Browsing by Author "Olivares, Felipe"
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artículo de publicación periódica.listelement.badge Permutation entropy based time series analysis: equalities in the input signal can lead to false conclusions(2017-06) Zunino, Luciano; Olivares, Felipe; Scholkmann, Felix; Rosso, Osvaldo A."A symbolic encoding scheme, based on the ordinal relation between the amplitude of neighboring values of a given data sequence, should be implemented before estimating the permutation entropy. Consequently, equalities in the analyzed signal, i.e. repeated equal values, deserve special attention and treatment. In this work, we carefully study the effect that the presence of equalities has on permutation entropy estimated values when these ties are symbolized, as it is commonly done, according to their order of appearance. On the one hand, the analysis of computer-generated time series is initially developed to understand the incidence of repeated values on permutation entropy estimations in controlled scenarios. The presence of temporal correlations is erroneously concluded when true pseudorandom time series with low amplitude resolutions are considered. On the other hand, the analysis of real-world data is included to illustrate how the presence of a significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts."artículo de publicación periódica.listelement.badge Permutation min-entropy: an improved quantifier for unveiling subtle temporal correlations(2015-01) Zunino, Luciano; Olivares, Felipe; Rosso, Osvaldo A."The aim of this letter is to introduce the permutation min-entropy as an improved symbolic tool for identifying the existence of hidden temporal correlations in time series. On the one hand, analytical results obtained for the fractional Brownian motion stochastic model theoretically support this hypothesis. On the other hand, the analysis of several computer-generated and experimentally observed time series illustrate that the proposed symbolic quantifier is a versatile and practical tool for identifying the presence of subtle temporal structures in complex dynamical systems. Comparisons against the results obtained with other tools confirm its usefulness as an alternative and/or complementary measure of temporal correlations."artículo de publicación periódica.listelement.badge A simple and fast representation space for classifying complex time series(2017-03) Zunino, Luciano; Olivares, Felipe; Fernández Bariviera, Aurelio; Rosso, Osvaldo A."In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease. "