Permutation extropy: A new time series complexity measure

IF 3.1 3区物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY

Physica A: Statistical Mechanics and its Applications Pub Date : 2025-09-10 DOI:10.1016/j.physa.2025.130951

Ritik Roshan Giri , Suchandan Kayal , Javier E. Contreras-Reyes

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引用次数: 0

Abstract

Several complexity measures have been proposed to understand the complexity of physiological, financial, biological, and other time series that involve real-world problems. Permutation entropy (PE), fractal dimension and Lyapunov exponents are such complexity parameters out of many. The enormous use of PE in specifying complexity of chaotic time series motivates us to propose an alternative complexity parameter in this paper, known as the permutation extropy (PExt) measure. Here, we combine the ideas behind the PE and extropy to construct this new measure. We then validate the proposed measure using logistic, Hénon and Burger chaotic maps. Further, we apply the proposed complexity measure to study the impact of Covid-19 on financial stock market time series data set and to analyze the situation of Covid in India across different phases, considering the WHO data set. The proposed measure demonstrates robustness, fast calculation and invariant with respect to monotonous nonlinear transformation like PE.

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置换外向性：一种新的时间序列复杂度度量

为了理解涉及现实世界问题的生理、金融、生物和其他时间序列的复杂性，已经提出了几种复杂性度量方法。排列熵（PE）、分形维数和李亚普诺夫指数是众多复杂参数中的一类。PE在描述混沌时间序列复杂性方面的大量使用促使我们在本文中提出了一种替代的复杂性参数，称为置换外向性（PExt）测度。在这里，我们结合PE和外部性背后的思想来构建这个新的衡量标准。然后，我们使用logistic、hsamnon和Burger混沌图验证了所提出的度量。此外，我们应用提出的复杂性度量来研究Covid-19对金融股票市场时间序列数据集的影响，并在考虑世卫组织数据集的情况下，分析印度不同阶段的Covid-19情况。该方法对PE等单调非线性变换具有鲁棒性、计算速度快、不变性等特点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physica A: Statistical Mechanics and its Applications 物理-物理：综合

CiteScore

7.20

自引率

9.10%

发文量

852

审稿时长

6.6 months

期刊介绍： Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.