Complex Network Approach for Recurrence Analysis of Time Series

TL;DR

This paper introduces a novel method that uses complex network theory to analyze time series by transforming recurrence matrices into network adjacency matrices, enabling detection of dynamical changes and climate regime shifts.

Contribution

It presents a new approach that combines recurrence analysis with complex network measures, applied to both simulated and real-world climate data.

Findings

Effective detection of dynamical transitions in logistic map

Identification of subtle climate regime changes in palaeo-climate record

Demonstrates the utility of complex network measures in time series analysis

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.