Architecture of Demand Forecast for Online Retailers in China Based on Big Data

Song, Luona; Lv, Tingjie; Chen, Xia; Gao, Junjun

doi:10.1007/978-3-319-31854-7_75

Cited by 1 publication

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“…ere are plenty of studies having been undertaken in sales forecasting. e methods of sales forecasts adopted in these studies can roughly be divided into time series models (TSMs) and machine learning algorithms (MLAs) [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Application of a Three‐Stage XGBoost‐Based Model to Sales Forecasting of a Cross‐Border E‐Commerce Enterprise

Wang

Zhao

et al. 2019

Mathematical Problems in Engineering

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Sales forecasting is even more vital for supply chain management in e-commerce with a huge amount of transaction data generated every minute. In order to enhance the logistics service experience of customers and optimize inventory management, e-commerce enterprises focus more on improving the accuracy of sales prediction with machine learning algorithms. In this study, a C-A-XGBoost forecasting model is proposed taking sales features of commodities and tendency of data series into account, based on the XGBoost model. A C-XGBoost model is first established to forecast for each cluster of the resulting clusters based on two-step clustering algorithm, incorporating sales features into the C-XGBoost model as influencing factors of forecasting. Secondly, an A-XGBoost model is used to forecast the tendency with the ARIMA model for the linear part and the XGBoost model for the nonlinear part. The final results are summed by assigning weights to forecasting results of the C-XGBoost and A-XGBoost models. By comparison with the ARIMA, XGBoost, C-XGBoost, and A-XGBoost models using data from Jollychic cross-border e-commerce platform, the C-A-XGBoost is proved to outperform than other four models.

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Section: Introductionmentioning

confidence: 99%