scholarly journals Combining time-series evidence: A complex network model based on a visibility graph and belief entropy

2022 ◽  
Author(s):  
Xingjian Song ◽  
Fuyuan Xiao
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Network Model ◽  
Visibility Graph ◽  
Model Based ◽  
Belief Entropy

scholarly journals SOMA Network Model Based on Native Visibility Graph

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 49-56
Author(s):  
Lukas Tomaszek ◽  
Ivan Zelinka
Keyword(s):  
Time Series ◽  
Network Model ◽  
Visibility Graph ◽  
New Model ◽  
Model Based ◽  
Network Properties ◽  
Basic Network ◽  
Self Organizing

In this article, we want to propose a new model of the network for analyzing the evolution algorithms.We focus on the graph called native visibility graph. We show how we can get a time series from the run ofthe self-organizing migrating algorithm and how we can convert these series into a network. At the end of thearticle, we focus on some basic network properties and we propose how can we use these properties for laterinvestigation. All experiments run on well-known CEC 2016 benchmarks.

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 Examination of Chaotic Structures in Semiconductor or Alloy Voltage Time-Series: A Complex Network Approach for the Case of TlInTe2

Physics ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 624-639
Author(s):  
Dimitrios Tsiotas ◽  
Lykourgos Magafas ◽  
Michael P. Hanias
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Graph Algorithm ◽  
Differential Resistance ◽  
Visibility Graph ◽  
Characteristic Region ◽  
Scale Free ◽  
Complex Network Analysis ◽  
Voltage Oscillation

This paper proposes a method for examining chaotic structures in semiconductor or alloy voltage oscillation time-series, and focuses on the case of the TlInTe2 semiconductor. The available voltage time-series are characterized by instabilities in negative differential resistance in the current–voltage characteristic region, and are primarily chaotic in nature. The analysis uses a complex network analysis of the time-series and applies the visibility graph algorithm to transform the available time-series into a graph so that the topological properties of the graph can be studied instead of the source time-series. The results reveal a hybrid lattice-like configuration and a major hierarchical structure corresponding to scale-free characteristics in the topology of the visibility graph, which is in accordance with the default hybrid chaotic and semi-periodic structure of the time-series. A novel conceptualization of community detection based on modularity optimization is applied to the available time-series and reveals two major communities that are able to be related to the pair-wise attractor of the voltage oscillations’ phase portrait of the TlInTe2 time-series. Additionally, the network analysis reveals which network measures are more able to preserve the chaotic properties of the source time-series. This analysis reveals metric information that is able to supplement the qualitative phase-space information. Overall, this paper proposes a complex network analysis of the time-series as a method for dealing with the complexity of semiconductor and alloy physics.

scholarly journals Limited penetrable visibility graph for establishing complex network from time series

2012 ◽  
Vol 61 (3) ◽  
pp. 030506
Author(s):  
Zhou Ting-Ting ◽  
Jin Ning-De ◽  
Gao Zhong-Ke ◽  
Luo Yue-Bin
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Visibility Graph

scholarly journals Financial complex network model based on textual mutual information

2018 ◽  
Vol 67 (14) ◽  
pp. 148901
Author(s):  
Sun Yan-Feng ◽  
Wang Chao-Yong
Keyword(s):  
Mutual Information ◽  
Complex Network ◽  
Network Model ◽  
Model Based

scholarly journals Wavelet Network Model Based on Multiple Criteria Decision Making for Forecasting Temperature Time Series

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Jian Zhang ◽  
Xiao-hua Yang ◽  
Xiao-juan Chen
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Relative Error ◽  
Network Model ◽  
Multiple Criteria Decision Making ◽  
Temperature Time Series ◽  
Multiple Criteria ◽  
Wavelet Network ◽  
Model Based ◽  
Temperature Time

Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regression (NNBR), the probability of relative error smaller than 10% increases from 65.79% to 84.21% (forecast periodT=1) and from 51.35% to 91.89%(T=2)by WNMCDM model. Similarly, the probability of relative error smaller than 20% increases from 84.21% to 97.37%(T=1)and from 81.08% to 91.89%(T=2)by WNMCDM model. Therefore, WNMCDM model is superior to NNBR model in forecasting temperature time series.

Decluster: a complex network model-based data center network topology

2014 ◽  
Vol 70 (3) ◽  
pp. 1365-1382
Author(s):  
Xu Zhang ◽  
Hai Wang ◽  
Qingyuan Gong ◽  
Xin Wang
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Network Topology ◽  
Data Center ◽  
Data Center Network ◽  
Model Based
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