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		<identifier>8JMKD3MGP8W/37QJLBP</identifier>
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		<citationkey>CampanharoRamo:2008:NoFeAn</citationkey>
		<title>Redes complexas: uma nova ferramenta na análise de séries temporais</title>
		<format>On-line.</format>
		<year>2008</year>
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		<size>355 KiB</size>
		<author>Campanharo, Andriana S. L. O.,</author>
		<author>Ramos, Fernando Manuel,</author>
		<resumeid></resumeid>
		<resumeid>8JMKD3MGP5W/3C9JH4A</resumeid>
		<group>SPG-INPE-MCT-BR</group>
		<group>LAC-CTE-INPE-MCT-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<editor>Vijaykumar, Nandamudi Lankalapalli,</editor>
		<editor>Luz, Eduardo Fávero Pacheco da,</editor>
		<editor>Furtado, Helaine Cristina Morais,</editor>
		<editor>Yanasse, Horacio Hideki,</editor>
		<editor>Domingues, Margarete Oliveira,</editor>
		<editor>Rocha, Renata Sampaio da,</editor>
		<editor>Follmann, Rosângela,</editor>
		<editor>Cintra, Rosângela Saher Correa,</editor>
		<editor>Veronese, Thalita Biazzuz,</editor>
		<e-mailaddress>capsecretaria@gmail.com</e-mailaddress>
		<conferencename>Workshop dos Cursos de Computação Aplicada do INPE, 8 (WORCAP).</conferencename>
		<conferencelocation>São José dos Campos</conferencelocation>
		<date>15 e 16 out. 2008</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<booktitle>Anais</booktitle>
		<secondarytype>PRE CN</secondarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>In this work we construct complex networks from different types of time series (e.g. random, periodic and chaotic time series), where each point is represented by a node in the associated network and the connection between nodes is determined by the correlation between the points of the time series. We investigate the statistical properties of these networks, such as the clustering coefficient and the average path length and found that time series with different dynamics exhibit distinct topological structures. These results show the complex network theory can be a powerful tool in the time series analysis.</abstract>
		<area>COMP</area>
		<subject>Modelagem Computacional</subject>
		<session>Modelagem Computacional</session>
		<type>Modelagem Computacional</type>
		<language>pt</language>
		<targetfile>Andriana_Campanharo.pdf</targetfile>
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		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m18@80/2010/07.06.12.44</url>
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