Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph

2016 ◽  
Vol 450 ◽  
pp. 635-648 ◽  
Author(s):  
Jinjun Tang ◽  
Fang Liu ◽  
Weibin Zhang ◽  
Shen Zhang ◽  
Yinhai Wang
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Complex Networks ◽  
Traffic Flow ◽  
Dynamic Property ◽  
Flow Time ◽  
Phase Space Reconstruction ◽  
Visibility Graph

scholarly journals Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

CONVERTER ◽  
2021 ◽  
pp. 146-161
Author(s):  
Xue Xing, Yaqi Zhai, Zhongtai Jiang, Xiaoyu LI
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Network Analysis ◽  
Traffic Flow ◽  
Complex Network ◽  
Degree Distribution ◽  
Data Flow ◽  
Flow Time ◽  
Phase Space Reconstruction ◽  
Traffic Data

Traffic flow time series is vital for mining the traditional statistical characteristics by using the theory of statistics and machine learning when its identity is a special time series. The network analysis of the traffic flow time series, who uses the complex network of time series analysis method, is designed to inquire into the special law of traffic flow time series which uses its visualization characteristics. Through the network analysis of traffic data flow, the connotation of traffic data flow can be revealed, and the relationship between all data and some data can be further studied. Therefore, it is constructed by combination with the phase space reconstruction theory. The phase space trajectory may be squeezed and the structure of attractor may change. We need to use C-C method to estimate the time delay according to the characteristics of integral parameters, and use G-P algorithm to estimate the embedding dimension to avoid it. This study can effectively reveal the motion law of the system. After constructing the complex network of traffic flow time series with various traffic parameters, the degree distribution, clustering coefficient and modularization of the representative critical threshold corresponding network are statistically analysed. The analysis results show that the new networked structure of traffic flow time series proposed in this study has strong advantages, and its core is phase space reconstruction, which can well reflect the information space of traffic dynamic fluctuation. The time series networking method based on phase space reconstruction has become a new approach to inquire into the characteristics of traffic flow time series. The degree distribution of the actual multi-traffic parameter time series construction network satisfies the characteristics of a Gaussian distribution. Their average clustering coefficients have attenuation characteristics, and their modularization degree is obvious.

scholarly journals Analysis of multi-state traffic flow time series properties using visibility graph

2017 ◽  
Vol 66 (23) ◽  
pp. 230501
Author(s):  
Xing Xue ◽  
Yu De-Xin ◽  
Tian Xiu-Juan ◽  
Wang Shi-Guang
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Flow Time ◽  
Visibility Graph

Temporal Dynamics of Streamflow Using Complex Networks

2020 ◽  
Author(s):  
Bellie Sivakumar
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Complex Networks ◽  
Chaos Theory ◽  
Temporal Dynamics ◽  
The United States ◽  
Phase Space Reconstruction ◽  
Clustering Coefficient ◽  
Network Construction ◽  
Original Time

<p>Modeling the dynamics of streamflow continues to be highly challenging. The present study proposes a new approach to study the temporal dynamics of streamflow. The approach couples the concepts of complex networks and chaos theory. Applications of the concepts of complex networks for studying streamflow dynamics have been gaining momentum in recent years. A key step in such applications is the construction of the network – a network is a set of points (nodes) connected by lines (links). The present study uses the concept of phase-space reconstruction, an essential first step in chaos theory-based methods, for network construction to study the temporal dynamics of streamflow. The phase-space reconstruction involves representation of a single-variable time series in a multi-dimensional phase space using delay embedding. The reconstructed phase space is treated as a network, with the reconstructed vectors (rather than the original time series) serving as the nodes and the connections between them serving as the links. With this network construction, the clustering coefficient of the individual nodes and the entire network is calculated to assess the node and network strengths. The approach is employed to a large number of streamflow time series observed in the United States. The results indicate the usefulness and effectiveness of the phase-space reconstruction-based approach for network construction. The implications of the outcomes for identification of the appropriate type and complexity of model as well as for classification of catchments are discussed.</p>

Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach

2018 ◽  
Vol 17 (01) ◽  
pp. 1850006 ◽  
Author(s):  
Yongping Zhang ◽  
Pengjian Shang ◽  
Hui Xiong ◽  
Jianan Xia
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Financial Time Series ◽  
Real Data ◽  
Accurate Estimate ◽  
Phase Space Reconstruction ◽  
Visibility Graph ◽  
Reconstruction Technique ◽  
Nonequilibrium Dynamic ◽  
Time Irreversibility

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.

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