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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....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-7514219 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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