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深度学习组合模型用于智能供应链需求预测

Xiaoya Ma^1,2, Mengxiu Li¹, Jin Tong²

¹Department of Logistics Management and Engineering, Nanning Normal University, Nanninng 530023, China.

Biomimetics (Basel, Switzerland)

|July 28, 2023

概括

对新能源汽车 (NEV) 的准确预测对于行业增长至关重要. 一个新的SARIMA-LSTM-BP模型显著提高了对传统和深度学习方法的预测准确性.

科学领域:

汽车工业汽车工业汽车工业
供应链管理供应链管理
数据科学数据科学数据科学

背景情况:

由于环境问题,对新能源汽车 (NEV) 的需求日益增加.
需要准确的需求预测来支持NEV企业决策和行业发展.
智能供应链视角对于优化新能源汽车市场战略至关重要.

研究的目的:

在智能供应链框架内探索NEV需求预测.
提出和评估一个创新的综合预测模型.
为了提高预测准确度和性能,用于新能源汽车市场规划.

主要方法:

开发一种新的SARIMA-LSTM-BP组合模型,用于需求预测.
与传统的计量经济学和深度学习模型进行比较分析 (随机森林,SVR,LSTM,BP).
使用关键性能指标进行评估:根平均平方误差 (RMSE),平均平方误差 (MSE) 和平均绝对误差 (MAE).

主要成果:

与单个模型相比,SARIMA-LSTM-BP模型实现了较低的RMSE (2.757),MSE (7.603) 和MAE (1.912).
证明了卓越的预测准确性和性能.
在预测NEV需求方面表现优于已有的预测技术.

结论:

该SARIMA-LSTM-BP组合模型在NEV需求预测方面取得了重大进展.
这种混合方法为新能源汽车行业的战略规划提供了更准确,更可靠的基础.
这些发现支持在汽车行业采用先进的混合模型进行智能供应链管理.

关键词:

在SARIMA-LSTM-BP模型中.深度学习是一种深度学习.需求预测需要预测.智能供应链是一个智能供应链.新能源汽车新能源汽车预测建模预测建模

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