Cargando…

Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series

In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relation...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Meihui, Gao, Xiangyun, An, Haizhong, Li, Huajiao, Sun, Bowen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585247/
https://www.ncbi.nlm.nih.gov/pubmed/28874713
http://dx.doi.org/10.1038/s41598-017-10759-3
_version_ 1783261581992263680
author Jiang, Meihui
Gao, Xiangyun
An, Haizhong
Li, Huajiao
Sun, Bowen
author_facet Jiang, Meihui
Gao, Xiangyun
An, Haizhong
Li, Huajiao
Sun, Bowen
author_sort Jiang, Meihui
collection PubMed
description In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.
format Online
Article
Text
id pubmed-5585247
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-55852472017-09-06 Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series Jiang, Meihui Gao, Xiangyun An, Haizhong Li, Huajiao Sun, Bowen Sci Rep Article In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers. Nature Publishing Group UK 2017-09-05 /pmc/articles/PMC5585247/ /pubmed/28874713 http://dx.doi.org/10.1038/s41598-017-10759-3 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Jiang, Meihui
Gao, Xiangyun
An, Haizhong
Li, Huajiao
Sun, Bowen
Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title_full Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title_fullStr Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title_full_unstemmed Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title_short Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
title_sort reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5585247/
https://www.ncbi.nlm.nih.gov/pubmed/28874713
http://dx.doi.org/10.1038/s41598-017-10759-3
work_keys_str_mv AT jiangmeihui reconstructingcomplexnetworkforcharacterizingthetimevaryingcausalityevolutionbehaviorofmultivariatetimeseries
AT gaoxiangyun reconstructingcomplexnetworkforcharacterizingthetimevaryingcausalityevolutionbehaviorofmultivariatetimeseries
AT anhaizhong reconstructingcomplexnetworkforcharacterizingthetimevaryingcausalityevolutionbehaviorofmultivariatetimeseries
AT lihuajiao reconstructingcomplexnetworkforcharacterizingthetimevaryingcausalityevolutionbehaviorofmultivariatetimeseries
AT sunbowen reconstructingcomplexnetworkforcharacterizingthetimevaryingcausalityevolutionbehaviorofmultivariatetimeseries