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Correction published on 13 April 2016, see Entropy 2016, 18(4), 133.

Open AccessArticle
Entropy 2016, 18(1), 22; doi:10.3390/e18010022

Increment Entropy as a Measure of Complexity for Time Series

1
College of IOT Engineering, Hohai University, Changzhou 213022, China
2
Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China
3
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editors: J. A. Tenreiro Machado and António M. Lopes
Received: 22 October 2015 / Revised: 4 January 2016 / Accepted: 5 January 2016 / Published: 8 January 2016
(This article belongs to the Special Issue Complex and Fractional Dynamics)
View Full-Text   |   Download PDF [2373 KB, uploaded 13 April 2016]   |  

Abstract

Entropy has been a common index to quantify the complexity of time series in a variety of fields. Here, we introduce an increment entropy to measure the complexity of time series in which each increment is mapped onto a word of two letters, one corresponding to the sign and the other corresponding to the magnitude. Increment entropy (IncrEn) is defined as the Shannon entropy of the words. Simulations on synthetic data and tests on epileptic electroencephalogram (EEG) signals demonstrate its ability of detecting abrupt changes, regardless of the energetic (e.g., spikes or bursts) or structural changes. The computation of IncrEn does not make any assumption on time series, and it can be applicable to arbitrary real-world data. View Full-Text
Keywords: incremental Entropy; complexity; time series; EEG incremental Entropy; complexity; time series; EEG
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, X.; Jiang, A.; Xu, N.; Xue, J. Increment Entropy as a Measure of Complexity for Time Series. Entropy 2016, 18, 22.

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