Multivariate financial time series in the light of complex network analysis

2018 ◽  
Vol 503 ◽  
pp. 1241-1255 ◽  
Author(s):  
Sufang An ◽  
Xiangyun Gao ◽  
Meihui Jiang ◽  
Xiaoqi Sun
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Financial Time Series ◽  
Complex Network Analysis ◽  
Financial Time ◽  
Multivariate Financial Time Series

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.

Monitoring the mean of multivariate financial time series

2013 ◽  
Vol 30 (3) ◽  
pp. 328-340 ◽  
Author(s):  
Robert Garthoff ◽  
Vasyl Golosnoy ◽  
Wolfgang Schmid
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Financial Time ◽  
The Mean ◽  
Multivariate Financial Time Series

scholarly journals Multivariate Financial Time-Series Prediction With Certified Robustness

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109133-109143 ◽  
Author(s):  
Hui Li ◽  
Yunpeng Cui ◽  
Shuo Wang ◽  
Juan Liu ◽  
Jinyuan Qin ◽  
...  
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Multivariate Financial Time Series
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