Machine learning lumber price forecasts

Price predictions for primary agricultural commodities have been very significant to the majority of market participants for a considerable amount of time. To solve the problem, we examine the lumber price that is released every day in this study. The analytic sample spans over a decade and a half year, from January 2, 2014 to April 30, 2024. The price series that is being studied has a big impact on the business industry. In particular, Bayesian optimization approaches and cross-validation procedures are used to develop Gaussian process regression models with regard to this specific case. This situation therefore leads to the creation of price forecasting techniques. We provide reasonably accurate price estimates for the out-of-sample evaluation period, which spans from April 15, 2022 to April 30, 2024, using our empirical forecasting approach. It was discovered that the relative root mean square error for the price of lumber was 3.3216%. Given the availability of price forecasting models, governments and investors may make well-informed judgments on the lumber market since they have access to the necessary data.