Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

Abstract

We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.

10 pages, 8 figures

References in corpus (8)

• From time series to complex networks: the visibility graph
• Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
• Complex Network Approach for Recurrence Analysis of Time Series
• The backbone of the climate network
• Comparing modern and Pleistocene ENSO-like influences in NW Argentina using nonlinear time series analysis methods
• Geometric detection of coupling directions by means of inter-system recurrence networks
• Identifying complex periodic windows in continuous-time dynamical systems using recurrence-based methods
• Transition from phase to generalized synchronization in time-delay systems