LSTM和GRU模型对湖北省物流需求预测性能比较研究
Comparative Study of LSTM and GRU Models on the Prediction Performance of Logistic Demand in Hubei Province
王泽宇 1张志清1
作者信息
- 1. 武汉科技大学,湖北 武汉 430065
- 折叠
摘要
预测物流需求是提高和优化物流供应链效率和降低成本的关键因素.文中以湖北省物流需求为应用场景,采用LSTM和GRU网络对湖北省物流需求量进行预测,根据误差对比发现,LSTM网络的性能显著优于GRU网络,拥有更高的预测精度.
Abstract
Predicting logistics demand is a key factor in improving and optimizing logistics supply chain efficiency and reducing costs.This paper takes the logistics demand of Hubei Province as the application scenario,and uses LSTM and GRU networks to predict its logistics demand.According to the error comparison,it is found that the performance of LSTM network is significantly better than that of GRU network,and it has higher prediction accuracy.
关键词
物流预测/LSTM网络/GRU网络Key words
logistics predicting/LSTM network/GRU network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据