Supply chain sales forecasting based on lightGBM and LSTM combination model

摘要

Purpose The purpose of this paper is to design a model that can accurately forecast the supply chain sales. Design/methodology/approach This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments. Findings The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability. Originality/value The proposed model not only inherits the ability of LSTM model to automatically mine high-level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.