Duality between time series and networks.

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characteri...

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Autores principales:	Andriana S L O Campanharo, M Irmak Sirer, R Dean Malmgren, Fernando M Ramos, Luís A Nunes Amaral
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d58ba0ad513d425e9f3fbe32f648ae51
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id	oai:doaj.org-article:d58ba0ad513d425e9f3fbe32f648ae51
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spelling	oai:doaj.org-article:d58ba0ad513d425e9f3fbe32f648ae512021-11-18T06:48:09ZDuality between time series and networks.1932-620310.1371/journal.pone.0023378https://doaj.org/article/d58ba0ad513d425e9f3fbe32f648ae512011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21858093/?tool=EBIhttps://doaj.org/toc/1932-6203Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways.Andriana S L O CampanharoM Irmak SirerR Dean MalmgrenFernando M RamosLuís A Nunes AmaralPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e23378 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andriana S L O Campanharo M Irmak Sirer R Dean Malmgren Fernando M Ramos Luís A Nunes Amaral Duality between time series and networks.
description	Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways.
format	article
author	Andriana S L O Campanharo M Irmak Sirer R Dean Malmgren Fernando M Ramos Luís A Nunes Amaral
author_facet	Andriana S L O Campanharo M Irmak Sirer R Dean Malmgren Fernando M Ramos Luís A Nunes Amaral
author_sort	Andriana S L O Campanharo
title	Duality between time series and networks.
title_short	Duality between time series and networks.
title_full	Duality between time series and networks.
title_fullStr	Duality between time series and networks.
title_full_unstemmed	Duality between time series and networks.
title_sort	duality between time series and networks.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/d58ba0ad513d425e9f3fbe32f648ae51
work_keys_str_mv	AT andrianaslocampanharo dualitybetweentimeseriesandnetworks AT mirmaksirer dualitybetweentimeseriesandnetworks AT rdeanmalmgren dualitybetweentimeseriesandnetworks AT fernandomramos dualitybetweentimeseriesandnetworks AT luisanunesamaral dualitybetweentimeseriesandnetworks
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Duality between time series and networks.

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