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A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence
Kaixuan Sun1, Qi Yao2,3, Yanhui Li2,3
1School of Economics and Management, Huainan Normal University, Huainan, China.
Plos One
|December 2, 2025
Summary
Accurate agricultural commodity price forecasting is achieved using a novel framework combining time series decomposition, deep learning, and swarm intelligence optimization. This advanced model significantly improves prediction accuracy for corn and wheat prices.
Area of Science:
- Agricultural Economics
- Computational Finance
- Data Science
Background:
- Agricultural commodity prices exhibit high volatility, impacting market stability and financial dynamics, especially during economic uncertainty.
- Accurate price prediction is difficult due to complex, nonlinear market characteristics and numerous influencing factors.
Purpose of the Study:
- To develop a novel price forecasting framework integrating time series decomposition, swarm intelligence, and deep learning.
- To enhance the accuracy and reliability of agricultural commodity price predictions.
Main Methods:
- Successive Variational Mode Decomposition (SVMD) for time series deconstruction.
- A CNN-BiLSTM model with an attention mechanism for feature extraction.
- Multiple Strategies Dung Beetle Optimization (MSDBO) for hyperparameter tuning.
Main Results:
- The proposed SVMD-MSDBO-CNN-BiLSTM-A model significantly outperformed nine baseline approaches in corn and wheat price forecasting.
- Achieved a 25.78% and 37.57% reduction in Mean Absolute Percentage Error (MAPE).
- Improved directional accuracy (Dstat) by 1.15% and 14.53% compared to top single models.
Conclusions:
- The integrated framework effectively captures nonlinear patterns and temporal dependencies for improved price forecasting.
- The novel approach offers a robust solution for volatile agricultural commodity markets.
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