Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy

Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy

Financial market prediction faces significant challenges due to the complex temporal dependencies and heterogeneous data relationships inherent in futures price-spread data. Traditional machine learning methods struggle to effectively mine these patterns, while conventional long short-term memory (L...

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Bibliographic Details
Main Authors: Yongli Tang, Zhenlun Gao, Zhongqi Cai, Jinxia Yu, Panke Qin
Format: Article
Language:English
Published: PeerJ Inc. 2025-06-01
Series:PeerJ Computer Science
Subjects:
Futures price-spread forecasting
LSTM network
Hyperparameter optimization
Attention mechanism
Online Access:https://peerj.com/articles/cs-2865.pdf
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