考虑促销因素的医药电商平台需求预测研究

医药电商平台需求预测涉及到药品自身属性及电商平台推出的各种促销活动，本文针对以上影响药品销量的因素提出了时间序列-机器学习组合模型对医药电商平台进行需求预测。传统研究促销因素的需求预测文献将促销阶段商品销量拆分为常规销量和促销增量的线性组合，本文首先拟合各药品促销阶段的常规销量，根据各药品常规销量时间序列数据及服用周期，使用SARIMA模型拟合药品的常规销量预测值，并将常规销量预测值与商品促销特征数据一同输入XGBoost模型进行集成学习预测。本文使用国内某医药电商平台真实销售数据测试组合模型的有效性，结果显示组合预测模型的预测效果相比其他三种传统预测模型更优。此外，本文验证了不同折扣力度下组合预测模型的有效性，以及促销变量在预测模型中的有效性，同时研究了数据共享策略在需求预测中的应用场景，结果显示预测模型在引入促销变量和采用数据共享策略后都能显著降低模型的预测误差。

Epharmacy Demand Forecasting in the Presence of Promotional Activities

E-pharmacy demand forecasting is highly affected by drug attributes and promotional activitiesproposed by the e-pharmacy platform. Atimeseries-machine learning hybrid model that integrates price discount and coupons is proposed to better analyze sales improvement brought by promotional activities, based on which more accurate forecasting results can be obtained. Traditional demand forecasting research decomposes demand under promotional activities into a linear combination of baseline sales and promotional lifting sales, while the drug’s treatment cycleis considered in this model, and SARIMA model is used to predict the baseline sales.Finally,predicted baseline sales data and promotional features are put into XGBoost model for integrated learning to further analyze the promotional effects. Sales data from a Chinese leading e-pharmacy is used to test the model’s effectiveness, results indicate that this proposed hybrid model performs better compared to the other three widely used forecasting models. At the same time, the hybrid model’s efficiency under different price discount, as well as promotional information and data pooling strategy is verified.Results show that the hybrid model performs better when price discount varies,promotional information can sufficiently reduce the forecasting error by at least 40% when is added into the proposed hybrid model, while data pooling strategy can help the hybrid model reduce forecasting error by around 10%. The proposed hybrid model is confirmed to be applicable and useful, which sheds light on e-pharmacy’s demand forecasting with promotional activities.