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Assessing Time Series Reversibility through Permutation Patterns.

Massimiliano Zanin1,2, Alejandro Rodríguez-González1, Ernestina Menasalvas Ruiz1

  • 1Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, 28040 Madrid, Spain.

Entropy (Basel, Switzerland)
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Summary
This summary is machine-generated.

We introduce a new, efficient method to measure time irreversibility in data using permutation entropy. This approach reveals complex dynamics in financial time series, offering advantages over existing techniques for data analysis.

Keywords:
efficient market hypothesispermutation entropytime irreversibilityvisibility graphs

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Area of Science:

  • Complex systems analysis
  • Statistical physics
  • Time series analysis

Background:

  • Time irreversibility is a key characteristic of non-equilibrium systems.
  • It impacts statistical properties and predictability of time series.
  • Existing methods for quantifying irreversibility are often computationally intensive.

Purpose of the Study:

  • To develop a novel, computationally efficient method for quantifying time irreversibility.
  • To apply this method to financial time series analysis.
  • To compare the new method with existing techniques like visibility graphs.

Main Methods:

  • A new method based on permutation entropy is proposed.
  • The method is parameter-free and temporally local.
  • It offers straightforward statistical tests and fast convergence.

Main Results:

  • The permutation entropy method effectively quantifies time irreversibility in financial data.
  • Stocks and indices exhibit complex and rich irreversibility dynamics.
  • The proposed method demonstrates advantages over visibility graph approaches.

Conclusions:

  • The permutation entropy method provides an efficient and robust tool for analyzing time irreversibility.
  • This approach offers valuable insights into financial market dynamics.
  • It facilitates improved data analysis and interpretation in finance.