This study examines the application of machine learning in predicting price risk boundaries for industrial metals and critical minerals, emphasizing the role of statistical dependencies among their yields. Given these commodities’ pivotal role in various industries and their influence on the global economy, accurate forecasting of their price boundaries is critical. Our research employs statistical dependencies as key features to uncover meaningful correlations essential for effective forecasting, underscoring the value of analyzing rate-based statistical dependencies alongside price fluctuations. Applying explainable artificial intelligence (xAI) techniques validates the effectiveness of combining these approaches for price boundary prediction. Our findings indicate variations in effectiveness between the Pearson correlation coefficient and normalized mutual information, challenging the Pearson coefficient’s dominance in financial analysis. The study demonstrates that the relevance of each statistical dependency varies across different machine learning models, necessitating a comprehensive analytical approach incorporating information-theoretic methods. Our analysis improves the accuracy and interpretability of price predictions by pinpointing critical relationships among selected industrial metals and critical minerals.

中文翻译：

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通过统计机器学习技术对工业金属和关键矿物的风险边界进行实用预测


本研究探讨了机器学习在预测工业金属和关键矿物价格风险边界中的应用，强调了其产量之间统计依赖性的作用。鉴于这些商品在各个行业中的关键作用及其对全球经济的影响，准确预测其价格界限至关重要。我们的研究采用统计依赖性作为关键特征，以揭示有效预测所必需的有意义的相关性，强调分析基于利率的统计依赖性以及价格波动的价值。应用可解释的人工智能 (xAI) 技术验证了结合这些方法进行价格边界预测的有效性。我们的研究结果表明皮尔逊相关系数和标准化互信息之间的有效性存在差异，挑战了皮尔逊系数在财务分析中的主导地位。该研究表明，每种统计依赖性的相关性在不同的机器学习模型中各不相同，因此需要采用结合信息论方法的综合分析方法。我们的分析通过查明选定的工业金属和关键矿物之间的关键关系，提高了价格预测的准确性和可解释性。