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ordpy: A Python package for data analysis with permutation entropy and ordinal network methods.

Arthur A B Pessa1, Haroldo V Ribeiro1

  • 1Departamento de Física, Universidade Estadual de Maringá, Maringá, PR 87020-900, Brazil.

Chaos (Woodbury, N.Y.)
|July 9, 2021
PubMed
Summary
This summary is machine-generated.

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Permutation entropy, a key tool for time series analysis, now has a unified Python package called ordpy. This open-source module offers a comprehensive suite of methods for analyzing time series and image data.

Area of Science:

  • Complex Systems
  • Data Science
  • Information Theory

Background:

  • Permutation entropy is a vital method for time series analysis, originating from Bandt and Pompe's work.
  • This technique has inspired a framework for symbolic sequence mapping, leading to tools like ordinal networks.
  • Existing computational tools for these methods are fragmented, lacking a unified software solution.

Purpose of the Study:

  • To introduce ordpy, a novel, open-source Python module.
  • To provide a unified computational framework for permutation entropy and related methods.
  • To facilitate the analysis of one-dimensional (time series) and two-dimensional (image) data.

Main Methods:

  • Implementation of permutation entropy and its variants (Tsallis, Rényi).

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  • Development of tools for complexity-entropy plane and curves analysis.
  • Inclusion of methods for analyzing missing ordinal patterns and transitions, and ordinal networks.
  • Support for multiscale generalizations for both 1D and 2D data.

    • Main Results:

    • ordpy offers a consolidated platform for diverse time series and image analysis techniques.
    • The module successfully replicates several established literature results, validating its functionality.
    • Provides efficient computation for permutation entropy, ordinal networks, and related complexity measures.

    Conclusions:

    • ordpy addresses the fragmentation in computational tools for permutation entropy and its framework.
    • The module serves as a valuable resource for researchers in time series and image analysis.
    • Facilitates deeper insights into complex systems through advanced entropy and network measures.