基于时空注意力和变压器架构的CBEC库存优化模型设计
Zongping Lin1, Yingyi Huang2, Jing Yang3
1School of Economics and Management, Quanzhou University of Information Engineering, Quanzhou, Fujian, China.
PloS one
|February 2, 2026
概括
这项研究引入了一种新的时空变压器模型,用于准确的跨境库存预测. 该模型提高了长期预测的准确性,优于现有方法.
科学领域:
- 供应链管理 供应链管理
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 跨境企业面临的挑战是长期库存预测不准确.
- 现有的模型很难在库存数据中捕捉复杂的时空动态.
研究的目的:
- 开发一个先进的跨境库存预测模型.
- 提高长期库存预测的准确性和可靠性.
主要方法:
- 使用了一种空间时间感知变压器,结合了一种时间意识自我注意力机制.
- 采用多尺度扩散卷积来模拟在道路网络上的库存数据扩散.
- 实现了多维特征融合模块,用于适应性时空特征集成.
主要成果:
- 与ASTGNN模型相比,拟议的模型表现出优越的性能.
- 实现了7.6%的平均绝对误差 (MAE),4.2%的平均绝对百分比误差 (MAPE) 和1.1%的根平均平方误差 (RMSE) 的降低.
结论:
- 时空感知变压器模型在跨境库存预测方面取得了重大进展.
- 该模型有效地捕捉全球和本地时空特征,以提高预测准确度.
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