Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems

TL;DR

This paper reviews methods for detecting critical transitions in real-world non-autonomous systems using time-series analysis, emphasizing the importance of early detection for system stability and safety.

Contribution

It provides a critical assessment of current techniques for identifying critical transitions in non-autonomous systems, highlighting challenges and future directions.

Findings

Analyzes steps from data collection to detection reliability.

Highlights advantages and limitations of existing methods.

Suggests improvements for real-time forecasting of critical transitions.

Abstract

Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.