Abstract
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.
Highlights
Through the lens of probability, information and uncertainty can be viewed as related—the more uncertainty there is, the more information we gain by removing that uncertainty
This paper introduces EntropyHub, an open-source toolkit for entropic time series analysis in the MATLAB, Python [44] and Julia [45] programming
The growing number of entropy methods reported in the scientific literature for time series and image analysis warrants new software tools that enable researchers to apply such methods [2, 3, 38]
Summary
Through the lens of probability, information and uncertainty can be viewed as related—the more uncertainty there is, the more information we gain by removing that uncertainty. Numerous variants have since been derived from conditional entropy, and to a lesser extent Shannon’s entropy, to estimate the information content of time series data across various scientific domains [2], resulting in what has recently been termed “the entropy universe” [3]. This universe of entropies continues to expand as more and more methods are derived with improved statistical properties over their precursors, such as robustness to short signal lengths [4,5,6,7], resilience to noise [8,9,10], insensitivity to amplitude fluctuations [11,12,13]. New entropy variants are being identified which quantify the variability of time series data in specific applications, including assessments of cardiac disease from electrocardiograms [14,15,16], and examinations of machine failure from vibration signals [17, 18]
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