Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach

TL;DR

This paper presents a neurosymbolic ensemble approach for predicting critical metal price spikes, combining neural models with symbolic rules to improve accuracy and interpretability, with significant performance gains over traditional neural methods.

Contribution

Introduces a novel neurosymbolic ensemble framework that integrates neural models with symbolic error correction for improved prediction accuracy and interpretability in metal price spike forecasting.

Findings

Up to 6.42% improvement in precision

29.41% increase in recall

Enhanced interpretability through rule-based explanations

Abstract

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.