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为提升电商平台商家备货效率，构建了一种适用于亚马逊跨境电商平台的销量预测模型，通过对某类型商品历史销量数据、产品特征数据以及产品的增量数据等一系列输入进行训练学习。该模型通过对GAN(generative adversarial network)网络、GRU(gate recurrent unit)网络以及注意力机制的结合使用，使其具备更高的预测准确率，同时通过增量式学习策略，在保证预测准确率的前提下使得模型训练时间大为缩短，为商家对商品的库存规划提供了一定的指导。
Abstract：In order to improve the stocking efficiency of e-commerce platform merchants, a sales prediction model suitable for Amazon cross-border e-commerce platform is constructed, and trains and learns through a series of inputs such as historical sales data, product feature data, and product incremental data of a certain type of commodity.The combination of GAN(generative adversarial network)network, GRU(gate recurrent unit)network and attention mechanism enables the model to have a higher prediction accuracy rate.At the same time, through incremental learning strategy, the model training time is greatly shortened on the premise of ensuring the prediction accuracy rate, which provides certain guidance for businesses to plan the inventory of goods.
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基本信息:
DOI：10.19724/j.cnki.jmju.2023.05.002
中图分类号:TP183;F713.36;F274
引用信息:
[1]刘华锐,颜胜男,郭仕忠,等.基于Attention-GRU-GAN神经网络的亚马逊平台下电商公司销量预测[J].闽江学院学报,2023,44(05):10-17.DOI:10.19724/j.cnki.jmju.2023.05.002.
基金信息:
2022年度福建省中青年教师教育科研项目(JAT220311)
2023-01-06
2023
2023-03-06
2023-09-20
2023
1
2023-09-25
2023-09-25