关键词: 变分模态分解, 极限学习机, 分解集成, 农产品期货价格, 预测

Abstract: To capture the complex characteristics of price fluctuations and further improve prediction accuracy of the agricultural futures market, a hybrid model, combined with variational mode decomposition and extreme learning machine, is constructed based on the decomposition and ensemble theory. The VMD model, which can effectively avoid mode mixing and end effects in signal decomposition, is more robust to complex time series. In addition, by randomly generates the connection weights and implicit between the input layer and the hidden layer, the ELM model solves the problems of slow convergence and overfitting of traditional machine learning model. More importantly, aiming to select the key parameter mode K of the VMD model, a K-value optimization method based on the minimum fuzzy entropy criterion is proposed. Taking the rice, wheat, and soybean meal futures closing prices of CBOT as the objects, the empirical results show that the performance of the proposed VMD-ELM hybrid model is optimal.

Key words: VMD, ELM, decomposition and ensemble, agricultural commodity futures, forecast