%0 Journal Article
%4 dpi.inpe.br/plutao/2011/09.22.16.56
%2 dpi.inpe.br/plutao/2011/09.22.16.56.40
%@doi 10.1371/journal.pone.0023378
%@issn 1932-6203
%F lattes: 9205282923078496 4 CampanharoRam:2011:DuBeTi
%T Duality between time series and network analysis
%D 2011
%8 Aug.
%A Campanharo, Andriana Susana Lopes de Oliveira,
%A Sirer, M. Irmak,
%A Malmgren, R. Dean,
%A Ramos, Fernando Manuel,
%A Amaral, L. A. N,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL USA
%@affiliation Northwestern Univ, Dept Chem & Biol Engn, Evanston, IL USA
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress fernando@lac.inpe.br
%B PLoS One
%V 6
%N 8
%P 1-12
%K Arabidopsis, article, artificial neural network, controlled study, correlation analysis, heart rate, information processing, intermethod comparison, Internet, metabolism, statistical analysis, time series analysis.
%X 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.
%@language en
%3 Campanharo-LAC-journal.pone.0023378[1].pdf