EntropyHub: An open-source toolkit for entropic time series analysis.

An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be...

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Autores principales:	Matthew W Flood, Bernd Grimm
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/735f05523ac14206971dba5db4845a5b
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spelling	oai:doaj.org-article:735f05523ac14206971dba5db4845a5b2021-12-02T20:04:21ZEntropyHub: An open-source toolkit for entropic time series analysis.1932-620310.1371/journal.pone.0259448https://doaj.org/article/735f05523ac14206971dba5db4845a5b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259448https://doaj.org/toc/1932-6203An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.Matthew W FloodBernd GrimmPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259448 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthew W Flood Bernd Grimm EntropyHub: An open-source toolkit for entropic time series analysis.
description	An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.
format	article
author	Matthew W Flood Bernd Grimm
author_facet	Matthew W Flood Bernd Grimm
author_sort	Matthew W Flood
title	EntropyHub: An open-source toolkit for entropic time series analysis.
title_short	EntropyHub: An open-source toolkit for entropic time series analysis.
title_full	EntropyHub: An open-source toolkit for entropic time series analysis.
title_fullStr	EntropyHub: An open-source toolkit for entropic time series analysis.
title_full_unstemmed	EntropyHub: An open-source toolkit for entropic time series analysis.
title_sort	entropyhub: an open-source toolkit for entropic time series analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/735f05523ac14206971dba5db4845a5b
work_keys_str_mv	AT matthewwflood entropyhubanopensourcetoolkitforentropictimeseriesanalysis AT berndgrimm entropyhubanopensourcetoolkitforentropictimeseriesanalysis
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EntropyHub: An open-source toolkit for entropic time series analysis.

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