Duality between Time Series and Networks

• Published in: PloS one Vol. 6; no. 8; p. e23378
• Main Authors: Campanharo, Andriana S. L. O., Sirer, M. Irmak, Malmgren, R. Dean, Ramos, Fernando M., Amaral, Luís A. Nunes
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.08.2011; Public Library of Science (PLoS)
• Subjects: Algorithms; Arabidopsis - physiology; Bioengineering; Biology; Chaos theory; Coding; Computer Simulation; Dynamical systems; Engineering; Heart Rate - physiology; Humans; International conferences; Kinetics; Mathematics; Models, Biological; Network analysis; Networks; Neural networks; Physics; Probability; Researchers; Signal Transduction - physiology; Statistical analysis; Time Factors; Time series; Brazil; United States > US; Illinois
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0023378

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.