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Correlation lags give early warning signals of approaching bifurcations

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  • Tirabassi, Giulio
  • Masoller, Cristina

Abstract

Identifying approaching bifurcations and regime transitions from observations is an important challenge in time series analysis with practical applications in many fields of science. Well-known indicators are the increase in spatial and temporal correlations. However, the performance of these indicators depends on the system under study and on the type of approaching bifurcation, and no indicator provides a reliable warning for any system and bifurcation. Here we propose an indicator that simultaneously takes into account information about spatial and temporal correlations. By performing a bivariate correlation analysis of signals recorded in pairs of adjacent spatial points, and analyzing the distribution of lag times that maximize the cross-correlation, we find that the variance of the lag distribution displays an extreme value that is a consistent early warning indicator of the approaching bifurcation. We demonstrate the reliability of this indicator using different types of models that present different types of bifurcations, including local bifurcations (transcritical, saddle-node, supercritical and subcritical Hopf), and global bifurcations.

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  • Tirabassi, Giulio & Masoller, Cristina, 2022. "Correlation lags give early warning signals of approaching bifurcations," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:chsofr:v:155:y:2022:i:c:s0960077921010742
    DOI: 10.1016/j.chaos.2021.111720
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    Cited by:

    1. Wu, Tao & Gao, Xiangyun & An, Feng & Xu, Xin & Kurths, Jürgen, 2024. "Forecasting the dynamics of correlations in complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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