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Introduction to Focus Issue: Data-driven models and analysis of complex systems.

Johann H Martínez1, Klaus Lehnertz2,3,4, Nicolás Rubido5

  • 1Complex Systems Group and G.I.S.C, Universidad Rey Juan Carlos, Móstoles, 28933 Madrid, Spain.

Chaos (Woodbury, N.Y.)
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Summary
This summary is machine-generated.

This Focus Issue showcases data-driven research in complex systems, spanning diverse fields from finance to neuroscience. Advanced methods like machine learning and persistent homology are revolutionizing our understanding of intricate system dynamics.

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Area of Science:

  • Complexity Science
  • Data-Driven Research

Background:

  • Complex systems research spans diverse fields including finance, climate, and neuroscience.
  • Traditional methods often struggle with the intricate dynamics of these systems.

Purpose of the Study:

  • To highlight recent advances in the study of complex systems.
  • To emphasize the impact of data-driven research and novel methodologies.

Main Methods:

  • Machine learning
  • Higher-order correlations
  • Control theory
  • Information theory
  • Time series analysis
  • Persistent homology

Main Results:

  • Summarizes 47 published works showcasing diverse applications of complex systems.
  • Demonstrates the power of advanced analytical techniques in understanding system dynamics.
  • Highlights breakthroughs in areas like financial markets, climate science, and biomedicine.

Conclusions:

  • Data-driven approaches are revolutionizing the study of complex systems.
  • Novel methodologies significantly enhance the understanding of intricate system dynamics.
  • Future research will be driven by the intersection of complexity science and the digital data era.