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Learning driver-response relationships from synchronization patterns.

R Q Quiroga1, J Arnhold, P Grassberger

  • 1John von Neumann Institute for Computing, Forschungszentrum Jülich GmbH, Germany.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|October 14, 2000
PubMed
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Causal relationships from time series data are difficult to detect. Finite data length and system dimensionality significantly impact interdependence measures, complicating driver-response detection.

Area of Science:

  • Complex Systems
  • Time Series Analysis
  • Nonlinear Dynamics

Background:

  • Recent claims suggest causal relationships can be deduced from time series interdependencies.
  • Interdependence measures are proposed as tools for identifying driver-response dynamics.

Purpose of the Study:

  • To evaluate the reliability of recent interdependence measures for inferring causality.
  • To investigate the influence of finite data length and system dimensionality on causality detection.

Main Methods:

  • Application of two novel interdependence measures to asymmetrically coupled Lorenz, Roessler, and Hénon models.
  • Analysis of systems exhibiting generalized synchronization under varying data conditions.

Main Results:

Related Experiment Videos

  • Estimated interdependencies are strongly influenced by the effective dimension of coupled systems at typical neighborhood sizes.
  • Finite time series length complicates the detection of causal driver-response relationships.
  • Slight variations in interdependence measure formulations exhibit differing sensitivities.

Conclusions:

  • Current interdependence measures may not reliably infer causality from real-world, finite time series data.
  • Effective dimension and data length are critical factors affecting the accuracy of causal inference.