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

Author

Listed:
  • Junke Huang

    (Nanjing University, School of Management and Engineering)

  • Hui Qu

    (Nanjing University, School of Management and Engineering)

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.

Suggested Citation

  • Junke Huang & Hui Qu, 2026. "WTI Futures Price Forecasting Based on Multi-Graph Fusion Spatiotemporal Attention Network," Advances in Economics, Business and Management Research,, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-672-2_35
    DOI: 10.2991/978-94-6239-672-2_35
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