Visibility graph analysis for re-sampled time series from auto-regressive stochastic processes

DOI10.1016/j.cnsns.2016.04.031zbMath1473.62323OpenAlexW2418612082MaRDI QIDQ2004821

Yong Zou, Shuguang Guan, Rong Zhang, Zhong-Ke Gao, Jie Zhou

Publication date: 7 October 2020

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2016.04.031

zbMATH Keywords

AR processesvisibility graphnonlinear time series analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (8)

Time irreversibility and intrinsics revealing of series with complex network approach ⋮ Nonuniversality of the horizontal visibility graph in inferring series periodicity ⋮ Multiscale horizontal visibility entropy: measuring the temporal complexity of financial time series ⋮ Mapping time series into signed networks via horizontal visibility graph ⋮ Could network analysis of horizontal visibility graphs be faithfully used to infer long-term memory properties in real-world time series? ⋮ Multi-affine visible height correlation analysis for revealing rich structures of fractal time series ⋮ Generalized Shannon-Fisher index: an effective method to quantify the instability of multivariate time series ⋮ Effective instability quantification for multivariate complex time series using reverse Shannon-Fisher index

Uses Software

• longmemo
• plfit

Cites Work

• Unnamed Item
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• Complex network approach for recurrence analysis of time series
• RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS
• On the degree distribution of horizontal visibility graphs associated with Markov processes and dynamical systems: diagrammatic and variational approaches
• Phase coherence and attractor geometry of chaotic electrochemical oscillators
• Recurrence networks—a novel paradigm for nonlinear time series analysis
• Superfamily phenomena and motifs of networks induced from time series
• From time series to complex networks: The visibility graph
• Power-Law Distributions in Empirical Data
• Nonlinear Time Series Analysis
• Collective dynamics of ‘small-world’ networks
• Fractional Brownian Motions, Fractional Noises and Applications
• The geometry of chaotic dynamics --- a complex network perspective

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