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Complex networks are an important paradigm of modern complex systems scienceswhich allows quantitatively assessing the structural properties of systemscomposed of different interacting entities. During the last years, intensiveefforts have been spent on applying network-based concepts also for theanalysis of dynamically relevant higher-order statistical properties of timeseries. Notably, many corresponding approaches are closely related with theconcept of recurrence in phase space. In this paper, we review recentmethodological advances in time series analysis based on complex networks, witha special emphasis on methods founded on recurrence plots. The potentials andlimitations of the individual methods are discussed and illustrated forparadigmatic examples of dynamical systems as well as for real-world timeseries. Complex network measures are shown to provide information aboutstructural features of dynamical systems that are complementary to thosecharacterized by other methods of time series analysis and, hence,substantially enrich the knowledge gathered from other existing (linear as wellas nonlinear) approaches.

Recurrence-based time series analysis by means of complex network methods