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@PhDThesis{Campanharo:2011:DuAAn,
               author = "Campanharo, Andriana Susana Lopes de Oliveira",
                title = "Dualidade entre a an{\'a}lise de s{\'e}ries temporais e de redes 
                         complexas",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2011",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2011-03-29",
             keywords = "sistemas complexos, redes complexas, an{\'a}lise de s{\'e}ries 
                         temporais, complex systems, complex networks, time series 
                         analysis.",
             abstract = "Recentemente, diversos mapeamentos de s{\'e}ries temporais em 
                         redes complexas foram propostos com o intuito de utilizar 
                         t{\'e}cnicas de caracteriza{\c{c}}{\~a}o de redes complexas 
                         para a an{\'a}lise de s{\'e}ries temporais. Embora esses 
                         mapeamentos demonstrem que s{\'e}ries temporais com 
                         din{\^a}micas distintas resultam em redes complexas com 
                         diferentes topologias, ainda n{\~a}o se sabe dizer como tais 
                         propriedades topol{\'o}gicas est{\~a}o relacionadas com as 
                         s{\'e}ries originais. Nesta tese, prop{\~o}e-se um mapeamento de 
                         uma s{\'e}rie temporal em uma rede complexa com uma 
                         opera{\c{c}}{\~a}o inversa aproximada, tornando 
                         poss{\'{\i}}vel a utiliza{\c{c}}{\~a}o de 
                         estat{\'{\i}}sticas em redes complexas para a 
                         caracteriza{\c{c}}{\~a}o de s{\'e}ries temporais e vice-versa. 
                         Como prova deste conceito, um conjunto de s{\'e}ries temporais 
                         {\'e} gerado, variando de peri{\'o}dica {\`a} aleat{\'o}ria e 
                         demonstra-se que o mapeamento proposto ret{\'e}m a maior parte da 
                         informa{\c{c}}{\~a}o embutida na s{\'e}rie temporal (ou rede) 
                         ap{\'o}s a aplica{\c{c}}{\~a}o do mesmo e de seu inverso. Os 
                         resultados obtidos sugerem que a an{\'a}lise de redes complexas 
                         pode ser utilizada na distin{\c{c}}{\~a}o de regimes 
                         din{\^a}micos em s{\'e}ries temporais e, talvez o mais 
                         importante, a an{\'a}lise de s{\'e}ries temporais pode fornecer 
                         um conjunto de ferramentas {\'u}teis para a 
                         caracteriza{\c{c}}{\~a}o de redes complexas de uma forma 
                         totalmente original. ABSTRACT: Recently, several mappings from a 
                         time series to a network have been proposed with the intent of 
                         using network metrics to characterize time series. Although these 
                         mappings 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, 
                         it was proposed an intuitive mapping 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, 
                         an ensemble of time series ranging from periodic to random were 
                         generated and confirm that the proposed mapping retains much of 
                         the information encoded in the original time series and networks 
                         after repeated application of the mapping and its inverse. The 
                         results obtained in this thesis 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.",
            committee = "Silva, Jos{\'e} Demisio Sim{\~o}es da (presidente) and Ramos, 
                         Fernando Manuel (orientador) and Carvalho, Solon Ven{\^a}ncio de 
                         and Velho, Haroldo Fraga de Campos and Castro, Joaquim Jos{\'e} 
                         Barroso de and Costa, Luciano da Fontoura and Viana, Ricardo 
                         Luiz",
           copyholder = "SID/SCD",
         englishtitle = "Duality between time series and network analysis",
             language = "pt",
                pages = "100",
                  ibi = "8JMKD3MGP7W/39AQPCB",
                  url = "http://urlib.net/rep/8JMKD3MGP7W/39AQPCB",
           targetfile = "publicacao.pdf",
        urlaccessdate = "13 abr. 2021"
}


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