Assessment of visibility graph similarity as a synchronization measure for chaotic, noisy and stochastic time series

被引：9

作者：

Ahmadi N. ^{[1
]}

Besseling R.M.H. ^{[2
]}

Pechenizkiy M. ^{[1
]}

机构：

[1] Department of Mathematics and Computer Science, Eindhoven University of Technology, POBox 513, Eindhoven

[2] Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven

来源：

Social Network Analysis and Mining | 2018年 / 8卷 / 01期

关键词：

Chaotic and stochastic time series; Visibility graph synchronization measures;

D O I：

10.1007/s13278-018-0526-x

中图分类号：

学科分类号：

摘要：

Finding synchronization between the outputs of a dynamic system, which are represented mostly as time series, helps to characterize the system activities during an occurrence. An important issue in analyzing time series is that they may behave chaotically or stochastically. Therefore, applying a reliable synchronization measure which can capture the dynamic features of the system helps to quantify the interdependencies between time series, correctly. In this paper, we employ similarity measures based on visibility graph (VG) algorithms as an alternative and radically different way to measure the synchronization between time series. We assess the performance of VG-based similarity measures on chaotic, noisy and stochastic time series. In our experiments, we use the Rössler system and the noisy Hénon map as representative instances of chaotic systems, and the Kuramoto model for studying detection of synchronization between stochastic time series. Our study suggests that the similarity measure based on the horizontal VG algorithm should be favored to other measures for detecting synchronization between chaotic and stochastic time series. © 2018, The Author(s).

引用

共 49 条

[1]

Acebron J.A., Bonilla L.L., Vicente C.J., Ritort F., Spigler R., The Kuramoto model: a simple paradigm for synchronization phenomena, Rev Mod Phy, 77, 1, (2005)

[2]

Ahmadi N., Pechenizkiy M., Application of horizontal visibility graph as a robust measure of neurophysiological signals synchrony, Computer-Based Medical Systems (CBMS), pp. 273-278, (2016)

[3]

Ahmadi N., Pechenizkiy M., Detection of alcoholism based on EEG signals and functional brain network features extraction, In: Computer-Based Medical Systems (CBMS), 2017 IEEE 30Th International Symposium On, pp. 179-184, (2017)

[4]

Ahmadlou M., Adeli H., Visibility graph similarity: a new measure of generalized synchronization in coupled dynamic systems, Physica D, 241, 4, pp. 326-332, (2012)

[5]

Ahmadlou M., Adeli H., Adeli A., Improved visibility graph fractality with application for the diagnosis of autism spectrum disorder, Phys A, 391, 20, pp. 4720-4726, (2012)

[6]

Ansari-Asl K., Senhadji L., Bellanger J.J., Wendling F., Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals, Phys Rev E, 74, 3, (2006)

[7]

Barlow J.S., Brazier M.A.B., A note on a correlator for electroencephalographic work, Electroen Clin Neuro, 6, pp. 321-325, (1954)

[8]

Box G.E., Jenkins G.M., Reinsel G.C., Ljung G.M., Time Series Analysis: Forecasting and Control, (2015)

[9]

Brazier M.A.B., Studies of the eeg activity of limbic structures in man, Electroen Clin Neuro, 25, 4, pp. 309-318, (1968)

[10]

Breakspear M., Heitmann S., Daffertshofer A., Generative models of cortical oscillations: neurobiological implications of the Kuramoto model, Front Hum Neurosci, 4, (2010)

← 1 2 3 4 5 →