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Detecting time lag between a pair of time series using visibility graph algorithm.

Majnu John1,2,3,4, Janina Ferbinteanu5,6

  • 1Department of Mathematics, Hofstra University, Hempstead, NY, USA.

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Summary
This summary is machine-generated.

This study introduces a novel visibility graph method for accurately estimating time lags between time series, outperforming traditional cross-correlation techniques in simulations and real-world applications.

Keywords:
Time seriescorrelogramcross correlationenvironmental epidemiologylocal field potentialsneuroscienceozone levelstime lagtransfer functionvisibility graph algorithm

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Area of Science:

  • Time series analysis
  • Graph theory applications
  • Statistical modeling

Background:

  • Estimating time lags between paired time series is crucial for many applications.
  • Current cross-correlation methods have limitations in accurately detecting these lags.
  • The visibility graph algorithm offers a novel approach to time series analysis.

Purpose of the Study:

  • To introduce and evaluate a new method for quantifying time lags using the visibility graph algorithm.
  • To compare the performance of the new method against traditional cross-correlation techniques.
  • To explore the applicability of the new method in neuroscience and environmental epidemiology.

Main Methods:

  • Adapting the visibility graph algorithm to convert time series into mathematical graphs.
  • Conducting extensive simulation studies to assess method performance under various scenarios.
  • Developing a likelihood-based parametric modeling framework for uncertainty quantification and hypothesis testing.

Main Results:

  • The visibility graph method accurately and unambiguously identified time lags in simulated data where cross-correlation failed.
  • Simulation studies provided insights into scenarios where the new method excels and where it may be outperformed.
  • The method was successfully applied to case studies in neuroscience and environmental epidemiology.

Conclusions:

  • The visibility graph method presents a robust alternative for time lag estimation in time series analysis.
  • This approach offers improved accuracy and clarity compared to conventional cross-correlation methods.
  • The method has demonstrated practical utility in diverse scientific fields.