Volume 116, Number 5, December 2016
|Number of page(s)||7|
|Published online||20 January 2017|
Complex network analysis of time series
1 School of Electrical and Information Engineering, Tianjin University - Tianjin 300072, China
2 School of Mathematics and Statistics, University of Western Australia - Crawley, WA, 6009, Australia
3 Potsdam Institute for Climate Impact Research - Telegraphenberg A 31, 14473 Potsdam, Germany
4 Department of Physics, Humboldt University Berlin - 12489 Berlin, Germany
5 Institute for Complex Systems and Mathematical Biology, University of Aberdeen - Aberdeen AB24 3UE, UK
Received: 16 December 2016
Accepted: 22 December 2016
Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.
PACS: 05.45.Tp – Time series analysis / 64.60.aq – Networks / 89.75.-k – Complex systems
© EPLA, 2016
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.