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

Efficiently Measuring Complexity on the Basis of Real-World Data

1,2,*  and 1
Received: 23 August 2013 / Accepted: 9 October 2013 / Published: 16 October 2013
Download PDF [616 KB, 24 February 2015; original version 24 February 2015]

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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

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