多种算法融合的产品销售预测模型应用  

Application of Product Sales Forecast Model Based on Multiple Algorithm Fusion

作　　者：张雷东 王嵩[2] 李冬梅 朱湘宁 焦艳菲 ZHANG Lei-Dong;WANG Song;LI Dong-Mei;ZHU Xiang-Ning;JIAO Yan-Fei(School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China;Shenyang Golding NC Technology Co.Ltd.,Shenyang 110168,China)

机构地区：[1]中国科学院大学计算机控制与工程学院,北京100049 [2]中国科学院沈阳计算技术研究所,沈阳110168 [3]沈阳高精数控智能技术股份有限公司,沈阳110168

出　　处：《计算机系统应用》2020年第9期244-248,共5页Computer Systems & Applications

摘　　要：销量预测一直是一个热点研究的课题,对于各个企业有着重要的意义.近年来,随着深度学习的崛起,用于销量预测的模型越来越多,而单一模型的预测性能往往不够理想,所以出现了越来越多的组合模型.本文利用Stacking策略将XGBoost、支持向量回归(Support Vector Regression,SVR)、GRU神经网络作为基础模型,然后将LightGBM作为最终的预测模型,并且融合了新的特征.集中了几种模型的优势,大大提高了模型的预测性能,更加接近真实的销量数据,为回归预测提供一种新的预测方法.Sales forecasting has always been a hot research topic and has great significance for all enterprises.In recent years,with the rise of deep learning,there are more and more models for sales forecasting,and the performance of single models is often not ideal.Therefore,there are more and more combinatorial models.In this study,we use Stacking strategy to support XGBoost,Support Vector Regression(SVR),GRU neural network as the basic model,then lightGBM as the final prediction model,with new features are merged.The advantages of several models are condensed,which greatly improves the prediction performance of the model,good enough to be more close to the real sales data,and provide a new prediction method for regression prediction.

关 键 词：销量预测 Stacking算法 集成学习 特征工程 梯度提升树 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP18[自动化与计算机技术—计算机科学与技术] F274[经济管理—企业管理]