A Multi-Factor Selection and Fusion Method through the CNN-LSTM Network for Dynamic Price Forecasting

Abstract

Commodity prices are important factors for investment management and policy-making, and price forecasting can help in making better business decisions. Due to the complex and volatile nature of the market, commodity prices tend to change frequently and fluctuate violently, often influenced by many potential factors with strong nonstationary and nonlinear characteristics. Thus, it is difficult to obtain satisfactory prediction effects by only using the historical data of prices individually. To address this problem, a novel dynamic price forecasting method based on multi-factor selection and fusion with CNN-LSTM is proposed. First, the factors related to commodity price are collected, and Granger causality inference is used to identify causal factors that affect the commodity price. Then, XGBoost is used to evaluate the importance of the remaining factors and screen out critical factors to reduce the interference of redundant information. Due to the high amount and complicated changes of the selected factors, a convolutional neural network is employed to fuse the selected factors and extract the hidden features. Finally, a long short-term memory network is adopted to establish a multi-input predictor to obtain the dynamic price. Compared with several advanced approaches, the evaluation results indicate that the proposed method has an excellent performance in dynamic price forecasting.