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Lagged multi-affine height correlation analysis for exploring lagged correlations in complex systems.

Fang Wang1, Lin Wang2, Yuming Chen3

  • 1College of Science/Agricultural Mathematical Modeling and Data Processing Center, Hunan Agricultural University, Changsha 410128, People's Republic of China.

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

We developed a new method to find hidden time-lagged correlations in complex systems. This approach successfully detected these correlations in simulated and real-world data.

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Area of Science:

  • Complex Systems Analysis
  • Time Series Analysis
  • Statistical Physics

Background:

  • Complex systems often exhibit hidden temporal dependencies.
  • Traditional correlation analysis may fail to capture lagged relationships.

Purpose of the Study:

  • To introduce a novel method for detecting time-lagged correlations.
  • To validate the method's efficacy in simulated and real-world complex systems.

Main Methods:

  • Incorporation of a time-lagged operator into multi-affine height correlation analysis (MA-HCA).
  • Application to artificially simulated data for feasibility testing.
  • Analysis of time series from three distinct real-world complex systems.

Main Results:

  • The proposed lagged MA-HCA method successfully detected the presence of lagged correlations.
  • Demonstrated feasibility in identifying temporal dependencies in simulated data.
  • Revealed lagged correlations within real-world complex system data.

Conclusions:

  • The lagged MA-HCA is an effective tool for analyzing hidden temporal structures.
  • This method enhances the understanding of dynamics in complex systems.
  • Provides a new approach for exploring lagged correlations in diverse datasets.