Efficiently Measuring Complexity on the Basis of Real-World Data

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.