A Hierarchical Forecasting Method for Tobacco Inventory-to-Sales Ratio Based on LSTM-LightGBM Model
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.
tobaccoinventory-to-sales ratioLSTMLightGBMhierarchical model
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