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Symbolic approach for measuring temporal "irreversibility"

Daw1, Finney, Kennel

  • 1Engineering Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-8088, USA.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|November 23, 2000
PubMed
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We introduce a new symbolic method to measure temporal irreversibility in data. This approach is efficient, robust to noise, and helps identify complex data-generating processes beyond simple linear models.

Area of Science:

  • Time-series analysis
  • Nonlinear dynamics
  • Statistical physics

Background:

  • Temporal irreversibility is a key characteristic distinguishing complex dynamical systems from simpler linear or Gaussian processes.
  • Identifying irreversibility is crucial for accurately modeling the underlying mechanisms of observed data.
  • Existing methods can be computationally intensive or sensitive to noise.

Purpose of the Study:

  • To present a computationally efficient and noise-robust symbolic method for quantifying temporal irreversibility in time-series data.
  • To introduce the "false flipped symbols" algorithm for detecting temporal irreversibility without surrogate data.
  • To demonstrate the applicability of the method for characterizing experimental data and model selection.

Main Methods:

Related Experiment Videos

  • Development of a symbolic approach to analyze time-series measurements.
  • Implementation of the "false flipped symbols" algorithm to detect temporal irreversibility.
  • Application and validation using numerical model outputs and experimental measurements.

    • Main Results:

    • The "false flipped symbols" algorithm successfully identifies temporal irreversibility.
    • The symbolic method offers computational efficiency and robustness against noise.
    • The approach aids in distinguishing complex dynamics from simpler models.

    Conclusions:

    • Symbolic analysis provides an effective and practical tool for measuring temporal irreversibility.
    • The proposed algorithm facilitates the exclusion of Gaussian linear dynamics as data-generating processes.
    • This method enhances the ability to select appropriate models for complex time-series data.