An improvement of the measurement of time series irreversibility with visibility graph approach

2018 ◽  
Vol 502 ◽  
pp. 370-378 ◽  
Author(s):  
Zhenyu Wu ◽  
Pengjian Shang ◽  
Hui Xiong
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Time Series Irreversibility

Jensen–Shannon Divergence Based on Horizontal Visibility Graph for Complex Time Series

2020 ◽  
pp. 2150013
Author(s):  
Yi Yin ◽  
Wenjing Wang ◽  
Qiang Li ◽  
Zunsong Ren ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Visibility Graph ◽  
Horizontal Visibility ◽  
Stock Indices ◽  
Leibler Divergence ◽  
Time Irreversibility ◽  
Jensen Shannon Divergence ◽  
Time Series Irreversibility ◽  
Horizontal Visibility Graph

In this paper, we propose Jensen–Shannon divergence (JSD) based on horizontal visibility graph (HVG) to measure the time series irreversibility for both stationary and non-stationary series efficiently. Numerical simulations are first conducted to show the validity of the proposed method and then empirical applications to the financial time series and traffic time series are investigated. It can be found that JSD shows better robustness than Kullback–Leibler divergence (KLD) on quantifying time series irreversibility and correctly distinguishes the different type of simulated series. For the empirical analysis, JSD based on HVG is able to detect the significant time irreversibility of stock indices and reveal the relationship between different stock indices. JSD results show the time irreversibility of speed time series for different detectors and present better accuracy and robustness than KLD. The hierarchical clustering based on their behavior of time irreversibility obtained by JSD classifies the detectors into four groups.

scholarly journals Time series irreversibility: a visibility graph approach

2012 ◽  
Vol 85 (6) ◽  
Author(s):  
L. Lacasa ◽  
A. Nuñez ◽  
É. Roldán ◽  
J. M. R. Parrondo ◽  
B. Luque
Keyword(s):  
Time Series ◽  
Visibility Graph ◽  
Time Series Irreversibility

Multifractal analysis of visibility graph-based Ito-related connectivity time series

2016 ◽  
Vol 26 (2) ◽  
pp. 023118 ◽  
Author(s):  
Zbigniew Czechowski ◽  
Michele Lovallo ◽  
Luciano Telesca
Keyword(s):  
Time Series ◽  
Multifractal Analysis ◽  
Visibility Graph

scholarly journals Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Sumanta Kundu ◽  
Anca Opris ◽  
Yohei Yukutake ◽  
Takahiro Hatano
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Recent Observation ◽  
Visibility Graph ◽  
Graph Analysis ◽  
Common Belief ◽  
Complex Network Theory ◽  
Event Time ◽  
Visibility Graph Analysis ◽  
Present Understanding

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

scholarly journals Visibility Graph Power Geometric Aggregation Operator and Its Application in Water, Energy and Food Efficiency Evaluation

2020 ◽  
Vol 17 (11) ◽  
pp. 3891
Author(s):  
Lihua Liu ◽  
Jing Huang ◽  
Huimin Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Coupling Efficiency ◽  
Aggregation Operator ◽  
Visibility Graph ◽  
Series Data ◽  
Efficiency Evaluation ◽  
Geometric Operator ◽  
The Relationship

In the real decision-making process, there are so many time series values that need to be aggregated. In this paper, a visibility graph power geometric (VGPG) aggregation operator is developed, which is based on the complex network and power geometric operator. Time series data are converted into a visibility graph. A visibility matrix is developed to denote the links among different time series values. A new support function based on the distance of two values are proposed to measure the support degree of each other when the two time series values have visibility. The VGPG operator considers not only the relationship but also the similarity degree between two values. Meanwhile, some properties of the VGPG operator are also investigated. Finally, a case study for water, energy, and food coupling efficiency evaluation in China is illustrated to show the effectiveness of the proposed operator. Comparative analysis with the existing research is also offered to show the advantages of the proposed method.

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