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Entropy 2013, 15(10), 4392-4415; doi:10.3390/e15104392

Efficiently Measuring Complexity on the Basis of Real-World Data

1
Institute of Mathematics, University of Lübeck, Lübeck D-23562, Germany
2
Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Lübeck D-23562, Germany
*
Author to whom correspondence should be addressed.
Received: 23 August 2013 / Accepted: 9 October 2013 / Published: 16 October 2013
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Abstract

Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order d, considering the fact that they are “overlapped” in d points, and on precomputing successive values of the permutation entropy related to “overlapping” successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time.
Keywords: permutation entropy; ordinal patterns; efficient computing; complexity permutation entropy; ordinal patterns; efficient computing; complexity
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Unakafova, V.A.; Keller, K. Efficiently Measuring Complexity on the Basis of Real-World Data. Entropy 2013, 15, 4392-4415.

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