用神经符号集成方法预测关键金属价格突变，提升准确率与可解释性。
Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach
- 融合多个神经模型与逻辑规则纠错，实现误差修正。
- 精度提升6.42%，召回率提高29.41%，F1值增长13.24%。
- 结果可解释，适合关注风险控制的能源与制造决策者。
预测钴、铜、镁、镍等关键金属的价格突变对缓解能源转型与制造业回流带来的经济风险至关重要。传统方法多依赖回归模型，本文提出一种神经符号集成框架，将多个神经模型与符号化误差检测校正规则结合，通过规则修正个体模型误差，并提供基于逻辑规则的可解释性说明。实验表明，该方法在最优神经模型基础上，精度提升6.42%，召回率提高29.41%，F1值增加13.24%。由于基于逻辑规则，该方法能明确指出哪些神经模型组合直接贡献于特定预测结果。
原文摘要 · Abstract (English)
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.
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