  Sales forecasts are crucial for the E-commerce business. State-of-the-art
techniques typically apply only univariate methods to make prediction for each
series independently. However, due to the short nature of sales times series in
E-commerce, univariate methods don't apply well. In this article, we propose a
global model which outperforms state-of-the-art models on real dataset. It is
achieved by using Tree Boosting Methods that exploit non-linearity and
cross-series information. We also proposed a preprocessing framework to
overcome the inherent difficulties in the E-commerce data. In particular, we
use different schemes to limit the impact of the volatility of the data.
