# A multi-series framework for demand forecasts in E-commerce

arXiv: https://arxiv.org/abs/1905.13614 · HTML: https://mlsanity.com/paper/1905.13614/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 1 of 1)

- [Big Dataunicodex2013Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques](https://mlsanity.com/paper/2307.12971/) (2023)

## Abstract (arXiv)

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.