Complex network approaches to nonlinear time series analysis

TL;DR

This paper reviews how complex network methods, such as recurrence networks, visibility graphs, and transition networks, enhance nonlinear time series analysis by providing new insights and practical tools across various scientific disciplines.

Contribution

It offers an in-depth review of recent complex network approaches in nonlinear time series analysis, highlighting their methodological foundations, potentials, and limitations.

Findings

Introduces phase space based recurrence networks, visibility graphs, and Markov transition networks.

Demonstrates the application of these methods in diverse fields like climatology and neurophysiology.

Highlights the new insights complex network approaches bring to nonlinear dynamics.

Abstract

In the last decade, there has been a growing body of literature addressing the utilization of complex network methods for the characterization of dynamical systems based on time series. While both nonlinear time series analysis and complex network theory are widely considered to be established fields of complex systems sciences with strong links to nonlinear dynamics and statistical physics, the thorough combination of both approaches has become an active field of nonlinear time series analysis, which has allowed addressing fundamental questions regarding the structural organization of nonlinear dynamics as well as the successful treatment of a variety of applications from a broad range of disciplines. In this report, we provide an in-depth review of existing approaches of time series networks, covering their methodological foundations, interpretation and practical considerations with an emphasis on recent developments. After a brief outline of the state-of-the-art of nonlinear time series analysis and the theory of complex networks, we focus on three main network approaches, namely, phase space based recurrence networks, visibility graphs and Markov chain based transition networks, all of which have made their way from abstract concepts to widely used methodologies. These three concepts, as well as several variants thereof will be discussed in great detail regarding their specific properties, potentials and limitations. More importantly, we emphasize which fundamental new insights complex network approaches bring into the field of nonlinear time series analysis. In addition, we summarize examples from the wide range of recent applications of these methods, covering rather diverse fields like climatology, fluid dynamics, neurophysiology, engineering and economics, and demonstrating the great potentials of time series networks for tackling real-world contemporary scientific problems.