Results 231 to 240 of about 6,104,331 (261)

Characterizing complexity of non-invertible chaotic maps in the Shannon–Fisher information plane with ordinal patterns

, 2020
Being able to distinguish the different types of dynamics present in a given nonlinear system is of great importance in complex dynamics. It allows to characterize the system, find similarities and differences with other nonlinear systems, and classify ...
David Spichak, Audrey Kupetsky, A. Aragoneses   +2 more
semanticscholar   +1 more source

Missing ordinal patterns in correlated noises

Physica A: Statistical Mechanics and its Applications, 2010
Abstract Recent research aiming at the distinction between deterministic or stochastic behavior in observational time series has looked into the properties of the “ordinal patterns” [C. Bandt, B. Pompe, Phys. Rev. Lett. 88 (2002) 174102]. In particular, new insight has been obtained considering the emergence of the so-called “forbidden ordinal ...
Carpi, Laura C., Saco, Patricia M., Rosso, O. A.   +2 more
openaire   +1 more source

Revisiting the decay of missing ordinal patterns in long-term correlated time series

Physica A: Statistical Mechanics and Its Applications, 2019
We revisit the decay of missing ordinal patterns in long-term correlated time series. More precisely, a stretched exponential model is proposed to describe more appropriately how the number of missing ordinal patterns decreases as a function of the time ...
Felipe Olivares, Luciano Zunino, Dario G Perez   +2 more
exaly   +2 more sources

Learning and distinguishing time series dynamics via ordinal patterns transition graphs

Applied Mathematics and Computation, 2019
Strategies based on the extraction of measures from ordinal patterns transformation, such as probability distributions and transition graphs, have reached relevant advancements in distinguishing different time series dynamics. However, the reliability of
João B. Borges Neto, Heitor S. Ramos, R. Mini, O. Rosso, A. Frery, A. Loureiro   +5 more
semanticscholar   +1 more source

Ordinal pattern based similarity analysis for EEG recordings

Clinical Neurophysiology, 2010
Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel dissimilarity ...
Gaoxiang, Ouyang, Chuangyin, Dang, Douglas A, Richards, Xiaoli, Li   +3 more
openaire   +2 more sources

Dissimilarity measure based on ordinal pattern for physiological signals

Communications in Nonlinear Science and Numerical Simulation, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jing Wang, Pengjian Shang, Wenbin Shi, Xingran Cui   +3 more
openaire   +2 more sources

Variance of permutation entropy and the influence of ordinal pattern selection

Physical Review E, 2017
Permutation entropy (PE) is a widely used measure for complexity, often used to distinguish between complex systems (or complex systems in different states). Here, the PE variance for a stationary time series is derived, and the influence of ordinal pattern selection, specifically whether the ordinal patterns are permitted to overlap or not, is ...
Douglas J, Little, Deb M, Kane
openaire   +2 more sources

Ordinal Pattern: A New Descriptor for Brain Connectivity Networks

IEEE Transactions on Medical Imaging, 2018
Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks.
Daoqiang Zhang, Jiashuang Huang, Biao Jie, Junqiang Du, Liyang Tu, Mingxia Liu 0001   +5 more
openaire   +2 more sources

DETECTING DETERMINISM IN TIME SERIES WITH ORDINAL PATTERNS: A COMPARATIVE STUDY

International Journal of Bifurcation and Chaos, 2010
Detecting determinism in univariate and multivariate time series is difficult if the underlying process is nonlinear, and the noise level is high. In a previous paper, the authors proposed a method based on observable ordinal patterns. This method exploits the robustness of admissible ordinal patterns against observational noise, and the super ...
José María Amigó, Samuel Zambrano, Miguel A. F. Sanjuán   +2 more
openaire   +2 more sources

Using spatial ordinal patterns for non-parametric testing of spatial dependence

Spatial Statistics, 2023
Christian H. Weiß, Hee-Young Kim
semanticscholar   +1 more source