Predicting price spikes in critical metals such as Cobalt, Copper, Magnesium, and Nickel is crucial for mitigating economic risks associated with global trends like the energy transition and reshoring of manufacturing. While traditional models have focused on regression-based approaches, our work introduces a neurosymbolic ensemble framework that integrates multiple neural models with symbolic error detection and correction rules. This framework is designed to enhance predictive accuracy by correcting individual model errors and offering interpretability through rule-based explanations. We show that our method provides up to 6.42% improvement in precision, 29.41% increase in recall at 13.24% increase in F1 over the best performing neural models. Further, our method, as it is based on logical rules, has the benefit of affording an explanation as to which combination of neural models directly contribute to a given prediction.

本研究解决了预测钴、铜、镁和镍等关键金属价格峰值的需求，以减少与全球趋势相关的经济风险。我们提出了一种神经符号集成框架，通过结合多个神经模型和符号错误检测与纠正规则，显著提高了预测准确性。研究表明，所提方法在精度上提升了6.42%，召回率增加29.41%，并提供了解释能力，帮助理解哪些神经模型组合对预测结果产生了直接贡献。

通过神经符号集成方法预测金属价格峰值