Results 221 to 230 of about 6,104,331 (261)

Conditional entropy of ordinal patterns [PDF]

Physica D: Nonlinear Phenomena, 2014
In this paper we investigate a quantity called conditional entropy of ordinal patterns, akin to the permutation entropy. The conditional entropy of ordinal patterns describes the average diversity of the ordinal patterns succeeding a given ordinal pattern.
Anton M Unakafov, Karsten Keller
exaly   +3 more sources

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Assessing serial dependence in ordinal patterns processes using chi-squared tests with application to EEG data analysis.

Chaos, 2022
We extend Elsinger's work on chi-squared tests for independence using ordinal patterns and investigate the general class of m-dependent ordinal patterns processes, to which belong ordinal patterns processes derived from random walk, white noise, and ...
Arthur Matsuo Yamashita Rios de Sousa, J. Hlinka   +1 more
semanticscholar   +1 more source

Mining Ordinal Patterns For Data Cleaning

Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004., 2005
It is well recognized that sequential pattern mining plays an essential role in many scientific and business domains. In this paper, a new extension of sequential pattern, ordinal pattern, is proposed. An ordinal pattern is an ordinal sequence of attributes, whose values commonly occur in ascending order over data set.
Ya-Bo Liu, Dayou Liu
openaire   +1 more source

Representation based on ordinal patterns for seizure detection in EEG signals

Computers in Biology and Medicine, 2020
EEG signals carry rich information about brain activity and play an important role in the diagnosis and recognition of epilepsy. Numerous algorithms using EEG signals to detect seizures have been developed in recent decades. However, most of them require
Youfang Lin, Ziyu Jia, Yan Ma
exaly   +2 more sources

Pipelined reconfigurable accelerator for ordinal pattern encoding

2014 IEEE 25th International Conference on Application-Specific Systems, Architectures and Processors, 2014
Ordinal analysis is a statistical method for analysing the complexity of time series. This method has been used in characterising dynamic changes in time series, with various applications such as financial risk modelling and biomedical signal processing. Ordinal pattern encoding is a fundamental calculation in ordinal analysis.
Ce Guo, Wayne Luk, Stephen Weston
openaire   +1 more source

Ordinal pattern analysis tutorial

2022
Manuscript describing ordinal pattern analysis for repeated measures data and R package.
openaire   +1 more source

Temporal assessment of terrain complexity through ordinal patterns in LiDAR data

2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)
We use permutation entropy to decipher the intricacies of terrain complexity within Wellington, New Zealand, over two pivotal periods. We establish a link between entropy variations and the spectrum of environmental and anthropogenic influences ...
Keila Barbosa, Alejandro C. Frery
semanticscholar   +1 more source

Causality and the entropy–complexity plane: Robustness and missing ordinal patterns [PDF]

Physica A: Statistical Mechanics and Its Applications, 2012
submitted to Physica ...
Osvaldo A Rosso, Laura C Carpi, Patricia M Saco   +2 more
exaly   +5 more sources

Multifaceted DDoS Attack Prediction by Multivariate Time Series and Ordinal Patterns

2024 IEEE International Conference on Communications Workshops (ICC Workshops)
Distributed Denial of Service (DDoS) attacks are recurrent threats, reaching unprecedented malicious network traffic volume and speed against targets. Predicting attacks is paramount to reduce costs in mitigating or remediating them.
Ligia F. Borges, Anderson Bergamini de Neira, Lucas Albano, Michele Nogueira Lima   +3 more
semanticscholar   +1 more source

Ordinal Pattern Analysis

1986
Our task in writing this chapter is to outline a strategy for analyzing data that we believe to be better suited to most psychological research than the most widely used statistical techniques (e.g., t, F, and chi square tests, product moment correlation, regression, covariance, discriminant, and factor analyses).
Warren Thorngate, Barbara Carroll
openaire   +1 more source