@Article{CampanharoSirMalRamAma:2011:DuTiSe,
               author = "Campanharo, Andriana Susana Lopes de Oliveira and Sirer, M. Irmak 
                         and Malmgren, R. Dean and Ramos, Fernando Manuel and Amaral, L. A. 
                         N",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Northwestern Univ, Dept Chem \& Biol Engn, Evanston, IL USA and 
                         Northwestern Univ, Dept Chem \& Biol Engn, Evanston, IL USA and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and Department 
                         of Chemical and Biological Engineering, Northwestern University, 
                         Evanston, IL, United States",
                title = "Duality between time series and network analysis",
              journal = "PLoS One",
                 year = "2011",
               volume = "6",
               number = "8",
                pages = "1--12",
                month = "Aug.",
             keywords = "Arabidopsis, article, artificial neural network, controlled study, 
                         correlation analysis, heart rate, information processing, 
                         intermethod comparison, Internet, metabolism, statistical 
                         analysis, time series analysis.",
             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.",
                  doi = "10.1371/journal.pone.0023378",
                  url = "http://dx.doi.org/10.1371/journal.pone.0023378",
                 issn = "1932-6203",
                label = "lattes: 9205282923078496 4 CampanharoRam:2011:DuBeTi",
             language = "en",
           targetfile = "Campanharo-LAC-journal.pone.0023378[1].pdf",
        urlaccessdate = "19 abr. 2024"
}