Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Abstract

In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes. It aims to be the first to (i) explore the potential of spatial-temporal graph neural networks family for price forecasting of these markets, (ii) examine the role of attention mechanism in improving forecasting accuracy, and (iii) integrate various sources of predictors for better performance. Specifically, we present three distinct models: Multivariate Time Series Graph Neural Networks with Temporal Attention and Learnable Adjacency matrix (MTGNN-TAttLA), Spatial Attention Graph with Temporal Convolutional Networks (SAG-TCN), and Attention-based Spatial-Temporal Graph Convolutional Networks (ASTGCN), to capture the intricate interplay of spatial and temporal dependencies within crude oil and precious metals markets. Moreover, the effectiveness of the attention mechanism in improving models' accuracies is shown. Our empirical results reveal remarkable prediction accuracy, with all three models outperforming conventional deep learning methods such as Temporal Convolutional Networks (TCN), long short-term memory networks (LSTM) and convolutional neural networks (CNN). The MTGNN-TAttLA model, enriched with a temporal attention mechanism, exhibits exceptional performance in predicting the direction of price movement in the WTI, Brent, and silver markets, while ASTGCN is the best-performing model for the gold market. Additionally, we observed that incorporating technical indicators from the crude oil and precious metal markets into the graph structure has improved the classification accuracy of spatial-temporal graph neural networks.

Keywords

• Spatial-temporal graph neural networks
• Deep learning
• Price movement classification
• Crude oil
• Precious metals