@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 = "20 set. 2024"
}