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...
Autores principales: | , , , , |
---|---|
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 |