Investors and governments have traditionally depended on estimates of prices of various commodities. Using data from 08/23/2013 to 04/15/2021, this study aims to investigate the challenging problem of forecasting scrap steel prices that are published on a daily basis for the east China regional market. The research has not given much attention to predictions of this important commodity price indicator.
Gaussian process regression models, which are estimated using cross-validation approaches with Bayesian optimizations, are used to provide price forecasts.
Having a relative root mean square error of 0.4357%, the constructed models appropriately generate price forecasts for the out-of-sample testing stage from 09/17/2019 to 04/15/2021.
Models designed to research prices can be used by governments and investors to make well-informed decisions.
