National Scrap Steel Price Forecasts Using Gaussian Process Regression Models

Governments and investors have historically found faith in price forecasts for a broad variety of commodities. By using time-series data covering 23 August 2013–15 April 2021, this study investigates the complicated challenge of projecting scrap steel prices that are provided daily at the national level for China. Prior research has not given adequate consideration to estimates in this crucial evaluation of commodity prices. In this instance, Gaussian process regression algorithms are developed using cross-validation processes and Bayesian optimization approaches, leading to the construction of price forecasts. Our empirical prediction technique produces reasonably accurate price estimates for the out-of-sample period encompassing 17 September 2019–15 April 2021, with a relative root mean square error of 0.1053%. Governments and investors may utilize price prediction models to make educated decisions about the scrap steel industry.