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Identifying hidden common causes from bivariate time series: a method using recurrence plots.

Yoshito Hirata1, Kazuyuki Aihara

  • 1Institute of Industrial Science, The University of Tokyo, Tokyo, Japan. yoshito@sat.t.u-tokyo.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|April 7, 2010
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Summary
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This study introduces a novel method to find hidden common causes in time series data using recurrence plots. The technique identifies related series by analyzing simultaneous recurrences and directional coupling, successfully applied to wind data analysis.

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Area of Science:

  • Complex Systems
  • Time Series Analysis
  • Causality Inference

Background:

  • Identifying causal relationships in complex systems is challenging.
  • Bivariate time series often obscure underlying common causes.
  • Recurrence plot analysis offers a non-linear approach to time series investigation.

Purpose of the Study:

  • To develop a method for inferring hidden common causes from bivariate time series.
  • To distinguish between direct causality and common cause influence.
  • To validate the proposed method using real-world data.

Main Methods:

  • Utilizing recurrence plots to visualize time series dynamics.
  • Detecting series relatedness through excessive simultaneous recurrences.
  • Applying a noncoverage property of recurrence plots to rule out directional coupling.

Main Results:

  • The proposed method successfully infers the presence of hidden common causes.
  • Simultaneous recurrences indicate related time series.
  • Noncoverage properties effectively deny directional coupling between series.
  • The method demonstrated applicability on real wind data.

Conclusions:

  • The developed method provides a robust approach for uncovering hidden common causes in bivariate time series.
  • Recurrence plot analysis is a valuable tool for causality inference in complex systems.
  • The findings have implications for understanding interconnected phenomena in various scientific domains.