The investigation of ordinal pattern distributions is a novel approach to quantifying the complexity of time series and detecting changes in the underlying dynamics. Being fast and robust against monotone distortions, this method is particularly well-suited for the analysis of long biophysical time series where the exact calibration of the measurement device is unknown. In this paper we investi...

Zunino et al. analyzed the problem of discrimination of developed and emergent markets by the use of ordinal patterns methods: number of forbidden patterns and ordinal pattern probability distribution as a basis for entropy and statistical measure of complexity. In this paper we apply the same methodology for the analysis of Polish stock market (index WIG). The results indicate that Polish mark...

We describe a convergent procedure for fitting the common factor analysis model to multivariate data whose variables may be nominal, ordinal or interval. Any mixture of measurement levels is permitted. There may be any pattern of missing data. As distinguished from previous work, the nonmetric relations (nominal or ordinal) are assumed on the raw observations (not on the correlations), and the ...

Linked Data interfaces exist in many flavours, as evidenced by subject pages, sparql endpoints, triple pattern interfaces, and data dumps. These interfaces are mostly used to retrieve parts of a complete dataset, such parts can for example be defined by ranges in one or more dimensions. Filtering Linked Data by dimensions such as time range, geospatial area, or genomic location, requires the lo...

In this paper, the orders induced by the residual implications obtained from uninorms are investigated. A necessary and sufficient condition is presented so that the ordinal sum of fuzzy implications satisfies the law of importation with a t-norm $T$. Some relationships between the orders induced by an ordinal sum implication and its summands are determined. The algebraic structures obtained fr...

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...

Sequential learning (SL) refers to the ability to learn the temporal and ordinal patterns of one’s environment. Whereas research on the learning of ordinal patterns is common, the learning of temporal patterns within sequential events has been far less studied. The current study examines the effects of synchronous and asynchronous temporal patterns on visual sequential learning. We hypothesize ...