A Deep Neural Framework for Sales Forecasting in E-Commerce

Product sales forecasting plays a fundamental role in enhancing timeliness of product delivery in E-Commerce. Among many heterogeneous features relevant to sales forecasting, promotion campaigns held in E-Commerce and competing relation between substitutable products would greatly complicate the matter. Unfortunately, these factors are usually overlooked in the existing literature, since the conventional time series analysis based techniques mainly consider the sales records alone. In this paper, we propose a novel deep neural framework for sales forecasting in E-Commerce, named DSF. In DSF, sales forecasting is formulated as a sequence-to-sequence learning problem where the sales is estimated in a recurrent fashion. On top of the decoder, we introduce a sales residual network to explicitly model the impact of competing relation when a promotion campaign is launched for a target item or some substitutable counterparts. Extensive experiments are conducted over two real-world datasets in different domains from Taobao E-Commerce platform. Our results demonstrate that the proposed DSF obtains substantial performance gain over the traditional baselines and up-to-date deep learning alternatives in terms of forecasting accuracy. Further comparison shows that DSF has also surpassed the deep learning based solution currently depolyed in Taobao platform.