基于BP-AdaBoost的电商短期销量预测模型

电子商务是伴随互联网技术快速兴起的一种规模大、潜力大的新型商业模式,对产品进行短期销量预测能够帮助电商企业对市场变化采取更加迅速的反应和措施.本文通过电商销量历史数据和门户商品链接点击量建立了一种应用于电子商务会计系统的短期销量预测模型.借助AdaBoost思想集合多个传统的BP神经网络的预测结果,使其具备更高的预测准确率,根据电商短期销量变化的特点规划时间窗口的时序设计,建立考虑周末效应的以日为单位的销量预测模型.实验证明,该预测模型的预测误差可以控制在20%以内.

Forecast Model of Short-Term Sales in E-Commerce Based on BP-AdaBoost

E-commerce is a new business mode on a large scale and with great potential that is flourishing along with the emerging Internet technology. Forecasting short-term sales of products can help e-commerce companies respond more quickly to market changes. This study establishes a forecast model of short-term sales applied to the e-commerce accounting system based on historical data on e-commerce sales and clicks on portal products. With the adoption of AdaBoost idea, the forecast results of multiple traditional BP neural networks are assembled, leading to a higher accuracy. According to the characteristics of the short-term sales in e-commerce, we plan the timing design of time window and establish a forecast model of sales in the unit of day considering the weekend effect. Experiments show that the forecast error of this model can be controlled within 20%.