We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Forecasting wholesale prices of yellow corn through the Gaussian process regression.
- Authors
Jin, Bingzi; Xu, Xiaojie
- Abstract
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.
- Subjects
KRIGING; WHOLESALE prices; CORN prices; PRICES; STANDARD deviations; FORECASTING; AGRICULTURAL forecasts
- Publication
Neural Computing & Applications, 2024, Vol 36, Issue 15, p8693
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-024-09531-2