<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Journal Article">
		<site>plutao.sid.inpe.br 800</site>
		<holdercode>{isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}</holdercode>
		<identifier>J8LNKAN8RW/3AFL2PP</identifier>
		<repository>dpi.inpe.br/plutao/2011/09.22.16.56</repository>
		<lastupdate>2012:06.25.13.04.45 dpi.inpe.br/plutao@80/2008/08.19.15.01 administrator</lastupdate>
		<metadatarepository>dpi.inpe.br/plutao/2011/09.22.16.56.40</metadatarepository>
		<metadatalastupdate>2022:04.09.17.55.12 sid.inpe.br/bibdigital@80/2006/04.07.15.50 administrator</metadatalastupdate>
		<doi>10.1371/journal.pone.0023378</doi>
		<issn>1932-6203</issn>
		<label>lattes: 9205282923078496 4 CampanharoRam:2011:DuBeTi</label>
		<citationkey>CampanharoSirMalRamAma:2011:DuTiSe</citationkey>
		<title>Duality between time series and network analysis</title>
		<project>Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq/Brazil); National Science Foundation (NSF)[SBE 0624318]</project>
		<year>2011</year>
		<month>Aug.</month>
		<secondarytype>PRE PI</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1355 KiB</size>
		<author>Campanharo, Andriana Susana Lopes de Oliveira,</author>
		<author>Sirer, M. Irmak,</author>
		<author>Malmgren, R. Dean,</author>
		<author>Ramos, Fernando Manuel,</author>
		<author>Amaral, L. A. N,</author>
		<resumeid></resumeid>
		<resumeid></resumeid>
		<resumeid></resumeid>
		<resumeid>8JMKD3MGP5W/3C9JH4A</resumeid>
		<group>LAC-CTE-INPE-MCT-BR</group>
		<group></group>
		<group></group>
		<group>DIR-DIR-INPE-MCT-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL USA</affiliation>
		<affiliation>Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL USA</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States</affiliation>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress></electronicmailaddress>
		<electronicmailaddress>fernando@lac.inpe.br</electronicmailaddress>
		<e-mailaddress>fernando@lac.inpe.br</e-mailaddress>
		<journal>PLoS One</journal>
		<volume>6</volume>
		<number>8</number>
		<pages>1-12</pages>
		<secondarymark>C_ASTRONOMIA_/_FÍSICA A1_BIOTECNOLOGIA C_CIÊNCIAS_AGRÁRIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A2_CIÊNCIAS_BIOLÓGICAS_III A1_ECOLOGIA_E_MEIO_AMBIENTE B5_EDUCAÇÃO B2_INTERDISCIPLINAR B3_MEDICINA_I B3_MEDICINA_II C_MEDICINA_III B3_MEDICINA_VETERINÁRIA B2_ODONTOLOGIA A2_PSICOLOGIA C_QUÍMICA B2_SAÚDE_COLETIVA</secondarymark>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<versiontype>publisher</versiontype>
		<keywords>Arabidopsis, article, artificial neural network, controlled study, correlation analysis, heart rate, information processing, intermethod comparison, Internet, metabolism, statistical analysis, time series analysis.</keywords>
		<abstract>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.</abstract>
		<area>GEST</area>
		<language>en</language>
		<targetfile>Campanharo-LAC-journal.pone.0023378[1].pdf</targetfile>
		<usergroup>administrator</usergroup>
		<usergroup>lattes</usergroup>
		<usergroup>secretaria.cpa@dir.inpe.br</usergroup>
		<readergroup>administrator</readergroup>
		<readergroup>secretaria.cpa@dir.inpe.br</readergroup>
		<visibility>shown</visibility>
		<archivingpolicy>allowpublisher allowfinaldraft</archivingpolicy>
		<readpermission>allow from all</readpermission>
		<documentstage>not transferred</documentstage>
		<nexthigherunit>8JMKD3MGPCW/3ESGTTP</nexthigherunit>
		<nexthigherunit>8JMKD3MGPCW/449PGL8</nexthigherunit>
		<citingitemlist>sid.inpe.br/bibdigital/2013/09.22.23.14 2</citingitemlist>
		<dissemination>WEBSCI; PORTALCAPES.</dissemination>
		<hostcollection>dpi.inpe.br/plutao@80/2008/08.19.15.01</hostcollection>
		<username>marciana</username>
		<lasthostcollection>dpi.inpe.br/plutao@80/2008/08.19.15.01</lasthostcollection>
		<url>http://plutao.sid.inpe.br/rep-/dpi.inpe.br/plutao/2011/09.22.16.56</url>
	</metadata>
</metadatalist>