Commodity futures price forecast based on multi-scale combination model

Abstract

Along with developing the commodity futures market, its promoting effect on China’s economic development has gradually increased. Research on the price prediction of commodity futures has important practical significance to society and enterprises. However, commodity futures price series often show nonstationary and nonlinear characteristics In this paper, a new multi-scale combined prediction model is proposed, which combines variational mode decomposition (VMD), long short-term memory neural network (LSTM), and improved self-attention mechanism (XNSA). First, VMD decomposes futures prices into several components to reduce their nonstationarity. Then, the LSTM model with an improved self-attention mechanism (XNSA) is used to model and optimize the decomposed sub-sequences so that the model can concentrate on learning more important data features and further improve the prediction performance. In order to verify the effectiveness of this method, this paper takes No. 1 Soybeans Futures, Corn Futures, and Soybean Meal Futures daily closing price series from Dalian Commodity Exchange as representatives to predict their future return trend. The results show that compared with the existing combination forecasting models, the proposed multi-scale combination model (VMD-LSTM-XNSA) has better forecasting performance. It lays the foundation for developing a corresponding quantitative investment strategy.