Research on Gold Demand Prediciton Based on GM-GPR Model

Abstract

The prediction system of gold demands in China is faced with issues such as uncertain factors, limited historical data, and nonlinearity. In order to have a more accurate prediction of gold demands, a prediction method based on the integration of grey prediction and Gaussian process regression is proposed. Specifically, equal weights are assigned to each model and a grey prediction is adopted to reflect the uncertain and changing relationship of gold demands, with Gaussian process regression indicating the nonlinear impacts of factors on gold demands. Moreover, modified particle swarm optimization plays a role in optimizing the hyper-parameters of Gaussian process regression, which solves the issue that conjugate gradient algorithms depend on initial value setting and are susceptible to be confined by locally optimal solutions. According to the study, the proposal of the paper is superior to a separate Gaussian process regression or grey prediction in terms of better predicting gold demands.