Detecting dynamical changes in time series using the permutation entropy

Yinhe Cao; Wen wen Tung; J. B. Gao; V. A. Protopopescu; L. M. Hively

doi:10.1103/PhysRevE.70.046217

Detecting dynamical changes in time series using the permutation entropy

Yinhe Cao, Wen wen Tung, J. B. Gao, V. A. Protopopescu, L. M. Hively

Computational Sciences & Engineering Div

Research output: Contribution to journal › Article › peer-review

110 Scopus citations

Abstract

Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.

Original language	English
Pages (from-to)	7
Number of pages	1
Journal	Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume	70
Issue number	4
DOIs	https://doi.org/10.1103/PhysRevE.70.046217
State	Published - 2004

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10.1103/PhysRevE.70.046217

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abstract = "Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.",

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AU - Hively, L. M.

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AB - Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.

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Detecting dynamical changes in time series using the permutation entropy

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