Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods

This study employs Bayesian Symbolic Regression (BSR) to forecasting spot prices of various commodities. This novel method exhibits promising potential as a forecasting tool, especially in the context of variable (feature) selection. Yet, there is no much research on symbolic regression as a forecasting tool for prices time-series in economics and finance. BSR offers valuable capabilities for tackling the challenges of variable selection (feature selection) in econometric modeling, as well as, it is expected to deal with some other issues smoothly. Herein, the analysis is specifically tailored to time-series data representing commodity markets. The accuracies of BSR models are compared with those of some alternative models: Symbolic Regression with Genetic Programming, Dynamic Model Averaging, LASSO regression, Time-Varying Parameters regression, ARIMA, no-change forecasting, etc. Unlike previous simulations of BSR, that relied on synthetic data, this study employs real-world data from commodities markets. The findings are expected to provide valuable insights for researchers and practitioners interested in applying BSR in econometric and financial contexts in the future.