A novel probabilistic connectivity network link prediction model for natural gas price based on an improved K-shell algorithm

• Published in: Physica A Vol. 671; p. 130672
• Main Authors: Liu, Jinpei, Qiu, Biyue, Du, Pengcheng, Zhao, Xiaoman, Zhu, Jiaming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2025
• Subjects: Influential nodes; K-shell algorithm; Link prediction; Natural gas price forecasting; Probabilistic connectivity network; Link prediction; Natural gas price forecasting; Influential nodes; K-shell algorithm; Probabilistic connectivity network
• ISSN: 0378-4371
• DOI: 10.1016/j.physa.2025.130672

Accurate natural gas price forecasts play a critical role in mitigating market volatility, guiding commodity trading, and enhancing regulatory decision-making. However, the existing natural gas price prediction studies predominantly rely on data with a limited lag period as the forecast input, ignoring historical information, and considering less the complex information embedded in historical data. Therefore, we propose a novel probabilistic connectivity network (PCnet) link prediction model for natural gas prices, comprising four key components: data decomposition, node influence measurement, probabilistic network construction, and combination forecasting. First, the Aquila Optimizer is employed to optimize the parameters of Variational Mode Decomposition to decompose the original data and extract its intrinsic mode functions, providing a better feature. Second, a K-shell method positioned with neighbor and Shell-diversity (KPNS) is proposed to comprehensively and effectively measure the contained information between nodes and extract structural features basis for subsequent modeling. Third, a probabilistic connectivity network (PCnet) is constructed based on the KPNS, effectively preserving the uncertainty information of the nodes. Finally, a Local Random Walk with Restart is used to locate similar nodes in the network, and the prediction results are obtained with a combination of Support Vector Regression, CatBoost, and Extra Trees according to the link prediction idea. The empirical results validate the outstanding predictive accuracy of the model, highlighting its potential applicability in natural gas price forecasting.