introduction: in this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. epilepsy is considered as a dynamical change in nonlinear and complex brain system. the ability of the proposed measure for characterizing the normal and epileptic eeg signals when the signal is short or is...

Statistically significant pattern mining (SSPM), which evaluates each via a hypothesis test, is an essential and challenging data task for knowledge discovery. We introduce preference relation between patterns aim to discover the most preferred under constraint of statistical significance, has never been considered in existing SSPM problems. propose iterative multiple testing procedure that can...

In this work we discuss the structure of ordinal pattern distributions obtained from orbits of dynamical systems. In particular, we consider the extreme cases of systems with a singular pattern distribution and of realizing each ordinal pattern of any order, respectively. Finally, we review results relating the Kolmogorov-Sinai entropy and the topological entropy of one-dimensional dynamical sy...

In this article, we show that the recently introduced ordinal pattern dependence fits into axiomatic framework of general multivariate measures, i.e., measures between two random objects. Furthermore, consider generalizations established univariate like Kendall’s ?, Spearman’s ? and Pearson’s correlation coefficient. Among these, only ? proves to take dynamical vectors stemming from multidimens...

The distribution of ordinal patterns in time series has been found to reflect important qualitative features of the underlying system dynamics. Abrupt changes in the dynamics typically result in clearly visible differences between the distributions before and after the break. Recurring dynamical regimes can be discovered by classifying the distributions in different parts of the time series. Th...

We study global climate networks constructed by means of ordinal time series analysis. Climate interdependencies among the nodes are quantified by the mutual information, computed from time series of monthly-averaged surface air temperature anomalies, and from their symbolic ordinal representation (OP). This analysis allows identifying topological changes in the network when varying the time-in...

In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentat...