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Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency

The horizontal visibility graph is not only a powerful tool for the analysis of complex systems, but also a promising way to analyze time series. In this paper, we present an approach to measure the nonlinear interactions between a non-stationary time series based on the horizontal visibility graph....

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Detalles Bibliográficos
Autores principales: Dong, Keqiang, Che, Haowei, Zou, Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514219/
http://dx.doi.org/10.3390/e21101008
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author Dong, Keqiang
Che, Haowei
Zou, Zhi
author_facet Dong, Keqiang
Che, Haowei
Zou, Zhi
author_sort Dong, Keqiang
collection PubMed
description The horizontal visibility graph is not only a powerful tool for the analysis of complex systems, but also a promising way to analyze time series. In this paper, we present an approach to measure the nonlinear interactions between a non-stationary time series based on the horizontal visibility graph. We describe how a horizontal visibility graph may be calculated based on second-order and third-order statistical moments. We compare the new methods with the first-order measure, and then give examples including stock markets and aero-engine performance parameters. These analyses suggest that measures derived from the horizontal visibility graph may be of particular relevance to the growing interest in quantifying the information exchange between time series.
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spelling pubmed-75142192020-11-09 Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency Dong, Keqiang Che, Haowei Zou, Zhi Entropy (Basel) Article The horizontal visibility graph is not only a powerful tool for the analysis of complex systems, but also a promising way to analyze time series. In this paper, we present an approach to measure the nonlinear interactions between a non-stationary time series based on the horizontal visibility graph. We describe how a horizontal visibility graph may be calculated based on second-order and third-order statistical moments. We compare the new methods with the first-order measure, and then give examples including stock markets and aero-engine performance parameters. These analyses suggest that measures derived from the horizontal visibility graph may be of particular relevance to the growing interest in quantifying the information exchange between time series. MDPI 2019-10-16 /pmc/articles/PMC7514219/ http://dx.doi.org/10.3390/e21101008 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dong, Keqiang
Che, Haowei
Zou, Zhi
Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title_full Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title_fullStr Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title_full_unstemmed Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title_short Multiscale Horizontal Visibility Graph Analysis of Higher-Order Moments for Estimating Statistical Dependency
title_sort multiscale horizontal visibility graph analysis of higher-order moments for estimating statistical dependency
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514219/
http://dx.doi.org/10.3390/e21101008
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