计算机工程与应用2019，Vol.55Issue(15)：177-184,8.DOI:10.3778/j.issn.1002-8331.1811-0231
小样本下多维指标融合的电商产品销量预测
E-Commerce Product Sales Forecast with Multi-Dimensional Index Integration Under Small Sample
摘要
Abstract
This paper studies the prediction model based on integrated learning Xgboost to break the low accuracy limita-tions 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 fore-cast 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.关键词
销量预测/电商产品/小样本/多维指标融合/XgboostKey words
sales forecast/ e-commerce products/ small sample/ multiple indicators fusion/ Xgboost分类
信息技术与安全科学引用本文复制引用
何喜军,马珊,武玉英,蒋国瑞..小样本下多维指标融合的电商产品销量预测[J].计算机工程与应用,2019,55(15):177-184,8.基金项目
北京市自然科学基金(No.9172002) （No.9172002）
国家自然科学基金(No.71371018) （No.71371018）
北京市社会科学基金(No.15JGB124). （No.15JGB124）
