Can visibility network degree detect abrupt transitions in time series?

How to identify an upcoming transition in a time series from different dynamical systems continues to be an open research issue. In various fields of physical science such as environment, economics, neuroscience and engineering, abrupt transitions can occur unexpectedly and are difficult to manage during the temporal evolution of the dynamic system. In this study we address the problem using the degree centrality measure from the complex network analysis of time series. Specifically, based on the Visibility Degree (VD) of a network which was introduced by Lacasa et al (2008), we propose a novel modification of this attribute called ‘’Backward Visibility Degree’’ (BVD) as a measure of detecting sudden shift. We compare the performance of the two attributes in a number of simulated data, and also both to experimental data as well as field data measurements to numerically demonstrate VD and BVD features. The experimental time series concern the study of various turbulent jet flows while the field measurements originated from Seawatch buoys at the Mediterranean Sea. Our results indicate that both VD and BVD identify points of transitions during the evolution of time series but BVD attribute provides a more accurate measure of the VD. The findings also indicate that in same case VD detect fault peaks as point transition while our proposed approach is able to detect abrupt transition in a better way and allows us to capture important aspects of the temporal structure of the system.

Authors: 
Avraam Charakopoulos and Theodoros Karakasidis
Room: 
8
Date: 
Monday, September 24, 2018 - 17:15 to 17:30

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