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Complexitymeasures.jl: Scalable Software To Unify And Accelerate Entropy And Complexity Timeseries Analysis.

Home
Research Domains
Information And Computing Sciences
Software Engineering
Formal Methods For Software
Complexitymeasures.jl: Scalable Software To Unify And Accelerate Entropy And Complexity Timeseries Analysis.

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ComplexityMeasures.jl: Scalable software to unify and accelerate entropy and complexity timeseries analysis.

George Datseris¹, Kristian Agasøster Haaga^2,3,4

¹Department of Mathematics and Statistics, University of Exeter, Exeter, United Kingdom.

|June 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

ComplexityMeasures.jl is a new open-source software for nonlinear timeseries analysis, offering 1638 complexity measures. Its composable design ensures high performance and extensibility for researchers.

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Area of Science:

Nonlinear dynamics and time series analysis.

Background:

The proliferation of entropy and complexity measures in nonlinear time series analysis poses challenges for software development and researcher decision-making.
A unified, sustainable software solution is needed to manage and apply these diverse measures effectively.

Purpose of the Study:

To introduce ComplexityMeasures.jl, an open-source software designed for nonlinear time series analysis.
To provide researchers with a comprehensive, high-performance tool for accessing and utilizing a vast array of complexity measures.
To demonstrate the software's design, performance, and extendability compared to existing alternatives.

Main Methods:

Development of ComplexityMeasures.jl with a mathematically rigorous, composable design.
Implementation of 1638 distinct complexity measures within the software.

Comparative analysis of ComplexityMeasures.jl against alternative software solutions based on performance, measure count, reliability, and extendability.

Main Results:

ComplexityMeasures.jl offers 1638 complexity measures with highly efficient code (2.3 lines/measure).
The software demonstrates superior computational performance, reliability, and extendability compared to alternatives.
It is integrated into the DynamicalSystems.jl library, promoting open-source development practices.

Conclusions:

ComplexityMeasures.jl provides a sustainable and performant solution for nonlinear time series analysis.
Its design facilitates informed measure selection and accelerates complexity-related research.
The software fosters a collaborative community for ongoing development and contribution.