Yang Xiong, Zhang Zihang, Xu Huihua
Fuzhou University Zhicheng College, Fuzhou, China.
School of Accounting, Zhongnan University of Economics and Law, Wuhan, China.
Heliyon. 2024 Aug 23;10(17):e36631. doi: 10.1016/j.heliyon.2024.e36631. eCollection 2024 Sep 15.
Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized. The results show that the MAPE(mean absolute percentage error) of one-step, three-step, and six-step methods for predicting crude oil prices are 0.48%, 0.66%, and 0.75%, respectively, and the MAPE of soybean prediction results are 0.22%, 0.27%, and 0.37%, respectively. The empirical results and ablation experiments show that it outperforms other benchmark models in terms of both horizontal and directional accuracy. Notably, it outperforms in predicting soybean futures prices, which demonstrates the ability of our model to better capture the characteristics of both the time and frequency domains of the series, to take sufficient consideration of the series characteristics, and to ensure robustness.
商品期货是物资贸易中的一种重要套期保值工具，通过准确预测价格，各国和企业能够做出明智的生产和消费决策。本文介绍了一种新颖的机器学习集成方法，该方法将分解算法和物理优化算法相结合来预测商品期货价格。首先，通过RIME算法（Rime优化算法）对VMD（变分模态分解）进行优化，以获得最优的模态分解结果，然后分别使用ELM（极限学习机）和FA（傅里叶注意力）模型预测趋势项和季节项，最后将结果进行合成。结果表明，预测原油价格的一步法、三步法和六步法的平均绝对百分比误差（MAPE）分别为0.48%、0.66%和0.75%，大豆预测结果的MAPE分别为0.22%、0.27%和0.37%。实证结果和消融实验表明，该方法在水平精度和方向精度方面均优于其他基准模型。值得注意的是，它在预测大豆期货价格方面表现出色，这证明了我们的模型能够更好地捕捉序列的时域和频域特征，充分考虑序列特征并确保稳健性。
