Artículos de publicaciones periódicas
Permanent URI for this collection
Browse
Browsing Artículos de publicaciones periódicas by Subject "ANALISIS DE SERIES DE TIEMPO"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
artículo de publicación periódica.listelement.badge Bandt-Pompe symbolization dynamics for time series with tied values: a data-driven approach(2018-07) Traversaro Varela, Francisco; Redelico, Francisco; Risk, Marcelo; Frery, Alejandro C.; Rosso, Osvaldo A."In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers."artículo de publicación periódica.listelement.badge Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy(2018-04) Traversaro Varela, Francisco; Redelico, Francisco"In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity is the Permutation Entropy. But there is still no known method to determine the accuracy of this measure. There has been little research on the statistical properties of this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/f α noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals."