Machine Learning Copper Price Predictions: Evidence Based on Gaussian Process Regressions Tuned with Cross-Validation and Bayesian Optimization
Bingzi Jin, Xiaojie Xu
Abstract
For a considerable amount of time, many market participants have placed great importance on price forecasts for major metal commodities. To tackle the problem, our study looks at the price of copper recorded on a daily basis. The sample under inquiry spans more than 10 years, from 01/02/2014 to 04/12/2024, and the price series under examination has substantial financial implications. In this instance, Gaussian process regression models are built using cross-validation techniques and Bayesian optimization methodologies, and the resultant strategies are employed to provide price estimations. The relative root-mean-square error of 1.3880% indicates that our empirical prediction approach produces reasonably accurate price estimates for the out-of-sample assessment period of 04/11/2022–04/12/2024. Models for predicting prices give investors and governments the information they require to make wise choices about the copper market.