An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy
The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world. How to predict soybean future price is a challenging topic being studied by many researchers. This paper proposes a novel hybrid soybean future price...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Higher Education Press
2025-06-01
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| Series: | Frontiers of Agricultural Science and Engineering |
| Subjects: | |
| Online Access: | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024599 |
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| Summary: | The futures trading market is an important part of the financial markets and soybeans are one of the most strategically important crops in the world. How to predict soybean future price is a challenging topic being studied by many researchers. This paper proposes a novel hybrid soybean future price prediction model which includes two stages of data preprocessing and deep learning prediction. In the data preprocessing stage, futures price series are decomposed into subsequences using the ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) method. The Lempel-Ziv complexity determination method was then used to identify and reconstruct high-frequency subsequences. Finally, the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm. In the deep learning prediction stage, a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results. Based on the experimental results of soybean future price markets in China, Italy, and the United States, it was found that the hybrid method proposed provides superior performance in terms of prediction accuracy and robustness. |
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| ISSN: | 2095-7505 |