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Information 2018, 9(8), 202; https://doi.org/10.3390/info9080202

Construction of Complex Network with Multiple Time Series Relevance

1
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2
East Sea Information Center, SOA China, Shanghai 644300, China
*
Author to whom correspondence should be addressed.
Received: 10 June 2018 / Revised: 20 July 2018 / Accepted: 4 August 2018 / Published: 7 August 2018
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Abstract

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method. View Full-Text
Keywords: multivariate time series; fusion similarity; connectivity efficiency; threshold determination; complex network; central node multivariate time series; fusion similarity; connectivity efficiency; threshold determination; complex network; central node
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Huang, Z.; Xu, L.; Wang, L.; Zhang, G.; Liu, Y. Construction of Complex Network with Multiple Time Series Relevance. Information 2018, 9, 202.

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