Copper price forecasting is crucial for both investors and governments due to its significant economic impact. Recently, machine learning techniques have been widely employed to construct copper price forecasting models, demonstrating high forecasting accuracy. However, there are two main limitations in these models: (1) the lack of ability to capture the non-Euclidean relationships among numerous features; and (2) using purely data-driven algorithms, which lack tractability and physical effectiveness. To address these challenges, this study proposes a multi-view graph transformer (MVGT) model for 1-month ahead copper price forecasting. MVGT integrates a parametric fractional Brownian motion module, which provides conditional expectations of future copper prices for data augmentation. Moreover, to comprehensively capture the non-Euclidean structure of copper features, MVGT introduces five graph generation methods. Furthermore, a multi-view graph transformers model is designed to provide structural copper feature embeddings, and an attention-based multi-view fusion mechanism is developed to enable the MVGT to comprehensively understand market trends while focusing on the most influential views. Experimental results on the COMEX and LME datasets demonstrate that MVGT outperforms baseline models in terms of training efficiency, forecasting accuracy, and generalization.

由于铜价预测对经济有重大影响，因此对投资者和政府都至关重要。最近，机器学习技术被广泛用于构建铜价预测模型，显示出很高的预测准确性。然而，这些模型有两个主要的局限性：（1） 缺乏捕捉众多特征之间的非欧几里得关系的能力;（2） 使用纯粹的数据驱动算法，缺乏可处理性和物理有效性。为了应对这些挑战，本研究提出了一种多视图图形变压器 （MVGT） 模型，用于提前 1 个月的铜价预测。MVGT 集成了一个参数分数阶布朗运动模块，该模块为数据增强提供了对未来铜价格的条件预期。此外，为了全面捕获铜特征的非欧几里得结构，MVGT 引入了五种图生成方法。此外，设计了多视图图 transformers 模型，提供结构铜特征嵌入，并开发了基于注意力的多视图融合机制，使 MVGT 能够在关注最有影响力的视图的同时，全面了解市场趋势。在 COMEX 和 LME 数据集上的实验结果表明，MVGT 在训练效率、预测准确性和泛化方面优于基线模型。