Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
- Title
- Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach
- Creator
- Singh A.; Mahajan J.; Sharma M.; Saxena A.; Khan M.H.; Kakani R.
- Description
- This research introduces a hybrid model for copper price prediction, employs advanced machine learning models (linear regression, random forest, SVM, Adaboost, ARIMA), and utilizes the SHAP method for model interpretability. The study focuses on transportation-related variables over a 10-year period from Bloomberg Terminal, employing STL decomposition for time series forecasting. Key features impacting copper prices are identified, emphasizing the significance of demand, transportation, and supply. The Random Forest model highlights the critical role of demand. Addressing transportation supply constraints is crucial for enhancing model output in the dynamic copper market. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Copper price prediction; Explainable AI (XAI); Machine learning models; Random Forest algorithm; Time series forecasting
- Coverage
- Singh A., Christ University, Bengaluru, India; Mahajan J., Christ University, Bengaluru, India; Sharma M., Christ University, Bengaluru, India; Saxena A., Christ University, Bengaluru, India; Khan M.H., Christ University, Bengaluru, India; Kakani R., Sg Analytics, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh A.; Mahajan J.; Sharma M.; Saxena A.; Khan M.H.; Kakani R., “Enhancing Copper Price Prediction: A Machine Learning and Explainable AI Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 28, 2025, https://archives.christuniversity.in/items/show/19185.