Early warning signals for critical transitions in complex systems

Abstract

In this topical review, we present a brief overview of the different methods and measures to detect the occurrence of critical transitions in complex systems. We start by introducing the mechanisms that trigger critical transitions, and how they relate to early warning signals (EWS) and mention briefly the conventional measures based on critical slowing down as computed from data and applied to real systems. We then present in detail the approaches for multivariate data, including those defined for complex networks. More recent techniques like the warning signals derived from the recurrence pattern underlying the data, are presented in detail as measures from recurrence plots and recurrence networks. This is followed by a discussion on how methods based on machine learning are used most recently, to detect critical transitions in real and simulated data. Towards the end, we summarise the issues faced while computing the EWS from real-world data and conclude with our outlook and perspective on future trends in this area.

31 pages, 9 figures

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Cited by in corpus (5)

• Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems
• Anticipating critical transitions in multidimensional systems driven by time- and state-dependent noise
• Interpretable Early Warnings using Machine Learning in an Online Game-experiment
• Detecting and forecasting tipping points from sample variance alone
• Finite-time scaling on low-dimensional map bifurcations