Cross and joint ordinal partition transition networks for multivariate time series analysis
Heng Guo, Jia-Yang Zhang, Yong Zou, Shu-Guang Guan
Cross and joint ordinal partition transition networks for multivariate time series analysis
We propose the construction of cross and joint ordinal pattern transition networks from multivariate time series for two coupled systems, where synchronizations are often present. In particular, we focus on phase synchronization, which is a prototypical scenario in dynamical systems. We systematically show that cross and joint ordinal pattern transition networks are sensitive to phase synchronization. Furthermore, we find that some particular missing ordinal patterns play crucial roles in forming the detailed structures in the parameter space, whereas the calculations of permutation entropy measures often do not. We conclude that cross and joint ordinal partition transition network approaches provide complementary insights into the traditional symbolic analysis of synchronization transitions.
nonlinear time series analysis / complex networks / ordinal pattern partition / transition network / phase synchronization
[1] |
R. V. Donner, M. Small, J. F. Donges, N. Marwan, Y. Zou, R. Xiang, and J. Kurths, Recurrence-based time series analysis by means of complex network methods, Int. J. Bifurcat. Chaos 21(04), 1019 (2011)
CrossRef
ADS
Google scholar
|
[2] |
R. V. Donner, Y. Zou, J. F. Donges, N. Marwan, and J. Kurths, Recurrence networks – A novel paradigm for nonlinear time series analysis, New J. Phys. 12(3), 033025 (2010)
CrossRef
ADS
Google scholar
|
[3] |
N. Marwan, J. F. Donges, Y. Zou, R. V. Donner, and J. Kurths, Complex network approach for recurrence analysis of time series, Phys. Lett. A 373(46), 4246 (2009)
CrossRef
ADS
Google scholar
|
[4] |
L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J. C. Nuno, From time series to complex networks: The visibility graph, Proc. Natl. Acad. Sci. USA 105(13), 4972 (2008)
CrossRef
ADS
Google scholar
|
[5] |
J. Zhang and M. Small, Complex network from pseudoperiodic time series: Topology versus dynamics, Phys. Rev. Lett. 96(23), 238701 (2006)
CrossRef
ADS
Google scholar
|
[6] |
Y. Yang and H. Yang, Complex network-based time series analysis, Physica A 387(5–6), 1381 (2008)
CrossRef
ADS
Google scholar
|
[7] |
J. F. Donges, R. V. Donner, M. H. Trauth, N. Marwan, H. J. Schellnhuber, and J. Kurths, Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution, Proc. Natl. Acad. Sci. USA 108(51), 20422 (2011)
CrossRef
ADS
Google scholar
|
[8] |
Y. Zou, R. V. Donner, M. Wickramasinghe, I. Z. Kiss, M. Small, and J. Kurths, Phase coherence and attractor geometry of chaotic electrochemical oscillators, Chaos 22(3), 033130 (2012)
CrossRef
ADS
Google scholar
|
[9] |
Z. K. Gao, W. D. Dang, Y. X. Yang, and Q. Cai, Multiplex multivariate recurrence network from multichannel signals for revealing oil-water spatial flow behavior, Chaos 27(3), 035809 (2017)
CrossRef
ADS
Google scholar
|
[10] |
J. B. Elsner, T. H. Jagger, and E. A. Fogarty, Visibility network of united states hurricanes, Geophys. Res. Lett. 36(16), L16702 (2009)
CrossRef
ADS
Google scholar
|
[11] |
Y. Zou, M. Small, Z. Liu, and J. Kurths, Complex network approach to characterize the statistical features of the sunspot series, New J. Phys. 16(1), 013051 (2014)
CrossRef
ADS
Google scholar
|
[12] |
Y. Zou, R. Donner, N. Marwan, M. Small, and J. Kurths, Long-term changes in the north-south asymmetry of solar activity: A nonlinear dynamics characterization using visibility graphs, Nonlinear Process. Geophys. 21(6), 1113 (2014)
CrossRef
ADS
Google scholar
|
[13] |
R. Zhang, Y. Zou, J. Zhou, Z. K. Gao, and S. Guan, Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes, Commun. Nonlinear Sci. Numer. Simul. 42, 396 (2017)
CrossRef
ADS
Google scholar
|
[14] |
Z. Czechowski, M. Lovallo, and L. Telesca, Multifractal analysis of visibility graph-based Ito-related connectivity time series, Chaos 26(2), 023118 (2016)
CrossRef
ADS
Google scholar
|
[15] |
C. Zhang, Y. Chen, and G. Hu, Network reconstructions with partially available data, Front. Phys. 12(3), 128906 (2017)
CrossRef
ADS
Google scholar
|
[16] |
Z. Q. Jiang, Y. H. Yang, G. J. Wang, and W. X. Zhou, Joint multifractal analysis based on wavelet leaders, Front. Phys. 12(6), 128907 (2017)
CrossRef
ADS
Google scholar
|
[17] |
R. V. Donner, J. Heitzig, J. F. Donges, Y. Zou, N. Marwan, and J. Kurths, The geometry of chaotic dynamics — A complex network perspective, Eur. Phys. J. B 84(4), 653 (2011)
CrossRef
ADS
Google scholar
|
[18] |
M. McCullough, M. Small, T. Stemler, and H. H. C. Iu, Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems, Chaos 25(5), 053101 (2015)
CrossRef
ADS
Google scholar
|
[19] |
C. W. Kulp, J. M. Chobot, H. R. Freitas, and G. D. Sprechini, Using ordinal partition transition networks to analyze ECG data, Chaos 26(7), 073114 (2016)
CrossRef
ADS
Google scholar
|
[20] |
C. W. Kulp, J. M. Chobot, B. J. Niskala, and C. J. Needhammer, Using forbidden ordinal patterns to detect determinism in irregularly sampled time series, Chaos 26(2), 023107 (2016)
CrossRef
ADS
Google scholar
|
[21] |
M. McCullough, K. Sakellariou, T. Stemler, and M. Small, Counting forbidden patterns in irregularly sampled time series (i): The effects of under-sampling, random depletion, and timing jitter, Chaos 26(12), 123103 (2016)
CrossRef
ADS
Google scholar
|
[22] |
K. Sakellariou, M. McCullough, T. Stemler, and M. Small, Counting forbidden patterns in irregularly sampled time series (ii): Reliability in the presence of highly irregular sampling, Chaos 26(12), 123104 (2016)
CrossRef
ADS
Google scholar
|
[23] |
C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett. 88(17), 174102 (2002)
CrossRef
ADS
Google scholar
|
[24] |
U. Parlitz, H. Suetani, and S. Luther, Identification of equivalent dynamics using ordinal pattern distributions, Eur. Phys. J. S.T. 222(2), 553 (2013)
CrossRef
ADS
Google scholar
|
[25] |
J. M. Amigó, K. Keller, and V. A. Unakafova, Ordinal symbolic analysis and its application to biomedical recordings, Phil. Trans. R. Soc. A 373(2034), 20140091 (2014)
CrossRef
ADS
Google scholar
|
[26] |
F. Takens, Detecting strange attractors in turbulence, in: D. Rand and L.-S. Young (Eds.), Dynamical Systems and Turbulence, Warwick 1980, Vol. 898 of Lecture Notes in Mathematics, Springer, New York, 1981, pp 366–381
CrossRef
ADS
Google scholar
|
[27] |
H. Kantz and T. Schreiber, Nonlinear Time Series Analysis, 2nd Ed., Cambridge: Cambridge University Press, 2004
|
[28] |
J. M. Amigó, S. Zambrano, and M. A. F. Sanju’an, True and false forbidden patterns in deterministic and random dynamics, Europhys. Lett. 79(5), 50001 (2007)
CrossRef
ADS
Google scholar
|
[29] |
J. M. Amigó, S. Zambrano, and M. A. F. Sanju’an, Combinatorial detection of determinism in noisy time series, Europhys. Lett. 83(6), 60005 (2008)
CrossRef
ADS
Google scholar
|
[30] |
O. A. Rosso, L. C. Carpi, P. M. Saco, M. G. Ravetti, H. A. Larrondo, and A. Plastino, The Amig’o paradigm of forbidden/missing patterns: A detailed analysis, Eur. Phys. J. B 85(12), 419 (2012)
CrossRef
ADS
Google scholar
|
[31] |
O. A. Rosso, L. C. Carpi, P. M. Saco, M. Gómez Ravetti, A. Plastino, and H. A. Larrondo, Causality and the entropy-complexity plane: Robustness and missing ordinal patterns, Physica A 391(1–2), 42 (2012)
CrossRef
ADS
Google scholar
|
[32] |
C. W. Kulp and L. Zunino, Discriminating chaotic and stochastic dynamics through the permutation spectrum test, Chaos 24(3), 033116 (2014)
CrossRef
ADS
Google scholar
|
[33] |
A. Politi, Quantifying the dynamical complexity of chaotic time series, Phys. Rev. Lett. 118(14), 144101 (2017)
CrossRef
ADS
Google scholar
|
[34] |
J. Zhang, J. Zhou, M. Tang, H. Guo, M. Small, and Y. Zou, Constructing ordinal partition transition networks from multivariate time series, Sci. Rep. 7(1), 7795 (2017)
CrossRef
ADS
Google scholar
|
[35] |
A. Pikovsky, M. Rosenblum, and J. Kurths, Synchronization– A Universal Concept in Nonlinear Sciences, Cambridge University Press, 2001
CrossRef
ADS
Google scholar
|
[36] |
G. V. Osipov, B. Hu, C. Zhou, M. V. Ivanchenko, and J. Kurths, Three types of transitions to phase synchronization in coupled chaotic oscillators, Phys. Rev. Lett. 91(2), 024101 (2003)
CrossRef
ADS
Google scholar
|
[37] |
J. Zhang, Y. Z. Yu, and X. G. Wang, Synchronization of coupled metronomes on two layers, Front. Phys. 12(6), 120508 (2017)
CrossRef
ADS
Google scholar
|
[38] |
H. B. Chen, Y. T. Sun, J. Gao, C. Xu, and Z. G. Zheng, Order parameter analysis of synchronization transitions on star networks, Front. Phys. 12(6), 120504 (2017)
CrossRef
ADS
Google scholar
|
[39] |
X. Huang, J. Gao, Y. T. Sun, Z. G. Zheng, and C. Xu, Effects of frustration on explosive synchronization, Front. Phys. 11(6), 110504 (2016)
CrossRef
ADS
Google scholar
|
[40] |
L. M. Ying, J. Zhou, M. Tang, S. G. Guan, and Y. Zou, Mean-field approximations of fixation time distributions of evolutionary game dynamics on graphs, Front. Phys. 13(1), 130201 (2018)
CrossRef
ADS
Google scholar
|
[41] |
Z. Zheng and G. Hu, Generalized synchronization versus phase synchronization, Phys. Rev. E 62(6), 7882 (2000)
CrossRef
ADS
Google scholar
|
[42] |
M. C. Romano, M. Thiel, J. Kurths, and W. von Bloh, Multivariate recurrence plots, Phys. Lett. A 330(3–4), 214 (2004)
CrossRef
ADS
Google scholar
|
[43] |
M. Pecora and T. L. Carroll, Synchronization of chaotic systems, Chaos 25(9), 097611 (2015)
CrossRef
ADS
Google scholar
|
[44] |
S. Boccaletti, J. Kurths, G. Osipov, D. Valladares, and C. Zhou, The synchronization of chaotic systems,Phys. Rep. 366(1–2), 1 (2002)
CrossRef
ADS
Google scholar
|
[45] |
M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, From phase to lag synchronization in coupled chaotic oscillators, Phys. Rev. Lett. 78(22), 4193 (1997)
CrossRef
ADS
Google scholar
|
[46] |
M. G. Rosenblum and A. S. Pikovsky, Detecting direction of coupling in interacting oscillators,Phys. Rev. E 64(4), 045202 (2001)
CrossRef
ADS
Google scholar
|
[47] |
M. C. Romano, M. Thiel, J. Kurths, and C. Grebogi, Estimation of the direction of the coupling by conditional probabilities of recurrence, Phys. Rev. E 76(3), 036211 (2007)
CrossRef
ADS
Google scholar
|
[48] |
J. Nawrath, M. C. Romano, M. Thiel, I. Z. Kiss, M. Wickramasinghe, J. Timmer, J. Kurths, and B. Schelter, Distinguishing direct from indirect interactions in oscillatory networks with multiple time scales, Phys. Rev. Lett. 104(3), 038701 (2010)
CrossRef
ADS
Google scholar
|
[49] |
Y. Zou, M. C. Romano, M. Thiel, N. Marwan, and J. Kurths, Inferring indirect coupling by means of recurrences,Int. J. Bifurcat. Chaos 21(04), 1099 (2011)
CrossRef
ADS
Google scholar
|
[50] |
A. Groth, Visualization of coupling in time series by order recurrence plots, Phys. Rev. E 72(4), 046220 (2005)
CrossRef
ADS
Google scholar
|
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