Predictive Modeling of Peanut Oil Prices Utilizing a Gaussian Process Regression-Based Machine Learning Framework

Accurate anticipation of fluctuations in commodity valuations is critical for diverse stakeholders, encompassing policymakers, investors, and supply chain entities, to ensure informed decision-making within volatile markets. As a staple edible oil, peanut oil exhibits pronounced price volatility, necessitating robust predictive frameworks to mitigate economic risks. This study leverages a decade-long weekly wholesale price index data set (January 1, 2010–January 10, 2020) to model price dynamics within the Chinese agricultural sector. A Gaussian process regression (GPR) methodology is implemented, integrating Bayesian optimization for hyperparameter tuning and [Formula: see text]-fold cross-validation to…