Ricci L, Perinelli A. Estimating Permutation Entropy Variability via Surrogate Time Series.
ENTROPY (BASEL, SWITZERLAND) 2022;
24:853. [PMID:
35885077 PMCID:
PMC9318716 DOI:
10.3390/e24070853]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/19/2022] [Accepted: 06/20/2022] [Indexed: 01/27/2023]
Abstract
In the last decade permutation entropy (PE) has become a popular tool to analyze the degree of randomness within a time series. In typical applications, changes in the dynamics of a source are inferred by observing changes of PE computed on different time series generated by that source. However, most works neglect the crucial question related to the statistical significance of these changes. The main reason probably lies in the difficulty of assessing, out of a single time series, not only the PE value, but also its uncertainty. In this paper we propose a method to overcome this issue by using generation of surrogate time series. The analysis conducted on both synthetic and experimental time series shows the reliability of the approach, which can be promptly implemented by means of widely available numerical tools. The method is computationally affordable for a broad range of users.
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