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Estimating Permutation Entropy Variability via Surrogate Time Series
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, mo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318716/ https://www.ncbi.nlm.nih.gov/pubmed/35885077 http://dx.doi.org/10.3390/e24070853 |
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author | Ricci, Leonardo Perinelli, Alessio |
author_facet | Ricci, Leonardo Perinelli, Alessio |
author_sort | Ricci, Leonardo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9318716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93187162022-07-27 Estimating Permutation Entropy Variability via Surrogate Time Series Ricci, Leonardo Perinelli, Alessio Entropy (Basel) Article 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. MDPI 2022-06-22 /pmc/articles/PMC9318716/ /pubmed/35885077 http://dx.doi.org/10.3390/e24070853 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ricci, Leonardo Perinelli, Alessio Estimating Permutation Entropy Variability via Surrogate Time Series |
title | Estimating Permutation Entropy Variability via Surrogate Time Series |
title_full | Estimating Permutation Entropy Variability via Surrogate Time Series |
title_fullStr | Estimating Permutation Entropy Variability via Surrogate Time Series |
title_full_unstemmed | Estimating Permutation Entropy Variability via Surrogate Time Series |
title_short | Estimating Permutation Entropy Variability via Surrogate Time Series |
title_sort | estimating permutation entropy variability via surrogate time series |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318716/ https://www.ncbi.nlm.nih.gov/pubmed/35885077 http://dx.doi.org/10.3390/e24070853 |
work_keys_str_mv | AT riccileonardo estimatingpermutationentropyvariabilityviasurrogatetimeseries AT perinellialessio estimatingpermutationentropyvariabilityviasurrogatetimeseries |