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Detecting direction of causal interactions between dynamically coupled signals.

Katsuhiko Ishiguro1, Nobuyuki Otsu, Max Lungarella

  • 1Graduate School of Information Science and Technology, University of Tokyo, 113-8656 Tokyo, Japan. ishiguro@cslab.kecl.ntt.co.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 21, 2008
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Summary

This study introduces a new method to map dynamic interdependencies in complex systems. It enhances the reliability of detecting causal interactions using signal prediction error minimization and nonlinear embedding.

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Area of Science:

  • Complex Systems Science
  • Nonlinear Dynamics
  • Signal Processing

Background:

  • Understanding dynamic interdependencies is crucial for analyzing complex systems.
  • Existing methods for causal interaction detection have limitations in reliability and temporal resolution.

Purpose of the Study:

  • To develop a robust technique for temporal localization and directional mapping of dynamic interdependencies.
  • To improve the reliability of detecting intermittent causal interactions in complex systems.

Main Methods:

  • A novel technique weighting sampled values to minimize mutual prediction error between signals.
  • Regularization for smoothing the weight landscape.
  • A nonlinear (polynomial) embedding vector variant.

Main Results:

  • The proposed technique effectively demonstrates temporal localization and directional mapping.
  • Reliability of detecting intermittent causal interactions is significantly enhanced.
  • Performance validated through three numerical examples of coupled chaotic maps.

Conclusions:

  • The presented technique offers a reliable approach for analyzing dynamic interdependencies.
  • It outperforms conventional measures of causal dependency in detecting intermittent interactions.