Gaussian process regression based machine learning forecasts of steel price indices for the southern market in China

The government and investors have long put a tremendous lot of weight on commodities price projections. This study looks at the difficult problem of daily regional steel price index forecasting in the south Chinese market from January 1, 2010, to April 15, 2021. The literature has not given adequate attention to the forecast of this important commodity price indicator. We use Gaussian process regressions to validate our results after employing cross-validation and Bayesian optimizations to train our models. With an out-of-sample relative root mean square error of 0.5352%, the models that were constructed correctly forecasted the price indices between January 8, 2019, and April 15, 2021. The models developed can be used by policymakers and investors for analysis and decision-making. When reference data on the price patterns recommended by these models are employed, forecasting findings may be useful in creating comparable commodity price indices.