Forecasting wholesale prices of yellow corn through the Gaussian process regression

• Published in: Neural computing & applications Vol. 36; no. 15; pp. 8693 - 8710
• Main Authors: Jin, Bingzi, Xu, Xiaojie
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2024; Springer Nature B.V
• Subjects: Agricultural commodities; Artificial Intelligence; Basis functions; Commodities; Commodity prices; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Corn; Data Mining and Knowledge Discovery; Decision making; Forecasting; Gaussian process; Image Processing and Computer Vision; Machine learning; Mean square errors; Neural networks; Optimization techniques; Original Article; Players; Probability and Statistics in Computer Science; Regression models; Root-mean-square errors; Time series; Wholesale Price Index-US; Corn; Gaussian process regression; Commodity price; Forecasting; Machine learning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09531-2

For market players and policy officials, commodity price forecasts are crucial problems that are challenging to address due to the complexity of price time series. Given its strategic importance, corn crops are hardly an exception. The current paper evaluates the forecasting issue for China’s weekly wholesale price index for yellow corn from January 1, 2010 to January 10, 2020. We develop a Gaussian process regression model using cross validation and Bayesian optimizations over various kernels and basis functions that could effectively handle this sophisticated commodity price forecast problem. The model provides precise out-of-sample forecasts from January 4, 2019 to January 10, 2020, with a relative root mean square error, root mean square error, and mean absolute error of 1.245%, 1.605, and 0.936, respectively. The models developed here might be used by market players for market evaluations and decision-making as well as by policymakers for policy creation and execution.