Characterization of time series via Rényi complexity–entropy curves

• Published in: Physica A Vol. 498; pp. 74 - 85
• Main Authors: Jauregui, M., Zunino, L., Lenzi, E.K., Mendes, R.S., Ribeiro, H.V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-05-2018
• Subjects: Complexity measures; Ordinal patterns probabilities; Rényi entropy; Time series; Ordinal patterns probabilities; Rényi entropy; Complexity measures; Time series
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2018.01.026

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity–entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity–entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity–entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity–entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity–entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
•A new method for characterizing time series is proposed.•The method is based on the Rényi entropy.•The approach is useful for distinguishing noise from chaos.