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Monitoring the informational efficiency of European corporate bond markets with dynamical permutation min-entropy
(Elsevier Science, 2016-03)
In this paper the permutation min-entropy has been implemented to unveil the presence of temporal structures in the daily values of European corporate bond indices from April 2001 to August 2015. More precisely, the ...
Information quantifiers and unpredictability in the COVID-19 time-series data
(Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica., 2023)
Permutation entropy of fractional brownian motion and fractional gaussian noise
(ELSEVIER SCIENCE B.V., 2008)
Time-series prediction with BEMCA approach: Application to short rainfall series
(Institute of Electrical and Electronics Engineers Inc., 2018)
This paper presents a new method to forecast short rainfall time-series. The new framework is by means of Bayesian enhanced modified combined approach (BEMCA) using permutation and relative entropy with Bayesian inference. ...
Comparing different approaches to compute Permutation Entropy with coarse time series
(Elsevier, 2019)
Abstract: Bandt and Pompe introduced Permutation Entropy as a complexity measure and has
been widely used in time series analysis and in many fields of nonlinear dynamics. In
theory these time series come from a process ...
Permutation min-entropy: An improved quantifier for unveiling subtle temporal correlations
(IOP Publishing, 2015-01)
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 ...
Feature evaluation for unsupervised bioacoustic signal segmentation of anuran calls
(Pergamon-Elsevier Science Ltd, 2018-09)
We present a comprehensive study of temporal Low-Level acoustic Descriptors (LLDs) to automatically segment anuran calls in audio streams. The acoustic segmentation, or syllable extraction, is a key task shared by most of ...