A multi-series framework for demand forecasts in E-commerce
This addresses the challenge of short sales time series in E-commerce, offering a solution for businesses to improve demand forecasting, though it appears incremental.
The paper tackled the problem of sales forecasting in E-commerce by proposing a global model using tree boosting methods and a preprocessing framework, which outperformed state-of-the-art models on real datasets.
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.