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

DOI

10.2991/978-94-6239-672-2_35How to use a DOI?

Keywords

Crude oil futures; Price forecast; Spatiotemporal graph neural network; Multi-graph fusion

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junke Huang
AU  - Hui Qu
PY  - 2026
DA  - 2026/05/12
TI  - WTI Futures Price Forecasting Based on Multi-Graph Fusion Spatiotemporal Attention Network
BT  - Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026)
PB  - Atlantis Press
SP  - 375
EP  - 384
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-672-2_35
DO  - 10.2991/978-94-6239-672-2_35
ID  - Huang2026
ER  -