear Dynamics and Pattern Formation #nlin.CD #nlin.PS

paper · pdf · doi:10.1016/j.physleta.2009.09.042

published as Physics Letters A, 373, 4246-4254 (2009) · 23 pages, 7 figures

openalex publication_date 2009/09/20 · arxiv created 2009/10/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a novel approach for analysing time series using complex network theory. We identify the recurrence matrix calculated from time series with the adjacency matrix of a complex network, and apply measures for the characterisation of complex networks to this recurrence matrix. By using the logistic map, we illustrate the potentials of these complex network measures for detecting dynamical transitions. Finally we apply the proposed approach to a marine palaeo-climate record and identify subtle changes of the climate regime.

Citations

• Recurrence-plot-based measures of complexity and their application to heart-rate-variability data
• The Fractal Geometry of Nature
• Line structures in recurrence plots
• The structure and function of complex networks
• Determining Lyapunov exponents from a time series

Cited by

• Geometric signature of complex synchronisation scenarios
• Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage
• Quantifying the irregularity of a time series
• Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
• Recurrence network analysis in a model tripartite quantum system

Related

• Directed recurrence networks
• Directed recurrence networks for the analysis of nonlinear and complex dynamical systems
• Geometric detection of coupling directions by means of inter-system recurrence networks
• Tracking Dynamical Transitions using Link Density of Recurrence Networks
• Degree weighted recurrence networks for the analysis of time series data