WTI Futures Price Forecasting Based on Multi-Graph Fusion Spatiotemporal Attention Network

Junke Huang () and Hui Qu ()
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Junke Huang: Nanjing University, School of Management and Engineering
Hui Qu: Nanjing University, School of Management and Engineering

A chapter in Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026), 2026, pp 375-384 from Springer

Abstract: Abstract This study develops a Multi-Graph Fusion Spatiotemporal Attention Network (MG-STAN) to better capture the evolving interactions between crude oil markets and related financial systems. The proposed framework incorporates temporal embeddings, spatial attention modules, and a multi-graph structure to reflect diverse inter-market relationships. Using a dataset covering 2011–2024 that includes commodity futures, supply-demand factors, and financial indicators, our proposed MG-STAN models consistently and significantly outperform conventional deep learning models. Notably, a three-graph fusion strategy—combining correlation, K-nearest neighbor and dynamic time warping graphs—achieves the best results, suggesting that selectively integrating heterogeneous graphs can enhance forecasting accuracy. The findings underscore the value of multi-graph designs and attention mechanisms in modeling market complexity, and offer new perspectives for price forecasting and energy finance research.

Keywords: Crude oil futures; Price forecast; Spatiotemporal graph neural network; Multi-graph fusion (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-672-2_35

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DOI: 10.2991/978-94-6239-672-2_35

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