烟台大学学报（自然科学与工程版）2024，Vol.37Issue(3)：256-261,6.DOI:10.13951/j.cnki.37-1213/n.230808
基于LSTM-LightGBM模型的烟草存销比层次化预测方法
A Hierarchical Forecasting Method for Tobacco Inventory-to-Sales Ratio Based on LSTM-LightGBM Model
摘要
Abstract
The paper employs the LSTM-LightGBM algorithm and incorporates the geographical location and grade information of retailers to develop a hierarchical model for accurately forecasting the inventory-to-sales ratio of tobac-co products.The model initially uses the LSTM network to forecast the overall inventory-to-sales ratio across differ-ent regions and grades.Subsequently,the obtained overall inventory-to-sales ratio is utilized as supplementary in-put for LightGBM to predict the inventory-to-sales ratio for each type of cigarette sold by individual retailers.The proposed model progressively combines macro-and micro-level features of the data.The validation results,using actual tobacco sales data from a specific region,demonstrate the superior predictive accuracy of the proposed ap-proach.关键词
烟草/存销比/LSTM/LightGBM/层次化模型Key words
tobacco/inventory-to-sales ratio/LSTM/LightGBM/hierarchical model分类
数理科学引用本文复制引用
李家蕊,杨旻..基于LSTM-LightGBM模型的烟草存销比层次化预测方法[J].烟台大学学报（自然科学与工程版）,2024,37(3):256-261,6.基金项目
山东省自然科学基金资助项目(ZR2021MA010). （ZR2021MA010）
