在间歇性需求预测中,特征工程优先于建筑复杂性
B Sendhil Nathan1,2, P M Aravinth1, B Veera Siva Reddy3
1Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing Kurnool (IIITDM Kurnool), Kurnool, Andhra Pradesh, 518008, India.
Scientific reports
|January 6, 2026
概括
使用光滑混合发生尺寸 (SHOS) 框架的特征工程显著改善间歇性需求预测. 这种基于统计学的方法优于复杂的模型,为供应链管理提供了更有效的解决方案.
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 统计建模 统计建模
背景情况:
- 间歇性需求预测在大型供应链中存在重大挑战,原因是数据稀疏性和可变性.
- 现有的研究往往侧重于复杂的模型架构,忽视了统计学基础的特征工程.
研究的目的:
- 引入平滑混合事件大小 (SHOS) 框架,以提高间歇性需求预测.
- 在监督机器学习模型中评估SHOS产生的特征的有效性.
主要方法:
- 开发了SHOS框架,使用稀疏度意识的指数级平滑来进行需求发生和规模估计.
- 集成的SHOS功能嵌入到机器学习模型中,这些模型是在大规模的零填充面板数据上训练的.
- 通过滚动窗口交叉验证验证汽车售后市场数据集的方法.
主要成果:
- 在间歇性需求细分市场中,SHOS增强的模型将平均绝对误差 (MAE) 降低了50%左右,加权平均绝对百分比误差 (WMAPE) 降低了40%以上.
- 单阶段SHOS框架的性能优于复杂的基于障碍的双阶段模型.
- 统计测试证实了SHOS模型的显著性能优势 (p < 0.001).
结论:
- 正如SHOS所示,基于统计数据的特征工程可以比增加的模型复杂性更有效地进行间歇性需求预测.
- SHOS框架为大规模的作战部署提供了一个计算效率高且可解释的替代方案.
- 未来的研究应该探索跨不同应用领域的SHOS验证.
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