Abstract
Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.
Highlights
The Earth’s past has been characterized by rapid and often unexpected punctuated shifts in temperature and climatic conditions [1], lakes and coral reefs have shifted among alternative states [2], neural cells move regularly between different dynamical regimes [3], and financial markets are notorious for abrupt shifts
We present results here assuming that the only available information to a practitioner is a time series derived from a system, which may be approaching a critical transition
Interpolation can solve issues of missing values and irregular time series, but it can result in spurious correlations, and checking interpolated records against the original time series to ensure that the density of interpolated points is constant along the time series should be considered [8]
Summary
The Earth’s past has been characterized by rapid and often unexpected punctuated shifts in temperature and climatic conditions [1], lakes and coral reefs have shifted among alternative states [2], neural cells move regularly between different dynamical regimes [3], and financial markets are notorious for abrupt shifts. Information about the drivers or the values at which systemic responses are so triggered (critical thresholds) is difficult to acquire (but see [5]) These sudden transition incur large costs as restoration to the previous conditions is difficult or sometimes even impossible [2]. To overcome these challenges, numerous studies have suggested the use of generic early warning signals (or leading indicators) that can detect the proximity of a system to a tipping point [6]. They may be used to rank instances of a system (e.g. different patients, individual coral reefs, different markets etc.) according to their proximity to a critical threshold
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
