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Detection of timescales in evolving complex systems.

Richard K Darst1, Clara Granell2, Alex Arenas3

  • 1Department of Computer Science, Aalto University School of Science, P.O. Box 15400, FI-00076, Finland.

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This study introduces a novel method for analyzing complex dynamic systems by identifying evolutionary timescales. The approach detects changes in system configuration, enabling dynamic interval generation for better data analysis.

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Area of Science:

  • Complex Systems Analysis
  • Data Science
  • Dynamical Systems Theory

Background:

  • Complex systems are inherently dynamic, often analyzed through discrete time snapshots.
  • Traditional analysis uses fixed time intervals, which may not accurately capture system evolution.
  • Dynamic intervals are needed to match the system's configuration changes.

Purpose of the Study:

  • To develop a method for detecting evolutionary changes in complex systems.
  • To generate dynamic time intervals that reflect system configuration evolution.
  • To identify and analyze evolutionary timescales in dynamic data.

Main Methods:

  • Proposing a method to detect evolutionary changes in complex system configurations.
  • Generating dynamic intervals based on detected evolutionary shifts.
  • Identifying evolutionary timescales by analyzing peaks in similarity between consecutive time interval event sets.

Main Results:

  • The method successfully detects evolutionary timescales in both toy models and real-world datasets.
  • It accurately identifies timescales whether system evolution is smooth or abrupt.
  • The technique is scalable to large datasets and computationally efficient.

Conclusions:

  • The proposed method offers a parameter-free approach for detecting multiple timescales in complex dynamic systems.
  • Dynamic interval generation provides a more accurate representation of system evolution.
  • This approach enhances the analysis of time-varying data in complex systems.