数据与计算发展前沿2024，Vol.6Issue(5)：169-177,9.DOI:10.11871/jfdc.issn.2096-742X.2024.05.016
基于多维度数据驱动的商品需求量预测研究
A Study on Multidimensional Data-Driven Commodity Demand Forecasting
摘要
Abstract
[Objective]For the tobacco industry,it is crucial to improve the commodity resource allocation strategy of retailers,optimize the supply policy of commodities,and solve the problems of unrea-sonable allocation of goods,behindhand commodities supply policy and low efficiency of business operations,which exist in the current commodities allocation based on the historical sales level and experiences but lack of systematic data theory support[Methods]A multi-dimensional data-driven neural network model is proposed.the data used by the model includes merchant internal order data and external shopping area,etc.By abnormal sample removal,feature construction,and partition of training and validation data sets,the attention layer,the dense layer,and the LightGBM model layer are passed successively on the training set.[Results]A prediction accuracy of 96.57%is finally achieved on the test set.[Conclusion]Based on the multi-di-mensional data of the cigarette system,this technology can establish a highly adaptable and flexible demand fore-casting system,break the current operation mode of determining the demand according to the stalls,realize high prediction accuracy,and provide technical support for the intelligent placement of cigarettes for enterprises.关键词
神经网络/需求预测/特征构造/Attention层/LightGBM模型Key words
neural networks/demand forecasting/feature construction/Attention layer/LightGBM model引用本文复制引用
陈宇镔,洪烨,崔文娟,黄敏毅,张锦玉..基于多维度数据驱动的商品需求量预测研究[J].数据与计算发展前沿,2024,6(5):169-177,9.基金项目
中国烟草总公司广东省公司科技项目"基于多维度数据驱动的零售客户精准投放模型研究"(粤烟科项202111) （粤烟科项202111）
