Superfamily phenomena and motifs of networks induced from time series

By Xiaoke Xu, Jie Zhang, Michael Small. Proceedings of the National Academy of Sciences of the United States of America, 2008

457 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9ca26a804dbd7d974f3b0ac9a338f775c886c5a1

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Citations in the corpus

Recurrence networks—a novel paradigm for nonlinear time series analysis (2009, background): “A related conceptual idea has recently been independently suggested by different authors [35, 36, 39, 40, 42], but not yet systematically studied.”
Complex network approach for recurrence analysis of time series (2009, methodology): “ is the matching of two cycles. A complementary approach was suggested by Xu et al. who studied the structural shape of the direct neighbourhood of the phase space trajectory by a motif classiﬁcation [31]. The adjacency matrix of the underlying network corresponds to the recurrence matrix. Other app”
Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence (2009, background): “its four nearest neighbors to form a complex network [11].”
Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application (2017, unknown): “t t d LPHVG t LPHVG t x x ( , ) ( [ ( ), ( )]), ( ) 1, 0, 0, 0 (18) m n m n”
The role of fluctuating modes of autocorrelation in crude oil prices (2014, methodology): “introduced a method to address pseudoperiodic time series and found that the structure of the corresponding network depended on the dynamics of the series [4,5].”
Complex network approaches to nonlinear time series analysis (2019, methodology): “In particular, the motif distribution of adaptive nearest neighbor networks has been empirically shown to allow a discrimination between different types of dynamics in terms of a different motif ranking [50].”
A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, background): “Among them are the visibility graph (VG) [36], the pseudo-periodic time series transform algorithm [37], phase space reconstruction [38], and the coarse graining of phase space [39].”