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Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot.

Xiaobi Chen1, Guanghua Xu1,2,3, Bo He1

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.

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|June 30, 2023
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Summary
This summary is machine-generated.

A novel method using ordinal pattern transition networks measures bivariate time series synchronization. This crossplot transition entropy approach is efficient, robust, and effective for short time series analysis.

Keywords:
crossplotordinal pattern transition networksynchronizationtransition entropy

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Area of Science:

  • Complex Systems
  • Time Series Analysis
  • Network Science

Background:

  • Synchronization of bivariate time series is a critical area of research.
  • Existing synchronization measures have limitations.
  • Ordinal patterns offer a novel perspective for time series analysis.

Purpose of the Study:

  • To propose a new method for measuring bivariate time series synchronization.
  • To introduce the concept of crossplot transition entropy.
  • To evaluate the performance and characteristics of the new method.

Main Methods:

  • Ordinal pattern transition networks are integrated with crossplots.
  • Crossplots are partitioned and coded to form network nodes.
  • A directed weighted network is constructed based on temporal adjacency.
  • Crossplot transition entropy is calculated as a synchronization indicator.

Main Results:

  • The proposed method demonstrates efficiency, robustness, and good consistency.
  • It is particularly suitable for analyzing short time series.
  • The method shows advantages in easy parameter setting.
  • Analysis of the unidirectional coupled Lorentz model validates its performance.
  • Useful results were obtained from electroencephalogram (EEG) data.

Conclusions:

  • The crossplot transition entropy method provides a reliable and versatile tool for bivariate time series synchronization.
  • This novel approach enhances the analysis of complex systems and biological signals.
  • The method offers practical advantages over existing techniques.