一个分层的RF-XGBoost模型用于短期周期农产品销售预测
Jiawen Li1,2, Binfan Lin1, Peixian Wang1
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Foods (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了一个层次的RF-XGBoost模型,用于准确预测农业销售,通过改进需求预测来显著减少食物浪费. 该模型提高了从农场到餐桌的供应链效率.
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
- 供应链管理 供应链管理
背景情况:
- 农产品短周期销售预测对于通过调整供应与需求来最大限度地减少食物浪费至关重要.
- 由于不确定的因素,由于波动和不连续的销售数据,预测准确性受到挑战.
- 现有的模型经常与农业市场动态固有的复杂性作斗争.
研究的目的:
- 开发和评估一种新的等级预测模型,以提高农产品短期销售预测.
- 提高需求预测的准确性,从而减少食物浪费和优化农业供应链.
- 通过先进的建模方法来解决销售数据的波动性和不连续性.
主要方法:
- 开发了一种将随机森林 (RF) 和极端梯度提升 (XGBoost) 结合在一起的等级模型.
- 第一个层使用射频与灰色关系分析 (GRA) 进行初始预测和残余提取.
- 第二层使用XGBoost在剩余集群特征上进行精细预测.
主要成果:
- 拟议的RF-XGBoost模型与独立的RF和XGBoost相比表现出更高的性能.
- 与RF和XGBoost相比,平均绝对百分比误差 (MAPE) 分别减少了10%和12%.
- 确定系数 (R2) 增加了22%和24%,表明预测准确度更高.
结论:
- 层次化的RF-XGBoost模型显著提高了短期农业销售预测的精度.
- 该模型的有效性在各种农产品中得到了验证,证明了其广泛适用性.
- 实施为优化供应链和减少食物浪费提供了巨大的好处.
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