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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.12785 |
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| _version_ | 1866910652960866304 |
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| author | Lee, Nathaniel Ngu, Noel Sahdev, Harshdeep Singh Motaganahall, Pramod Chowdhury, Al Mehdi Saadat Xi, Bowen Shakarian, Paulo |
| author_facet | Lee, Nathaniel Ngu, Noel Sahdev, Harshdeep Singh Motaganahall, Pramod Chowdhury, Al Mehdi Saadat Xi, Bowen Shakarian, Paulo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12785 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach Lee, Nathaniel Ngu, Noel Sahdev, Harshdeep Singh Motaganahall, Pramod Chowdhury, Al Mehdi Saadat Xi, Bowen Shakarian, Paulo Machine Learning 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. |
| title | Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.12785 |