预测全球每月棉花价格:NNAR模型比传统模型的优势
Rumita Limbu Sanwa1, Raksha Khadka1, Yeon Nain Chi1
1Department of Agriculture, Food, and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, MD, United States.
Frontiers in artificial intelligence
|December 22, 2025
概括
准确的棉花价格预测对于市场参与者来说至关重要. 神经网络自动回归 (NNAR) 模型明显优于传统方法,为波动的全球棉花市场提供可靠的预测.
科学领域:
- 农业经济学 农业经济学
- 时间序列分析时间序列分析
- 机器学习 机器学习
背景情况:
- 准确的农业大宗商品价格预测对于经济稳定和知情决策至关重要.
- 全球每月棉花价格呈现波动性和非线性模式,挑战了ARIMA等传统预测模型.
- 现有的模型往往难以捕捉棉花市场固有的复杂动态.
研究的目的:
- 系统地比较传统的统计,机器学习和混合模型的预测性能,用于全球每月棉花价格预测.
- 为波动的棉花市场确定最有效的预测工具.
- 为全球棉花市场的利益相关者提供见解.
主要方法:
- 传统统计模型的评价:ARIMA,ETS,STL,TBATS,Theta. 这些都是传统的统计模型.
- 评估机器学习模型,特别是神经网络自动回归 (NNAR).
- 使用性能指标进行比较分析:RMSE,ME,MAE,MPE和MAPE.
主要成果:
- 该NNAR (26,1,14) [12]模型显示出卓越的性能,实现了最低的RMSE,MAE和MAPE.
- 该NNAR模型表明,在预测棉花价格时,预测准确度高,偏差最小.
- 30个月的预测预计每磅0.66美元至0.74美元之间的周期性波动,没有明确的长期趋势.
结论:
- 先进的机器学习技术,特别是NNAR,擅长捕获复杂的棉花价格非线性模式.
- 与传统的统计方法相比,NNAR模型提供了更好的预测可靠性.
- 调查结果支持农民,贸易商和政策制定者在波动的棉花市场中加强决策.
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