通过深度学习混合模型提高玉米行业的可持续性,用于预测价格波动
Chengjin Yang1, Yanzhong Zhai1, Zehua Liu1
1School of Electronic Information Engineering, North China Institute of Science and Technology, Beijing, China.
PloS one
|June 9, 2025
概括
本研究引入了一种新的预测模型,用于预测玉米价格短期波动,提高农业的可持续性. 该TLDCF-TSD-BBF模型改善了农民的预测准确性和市场稳定性.
科学领域:
- 农业经济学 农业经济学
- 时间序列预测时间序列预测
- 机器学习 机器学习
背景情况:
- 玉米价格波动造成市场不确定性,影响农民的决策,阻碍可持续的农业投资.
- 这种不稳定性威胁到玉米部门的长期可行性和可持续性.
- 准确的短期价格预测对于缓解这些挑战至关重要.
研究的目的:
- 为玉米价格短期波动提出和评估一种基于多模块波段变换的新型聚变预测模型.
- 提高玉米价格预测的准确性和稳定性.
- 通过减少价格不确定性,为玉米产业的可持续性做出贡献.
主要方法:
- 开发TLDCF-TSD-BBF模型,集成三层分解组合双过时间序列无效化 (TLDCF-TSD),双向时间卷积增强网络 (BiTCEN),双向长期和短期记忆网络 (BiLSTM) 和频率增强通道注意力机制 (FECAM).
- TLDCF-TSD分解了特征提取和降噪的价格系列.
- BiTCEN和BiLSTM分别捕捉了短期和长期的依赖性,而FECAM则改进了特征重点.
主要成果:
- 拟议的TLDCF-TSD-BBF模型在多个评估指标上表现出高于基线模型的性能.
- 具体的性能指标包括低的平均绝对误差 (MAE) 和平均平方误差 (MSE),以及高的R平方 (R2) 值.
- 该模型在不同数据集中实现了MAE值从0.0055到0.0137,MSE从0.0001到0.0002,MAPE从0.8456到1.7567,R2从0.9888到0.9955.
结论:
- 该TLDCF-TSD-BBF模型有效地预测了玉米价格的短期波动性,为市场参与者提供了宝贵的工具.
- 波束分解,卷积网络,LSTM和注意力机制的综合方法提高了预测的准确性和稳定性.
- 该研究使用中国玉米价格数据验证了该模型的有效性,支持其改善农业可持续性的潜力.
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