Next Article in Journal
Long Range Dependence Prognostics for Bearing Vibration Intensity Chaotic Time Series
Next Article in Special Issue
Predicting Traffic Flow in Local Area Networks by the Largest Lyapunov Exponent
Previous Article in Journal
Entropy Assessment on Direct Contact Condensation of Subsonic Steam Jets in a Water Tank through Numerical Investigation
Previous Article in Special Issue
Cloud Entropy Management System Involving a Fractional Power
Article Menu

Export Article

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

Open AccessArticle
Entropy 2016, 18(1), 22;

Increment Entropy as a Measure of Complexity for Time Series

College of IOT Engineering, Hohai University, Changzhou 213022, China
Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China
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)
Full-Text   |   PDF [2373 KB, uploaded 13 April 2016]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top