使用CEEMDAN和时间延迟神经网络的农业大宗商品价格混合建模方法
Pramit Pandit1, Atish Sagar2, Bikramjeet Ghose3
1Department of Agricultural Statistics & Computer Application, Rabindra Nath Tagore Agriculture College, Birsa Agricultural University, Ranchi, 834006, India.
Scientific reports
|November 4, 2024
概括
准确的农业商品价格预测至关重要. 一个新的全套实证模式分解与自适应噪音时间延迟神经网络模型显著改善了对非线性,非静止价格系列的预测.
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利益相关者来说至关重要,以减轻风险并为政策提供信息.
- 传统的预测方法与农业价格数据的复杂性,非静止性和非线性性质作斗争.
- 现有的实证模式分解 (EMD) 变体和基准模型在捕捉这些价格动态方面存在局限性.
研究的目的:
- 为非线性,非静态的农业价格系列提出和评估一种新的混合预测模型.
- 将拟议的CEEMDAN-TDNN模型的性能与各种EMD变体和基准模型进行比较.
- 评估印度油作物的预测准确度和方向变化预测能力.
主要方法:
- 开发一个完整的综合实证模式分解与自适应噪声时间延迟神经网络 (CEEMDAN-TDNN) 模型.
- 使用印度主要油作物的每月批发价格进行比较分析.
- 根据EMD,集体EMD,互补集体EMD,ARIMA,SVR,GBM和随机森林模型进行评估.
- 使用迪博德-马里亚诺测试,弗里德曼测试和泰勒图进行统计验证.
主要成果:
- 与所有其他评估模型相比,CEEMDAN-TDNN模型显示出更高的预测准确性.
- 在根平均平方误差 (RMSE),相对RMSE和平均绝对百分比误差 (MAPE) 中观察到显著的平均改善.
- 该模型在预测定向价格变化方面表现更好.
- 统计测试证实了CEEMDAN-TDNN模型的优异性.
结论:
- CEEMDAN-TDNN混合模型为预测非线性和非静止农业大宗商品价格提供了明显的优势.
- 这种先进的方法提供了更可靠的价格预测,有利于农民,贸易商和政策制定者.
- 该研究强调了混合分解神经网络模型在农业经济学中的潜力.
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