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Detecting shifts in nonlinear dynamics using Empirical Dynamic Modeling with Nested-Library Analysis.

Yong-Jin Huang1,2, Chun-Wei Chang1,3, Chih-Hao Hsieh1,2,4,5

  • 1National Center for Theoretical Sciences, Taipei, Taiwan.

Plos Computational Biology
|January 5, 2024
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Summary
This summary is machine-generated.

We developed Nested-Library Analysis (NLA) to detect regime shifts, which are abrupt changes in dynamical systems. NLA uses empirical dynamic modeling to identify critical transition points in time series data.

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Area of Science:

  • Ecology
  • Complex Systems Science
  • Time Series Analysis

Background:

  • Natural systems exhibit abrupt changes called regime shifts, representing transitions between alternative stable states.
  • Existing methods for detecting regime shifts from time series data often lack theoretical grounding in underlying dynamics.
  • A generic, theoretically linked detection method for regime shifts is needed.

Purpose of the Study:

  • To introduce a novel, equation-free method, Nested-Library Analysis (NLA), for retrospectively detecting regime shifts.
  • To validate NLA's effectiveness using a chaotic model exhibiting critical transitions.
  • To demonstrate NLA's applicability to real-world ecological data.

Main Methods:

  • Nested-Library Analysis (NLA) utilizes empirical dynamic modeling (EDM) and attractor reconstruction theory.
  • Regime shift detection involves identifying the optimal data subset (library) that maximizes forecasting skill.
  • The method reconstructs system dynamics from a single time series without requiring prior knowledge of system equations.

Main Results:

  • NLA successfully identified the critical transition point in a simulated chaotic system.
  • The method outperformed existing statistical approaches in detecting regime shifts.
  • NLA accurately detected a known real-world regime shift in the Pacific Decadal Oscillation index around the mid-1970s.

Conclusions:

  • Nested-Library Analysis (NLA) provides a robust and generalizable method for detecting regime shifts.
  • The approach is theoretically grounded in empirical dynamic modeling and requires only time series data.
  • NLA offers a powerful tool for understanding and predicting abrupt changes in complex systems.