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…