App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Price forecasting model for aquatic products based on multiscale decomposition and dual-stream feature fusion

Ji Houling1,2,3
Kuang Qixian1,2,3
Bai Jingpan1,2,3,4,5
Cheng Jiabo1,2,3
1. School of Computer Science, Yangtze University, Jingzhou Hubei 434023, China
2. Research Center for Digital Agriculture and Intelligent Engineering, Yangtze University, Jingzhou Hubei 434023, China
3. Artificial Intelligence Research Platform, Yangtze University, Jingzhou Hubei 434023, China
4. Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering, Yangtze University, Wuhan Hubei 430100, China
5. Sichuan Key Laboratory of Automotive Measurement, Control and Safety, Chengdu Sichuan 610039, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0057

Abstract

This paper proposed an EEFO-VMD-DPCA-BiGRU hybrid forecasting model to address the insufficient prediction accuracy caused by the significant nonlinearity and complex multi-scale fluctuations of aquatic product price series. Electric Eel Foraging Optimization (EEFO) optimized the key parameters of Variational Mode Decomposition (VMD) and achieved effective decomposition of price series. A dual-stream feature fusion structure designed a Dual Pooling Channel Attention (DPCA) mechanism to enhance multi-scale feature extraction. Bidirectional Gated Recurrent Unit (BiGRU) performed temporal modeling and forecasting. Weekly price data from five aquatic products were used for experimental validation. The results showed that the proposed model performed well in root mean square error and related evaluation metrics. Compared with the best benchmark model, the average prediction error decreased by 48.24%. The results indicate that the model reduces mode mixing and feature information loss, improves forecasting accuracy, and provides support for market monitoring, price warning, and decision-making in the aquatic product market.

Foundation Support

湖北省自然科学基金资助项目(2024AFB851)
湖北省教育厅科学技术研究项目(Q20241307)
汽车测控与安全四川省重点实验室(QCCK2025-006)
湖北省自然科学基金资助项目(2026AFB686)
油气钻采工程湖北省重点实验室开放基金资助项目(YQZC202503)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0057
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-30] Accepted Paper

Cite This Article

姬厚灵, 匡启贤, 白静盼, 等. 基于多尺度分解与双流特征融合的水产品价格预测模型 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0057. (Ji Houling, Kuang Qixian, Bai Jingpan, et al. Price forecasting model for aquatic products based on multiscale decomposition and dual-stream feature fusion [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0057. )

About the Journal

  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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