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Received:February 16, 2016 Revised:March 31, 2016
Received:February 16, 2016 Revised:March 31, 2016
中文摘要: 随着我国大力推进电商行业的发展,越来越多的电商企业加入到线上的竞争之中.随着销量的增大,第三方电商企业所掌握的销售数据也越来越多,这些分类上零散的销售数据给数据处理预测带来了一定的难度,常常导致在预测过程中数据不完备或者预测结果存在非常大的偏差.为了改善这一问题,这里提出了一种基于销售数据的产品重分类预测模型,利用产品销售共性提取产品聚类簇,再使用时间序列模型得出预测结果并通过隐马尔科夫预测模型给出预测结果的概率分布.通过实验分析,利用以上模型的预测获得较好的预测结果,对电商企业制定营销策略具有一定的参考价值.
Abstract:By promoting of our government, more and more electronic business enterprise join the competition of online sales. With the sharp increase in sales, an ever increasing number of sales data is accumulated by the third party enterprise, and these sales data which is too scattered in original classification, it brings some difficulty in sales forecasting, in detail, it would lead to incomplete condition or severe deviation of predicted value. To improve this problem, a prediction model which is based on goods re-classification is constructed in this paper. This model used common sales features of products to extract the product cluster, then it used time series forecasting model to give the predicted value which is decorated by HMM in probability distribution aspect. Through experimental analysis, the final predicted values preferable fit the true values, and this achievement will provide the reference value to enterprise in establishing policies of distribution.
keywords: electric business clustering time series HMM sales forecast
文章编号: 中图分类号: 文献标志码:
基金项目:
Author Name | Affiliation |
WANG Jian-Wei | School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
Author Name | Affiliation |
WANG Jian-Wei | School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
引用文本:
王建伟.基于商品聚类的电商销量预测.计算机系统应用,2016,25(10):162-168
WANG Jian-Wei.Online Sales Volume Prediction Based on Items Clustering.COMPUTER SYSTEMS APPLICATIONS,2016,25(10):162-168
王建伟.基于商品聚类的电商销量预测.计算机系统应用,2016,25(10):162-168
WANG Jian-Wei.Online Sales Volume Prediction Based on Items Clustering.COMPUTER SYSTEMS APPLICATIONS,2016,25(10):162-168