一个新的农业大宗商品价格预测模型,集成深度学习和增强的群集智能算法
Kaixuan Sun1, Qi Yao2,3, Yanhui Li2,3
1School of Economics and Management, Huainan Normal University, Huainan, China.
PloS one
|December 2, 2025
概括
准确的农业商品价格预测是通过一种新的框架来实现的,该框架结合了时间序列分解,深度学习和群集智能优化. 这种先进的模型显著提高了对玉米和小麦价格的预测准确度.
科学领域:
- 农业经济学 农业经济学
- 计算金融是指计算金融.
- 数据科学数据科学数据科学
背景情况:
- 农业大宗商品价格具有很高的波动性,影响市场稳定性和金融动态,尤其是在经济不确定性时期.
- 由于复杂,非线性市场特征和众多影响因素,难以准确预测价格.
研究的目的:
- 开发一个新的价格预测框架,整合时间序列分解,群群智能和深度学习.
- 提高农产品价格预测的准确性和可靠性.
主要方法:
- 顺序变化模式分解 (SVMD) 用于时间序列分解.
- 一个具有特征提取注意力机制的CNN-BiLSTM模型.
- 多种策略泥虫优化 (MSDBO) 用于超参数调整.
主要成果:
- 拟议的SVMD-MSDBO-CNN-BiLSTM-A模型在玉米和小麦价格预测方面显著优于九种基线方法.
- 实现了平均绝对百分比误差 (MAPE) 的25.78%和37.57%的减少.
- 与顶级单一模型相比,提高了1.15%和14.53%的方向准确性 (Dstat).
结论:
- 综合框架有效地捕捉非线性模式和时间依赖性,以改善价格预测.
- 这种新的方法为不稳定的农业大宗商品市场提供了可靠的解决方案.
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