Affiliations: [a] College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| [b] Institute of Informatics, University of Göttingen, Göttingen, German
Correspondence:
[*]
Corresponding author. Hongjiu Liu and Yanrong Hu, College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, 311300, China. E-mails: [email protected] (H. Liu) and [email protected] (Y. Hu).
Abstract: Accurately predicting soybean futures fluctuations can benefit various market participants such as farmers, policymakers, and speculators. This paper presents a novel approach for predicting soybean futures price that involves adding sequence decomposition and feature expansion to an Long Short-Term Memory (LSTM) model with dual-stage attention. Sequence decomposition is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, a technique for extracting sequence patterns and eliminating noise. The technical indicators generated enrich the input features of the model. Dual-stage attention are finally employed to learn the spatio-temporal relationships between the input features and the target sequence. The research is founded on data related to soybean contract trading from the Dalian Commodity Exchange. The suggested method surpasses the comparison models and establishes a fresh benchmark for future price forecasting research in China’s agricultural futures market.
Keywords: Soybean futures, time series forecasting, attention mechanism, sequence decomposition, technical indicator
