Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products

Chuqi Qiu

doi:10.47297/taposatWSP2633-456911.20250608

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Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products

Volume 6, Issue 8

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Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products
Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products
School of Management, Hangzhou Dianzi University, Hangzhou, Zhejiang, 31001, China
Vol. 6, Issue 8, Pages: 58-65(2025)
Published：30 August 2025
DOI：10.47297/taposatWSP2633-456911.20250608
Accepted：

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Qiu Chuqi.Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products[J].Theory and Practice of Science and Technology,2025,06(08):58-65.
DOI：

Qiu Chuqi.Research on E-Commerce Product Inventory Monitoring and Sales Forecasting Based on the Spark Platform: A Case Study of Furniture Products[J].Theory and Practice of Science and Technology,2025,06(08):58-65. DOI： 10.47297/taposatWSP2633-456911.20250608.

摘要

Abstract

This study presents a forecasting and inventory management framework for e-commerce

integrating Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) within a distributed computing environment using Apache Spark. The approach is tested on the UK Online Retail dataset for a specific furniture SKU. Key contributions include integrating deep learning and ensemble learning

implementing a scalable Spark-based pipeline

and linking forecasts directly to inventory replenishment strategies. Experimental results show complementary strengths: LSTM excels at capturing long-term trends

while XGBoost responds quickly to short-term fluctuations. The framework demonstrates practical potential for forecast-driven inventory optimization.

关键词

Keywords

references

Bojer, C. S., & Meldgaard, J. P. (2021). Kaggle forecasting competitions: An overlooked learning opportunity. International Journal of Forecasting, 37(2), 587–603.

Wang, M., Liu, Y., Li, G., et al. (2024). Unlocking your sales insights: Advanced XGBoost forecasting models for Amazon products. arXiv preprint arXiv: 2411.00460 .

Bandara, K., Shi, P., Bergmeir, C., et al. (2019). Sales demand forecast in e-commerce using a long short-term memory neural network methodology. In International Conference on Neural Information Processing (pp. 462–474). Cham: Springer.

Salinas, D., Flunkert, V., & Gasthaus, J. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191.

Mukherjee, S., Shankar, D., Ghosh, A., et al. (2018). AR-MDN: Associative and recurrent mixture density networks for eRetail demand forecasting. Proceedings of the VLDB Endowment, 11(5), 553–565.

Han, Y. J., & Gao, X. (2022). E-commerce product sales forecasting based on a combined machine learning model. Computer Systems Applications, 31(1), 315–321.

Hu, B. W., & Li, J. (2021). E-commerce product sales forecasting based on multi-layer LSTM. Computer Science and Applications, 11(12), 3081–3090.

Hu, Y. Y., Zhang, C., & Zheng, M. (2020). Design and implementation of a sales forecasting system based on LSTM. Computer and Network, 46(18), 45–49.

Jiang, W. W. (2019). Research on commodity sales forecasting based on WaveNet-LSTM network (Master's thesis). Guangdong University of Technology.

Li, Z. H., & Zhang, K. L. (2020). Comparative study of automobile sales forecasting models based on BP algorithm and LSTM algorithm. Economic Research Guide, (20), 84–88.

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