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Related Experiment Video

Updated: Jul 3, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Easily adaptable complexity measure for finite time series.

Da-Guan Ke¹, Qin-Ye Tong

¹Department of Mathematics, Zhejiang University, Hangzhou 310027, China and Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China. kdg@zju.edu.cn

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|July 23, 2008

Summary

We developed a new complexity measure for time series data. This robust and adaptable measure, derived from Kolmogorov complexity, offers a unified approach to quantifying complexity across various systems.

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Last Updated: Jul 3, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

Area of Science:

Complexity Science
Time Series Analysis
Information Theory

Background:

Traditional complexity measures often conflict and fail to satisfy all desired criteria.
Kolmogorov complexity is typically associated with randomness, not a general complexity measure.
Existing methods struggle with noise and transformations.

Purpose of the Study:

To introduce a novel complexity measure for finite time series.
To develop a measure that is invariant to monotonic transformations and robust to noise.
To reconcile conflicting criteria for complexity measurement.

Main Methods:

The proposed measure is derived from Kolmogorov complexity.
It is designed to be invariant under monotonic transformations.
The method demonstrates robustness against noise in time series data.

Main Results:

A new complexity measure satisfying multiple, often conflicting, criteria is presented.
The measure shows invariance to monotonic transformations.
The approach offers robustness against noise, enhancing its practical applicability.

Conclusions:

The developed complexity measure provides a versatile tool for analyzing time series.
This work potentially bridges symbolic dynamics and permutation dynamics.
The findings offer a new perspective on applying Kolmogorov complexity for broader measures.