一个基因算法优化了基于VMD和LSTM网络的农业价格预测混合模型
Kapil Choudhary1,2,3, Girish Kumar Jha4, Ronit Jaiswal5
1Agriculture University, Jodhpur, Rajasthan, 342304, India.
Scientific reports
|March 23, 2025
概括
准确的农业大宗商品价格预测得到了改进,使用了新的VMD-LSTM模型. 这种混合方法通过分解和建模价格数据来增强预测,优于现有方法以获得更好的市场洞察力.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 农业大宗商品价格表现出复杂,非线性和非静止的模式,挑战了传统的预测模型.
- 现有的预测方法往往难以捕捉这些固有的复杂性,导致准确度低于最佳.
- 准确的价格预测对于农民,贸易商和政策制定者进行知情决策至关重要.
研究的目的:
- 开发和评估一种新的混合变态分解-长期短期记忆 (VMD-LSTM) 模型,用于增强农业大宗商品价格预测.
- 解决现有模型在捕捉非线性和非静止价格动态方面的局限性.
- 提高农业价格预测的准确性和可靠性.
主要方法:
- 一种混合VMD-LSTM模型,集成VMD和LSTM组件的遗传算法 (GA) 优化.
- 用GA优化的VMD将价格序列分解为稀疏的内在模式函数 (IMF),以实现高效的建模.
- 个人IMF使用GA优化的LSTM进行预测,并组合最终预测.
主要成果:
- 与单个LSTM和其他基于分解的模型 (EMD-LSTM,EEMD-LSTM,CEEMDAN-LSTM) 相比,VMD-LSTM模型表现出更高的性能.
- 在玉米,棕油和大豆油价格数据中,观察到根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 的显著减少.
- 统计测试 (TOPSIS,Diebold-Mariano) 证实了VMD-LSTM模型的增强预测准确性.
结论:
- 拟议的GA优化VMD-LSTM模型为农业大宗商品价格预测提供了强大而准确的解决方案.
- 这种先进的预测工具可以大大帮助利益相关者做出更明智的经济决策.
- 该模型能够捕捉复杂的价格动态,这代表了农业市场分析的重大进步.
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