Accurate and fast demand forecast is one of the hot topics in supply chain
for enabling the precise execution of the corresponding downstream processes
(inbound and outbound planning, inventory placement, network planning, etc). We
develop three alternatives to tackle the problem of forecasting the customer
sales at day/store/item level using deep learning techniques and the
Corporación Favorita data set, published as part of a Kaggle competition. Our
empirical results show how good performance can be achieved by using a simple
sequence to sequence architecture with minimal data preprocessing effort.
Additionally, we describe a training trick for making the model more time
independent and hence improving generalization over time. The proposed solution
achieves a RMSLE of around 0.54, which is competitive with other more specific
solutions to the problem proposed in the Kaggle competition.

利用深度学习技术和 Corporación Favorita 数据集，我们提出三种替代方案来解决具体的需求预测问题，设计了一种简单的序列到序列的体系结构以及在此基础上的训练技巧，达到了约 0.54 的 RMSLE 的好性能。