基于机器学习组合模型的电商商品销量预测

E-commerce Commodity Sales Forecast Based on Machine Learning Combination Model

如何准确高效地预测销量是企业一直以来关注的重要问题.传统的时间序列预测方法虽然在研究和实践中占主导地位,但是存在一定的局限性.随着大数据的发展,电商企业能获取前所未有的数据量和数据特征,仅利用过去的行为和趋势很难准确地对销量进行预测.本文提出一种基于随机森林、GBDT、XGBoost算法的成本厌恶偏向性组合预测模型,并利用每个商品的成本数据实现对样本的精细化赋权,进而输出预测结果.结果表明,组合预测模型能更精确预测销量,对电商企业降低商品管理成本有重要意义.

How to accurately and efficiently forecast sales data is an important issue for enterprises. Although the traditional time series prediction method is dominant in research and practice, it has some limitations. With the development of big data, e-commerce enterprises can obtain unprecedented data volume and data characteristics, and it is difficult to accurately predict sales only by using past behaviors and trends. This study proposes a risk aversion-biased combination forecasting model based on the random forest, GBDT, and XGBoost algorithm and used the cost data of each commodity to realize the accurate sample weighting and to output the forecasting results. The experimental results show that the combination forecasting model can predict sales more accurately, which is of great significance for e-commerce enterprises to reduce the cost of commodity management.