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This paper focuses on a new method of converting multidimensional time series into complex networks. Compared with the previous studies, this method puts different variables into the same high-dimensional system, not only can study the properties of individual variables but also can investigate the correlation between different variables and its dynamic evolution process. Taking the monthly crude oil price data of 23 regions in the United States as a sample, a complex network diagram of crude oil market prices was constructed in this paper. Through the study of network node degree, clustering coefficient and betweenness, this paper analyzes the linkage between crude oil markets. It is shown that the Illinois region is very important in the interaction of oil prices in the 23 regions of the USA. North Slope region in the network diagram is the middle junction of different node groups. This research can provide theoretical support for policy-making in the energy market.
Zhen et al. (Wed,) studied this question.
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