Iron ore has had a highly global market since setting a new pricing mechanism in 2008. With current dollar values, iron ore concentrate for sale price, which was $39 per tonne (62% Fe) in December 2015, reached $218 per tonne (62% Fe) in mid-2021. It is hovering around $120 in October 2023 (cf. https://tradingeconomics.com/commodity/iron-ore). The uncertainty associated with these fluctuations creates hardship for iron ore mine operators and steelmakers in planning mine development and making future sale agreements. Therefore, iron ore price forecasting is of special importance. This paper proposes a cutting-edge multi-echelon tandem learning (METL) model to forecast iron ore prices. This model comprises variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term-memory (SLSTM) network, and attention mechanism (AT). In the proposed METL (i.e., the combination of VMD, MCNN, SLSTM, AT) model, the VMD decomposes the time series data into sub-sequential modes for better measuring volatility. Then, the MCNN is applied as an encoder to extract spatial features from the decomposed sub-sequential modes. The SLSTM network is adopted as a decoder to extract temporal features. Finally, the AT is employed to capture spatial–temporal features to obtain the complete forecasting process. Extensive computational experiments are conducted based on daily-based and weekly-based iron ore price datasets with different time scales. It was validated that the proposed METL model outperformed its single-echelon and other categorized models by 10–65% in range. The proposed METL model can improve the prediction accuracy of iron ore prices and thus help mining and steelmaking enterprises to determine their sale or purchase strategies.

自 2008 年制定新的定价机制以来，铁矿石市场已高度全球化。以当前美元价值计算，铁矿石精矿销售价格从 2015 年 12 月的每吨 39 美元（62% Fe）升至每吨 218 美元（62% Fe）。 Fe）于 2021 年中期。 2023 年 10 月，该价格徘徊在 120 美元左右（参见 https://tradingeconomics.com/commodity/iron-ore）。与这些波动相关的不确定性给铁矿石矿山运营商和钢铁制造商规划矿山开发和制定未来销售协议带来了困难。因此，铁矿石价格预测显得尤为重要。本文提出了一种尖端的多级串联学习（METL）模型来预测铁矿石价格。该模型包括变分模式分解（VMD）、多头卷积神经网络（MCNN）、堆叠长短期记忆（SLSTM）网络和注意力机制（AT）。在提出的 METL（即 VMD、MCNN、SLSTM、AT 的组合）模型中，VMD 将时间序列数据分解为子序列模式，以更好地测量波动性。然后，将 MCNN 用作编码器，从分解的子序列模式中提取空间特征。采用SLSTM网络作为解码器来提取时间特征。最后，利用AT捕获时空特征以获得完整的预测过程。基于不同时间尺度的每日和每周铁矿石价格数据集进行了广泛的计算实验。经验证，所提出的 METL 模型在范围内优于单梯队模型和其他分类模型 10-65%。所提出的METL模型可以提高铁矿石价格的预测精度，从而帮助采矿和炼钢企业确定其销售或采购策略。