关键词: 销量预测, 电商产品, 小样本, 多维指标融合, Xgboost

Abstract: This paper studies the prediction model based on integrated learning Xgboost to break the low accuracy limitations of traditional prediction methods in small sample data e-commerce products. It comprehensively considers the multi-dimensional indicators of e-commerce products, including：online search, online reviews, page access, inventory and order quantity, sentiment index etc., entropy method is used to fuse the same type of indicators. Logistic function and regular correction term are applied and a sales forecasting model based on integrated learning Xgboost is built combining the greedy algorithm. Carrying out model test for lenovo zuk z2 mobile phone of Jingdong Mall, and comparing the results with BP neural network prediction model, SVM support vector machine prediction model and BP-SVM combination forecast model, the result shows that the accuracy of the Xgboost prediction model with multidimensional index fusion is higher, and this study provides new method and idea for e-commerce products sales forecasts under small sample data.

Key words: sales forecast, e-commerce products, small sample, multiple indicators fusion, Xgboost