Open AccessTime series forecast of sales volume based on XGBoost
Author(s) -
Lingyu Zhang,
Wenjie Bian,
Wenyi Qu,
Liheng Tuo,
Yunhai Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012067
Subject(s) - staffing , volume (thermodynamics) , idle , series (stratigraphy) , feature (linguistics) , feature engineering , computer science , retail sales , time series , distribution (mathematics) , state (computer science) , operations research , industrial engineering , business , marketing , engineering , economics , machine learning , mathematics , algorithm , quantum mechanics , biology , operating system , mathematical analysis , paleontology , linguistics , philosophy , physics , management , deep learning
Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models.