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

Efficiently Measuring Complexity on the Basis of Real-World Data

Received: 23 August 2013; Accepted: 9 October 2013 / Published: 16 October 2013
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.
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
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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.

AMA Style

Unakafova VA, Keller K. Efficiently Measuring Complexity on the Basis of Real-World Data. Entropy. 2013; 15(10):4392-4415.

Chicago/Turabian Style

Unakafova, Valentina A.; Keller, Karsten. 2013. "Efficiently Measuring Complexity on the Basis of Real-World Data." Entropy 15, no. 10: 4392-4415.

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