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Soybean futures price prediction with dual-stage attention-based long short-term memory: a decomposition and extension approach.

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Název: Soybean futures price prediction with dual-stage attention-based long short-term memory: a decomposition and extension approach.
Autoři: Fan, Kun, Hu, Yanrong, Liu, Hongjiu, Liu, Qingyang
Zdroj: Journal of Intelligent & Fuzzy Systems; 2023, Vol. 45 Issue 6, p10579-10602, 24p
Témata: COMMODITY futures, FUTURES sales & prices, HILBERT-Huang transform, COMMODITY exchanges, AGRICULTURAL extension work, FUTURES market, HEBBIAN memory, SOYBEAN
Geografický termín: CHINA
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:10641246
DOI:10.3233/JIFS-233060