Forecasts of thermal coal prices through Gaussian process regressions

Given thermal coal's significance as a tactical energy source, price projections for the commodity are crucial for investors and decision-makers alike. The goal of the current work is to determine whether Gaussian process regressions are useful for this forecast problem using a dataset of closing prices of thermal coal traded on the China Zhengzhou Commodity Exchange from January 4, 2016, to December 31, 2020. This is a significant financial index that has not received enough attention in the literature in terms of price forecasting. Our forecasting exercises make use of Bayesian optimizations and cross-validation. The price from January 02, 2020, to December 31, 2020 is successfully predicted by the generated models, with the out-of-sample relative root mean square error of 0.4210%. Gaussian process regressions are shown to be useful for the thermal coal price forecast problem. The outcomes of this projection might be used as independent technical forecasts or in conjunction with other forecasts for policy research that entails developing viewpoints on price patterns.