Identifying States of Global Oil and Gas Markets Based on Visibility Graph Embedding Algorithms

Oil and natural gas are indispensable energy commodities. Investigating the evolution and transformation of energy market dynamics represented by oil and natural gas holds significant implications for global energy governance, as well as political and economic development. We have employed the visibility graph algorithm to transform the most intuitive and readily available time series data of oil and natural gas prices into complex networks. Then, three network embedding algorithms based on machine learning are utilized to embed the network into multidimensional vector spaces, enabling us to identify market state changes in both energy products through the sequential clustering method. We find that the visibility graph algorithm and network embedding methods effectively preserve internal structural characteristics of data, capture similarities between price change rate nodes across different trading days in the oil and gas markets, while also identifying the memorability of price volatility trends. Beyond simple price trends, critical market fluctuations such as those induced by events like the COVID-19 or Russia–Ukraine conflict can be discerned from similarity matrix partitions of network node representation vectors. Furthermore, the sequential clustering algorithm accurately identifies transition points in the oil and gas markets’ states. Unlike conventional time series analysis methods, this innovative combination of visibility graph and network embedding algorithms allows for a multi-faceted exploration of market state changes at various levels; thereby facilitating deeper insights into underlying logic behind price time series.