@inproceedings{55058b21c21f4190a92a9087e834114e,
title = "Unlocking Your Sales Insights: Advanced XGBoost Forecasting Models for Amazon Products",
abstract = "One of the important factors of profitability is the volume of transactions. An accurate prediction of the future transaction volume becomes a pivotal factor in shaping corporate operations and decision-making processes. E-commerce has presented manufacturers with convenient sales channels to, with which the sales can increase dramatically. In this study, we introduce a solution that leverages the XGBoost model to tackle the challenge of predicting sales for consumer electronics products on the Amazon platform. Initially, our attempts to solely predict sales volume yielded unsatisfactory results. However, by replacing the sales volume data with sales range values, we achieved satisfactory accuracy with our model. Furthermore, our results indicate that XGBoost exhibits superior predictive performance compared to traditional models.",
keywords = "Amazon, CatBoost, Consumer electronics, E-commerce first section, Ensemble learning, GBDT, Sales forecasting, XGBoost",
author = "Meng Wang and Yuchen Liu and Gangmin Li and Pyane, \{Terry R.\} and Yong Yue and Man, \{Ka Lok\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 18th International Conference on Multimedia and Ubiquitous Engineering, MUE 2024 and 19th International Conference on Future Information Technology, Future Tech 2024 ; Conference date: 24-04-2024 Through 26-04-2024",
year = "2026",
doi = "10.1007/978-981-95-1565-3\_23",
language = "English",
isbn = "9789819515646",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "181--187",
editor = "Park, \{Ji Su\} and Yang, \{Laurence T.\} and Yi Pan and Park, \{James J.\}",
booktitle = "Advanced Multimedia and Ubiquitous Engineering - Proceedings of MUE-FutureTech 2024",
}