Forecasts of wholesale food price indices through Gaussian process regressions
Bingzi Jin, Xiaojie Xu
Abstract
For a wide range of market participants, food price projections in the agriculture industry have always been crucial. In this work, we tackle the prediction problem for the Chinese market’s weekly wholesale food price index over a 10-year period, from January 1, 2010 to January 3, 2020. We propose using Gaussian process regressions trained through Bayesian optimizations and cross-validation to carry out the analysis. The models that were produced accurately estimate the price between January 4, 2019 and January 3, 2020, with an out-of-sample relative root mean square error of 2.9391%. The projection’s results might be applied as stand-alone technical forecasts or in combination with other forecasts for policy research that involves formulating opinions about pricing trends.