  Sales forecast is an essential task in E-commerce and has a crucial impact on
making informed business decisions. It can help us to manage the workforce,
cash flow and resources such as optimizing the supply chain of manufacturers
etc. Sales forecast is a challenging problem in that sales is affected by many
factors including promotion activities, price changes, and user preferences
etc. Traditional sales forecast techniques mainly rely on historical sales data
to predict future sales and their accuracies are limited. Some more recent
learning-based methods capture more information in the model to improve the
forecast accuracy. However, these methods require case-by-case manual feature
engineering for specific commercial scenarios, which is usually a difficult,
time-consuming task and requires expert knowledge. To overcome the limitations
of existing methods, we propose a novel approach in this paper to learn
effective features automatically from the structured data using the
Convolutional Neural Network (CNN). When fed with raw log data, our approach
can automatically extract effective features from that and then forecast sales
using those extracted features. We test our method on a large real-world
dataset from CaiNiao.com and the experimental results validate the
effectiveness of our method.
