Comparative Analysis of Machine Learning Models for Accurate Retail Sales Demand Forecasting

Authors

  • Rasel Mahmud Jewel Department of Business Administration, Westcliff University, Irvine, California, USA
  • Ahmed Ali Linkon Department of Computer Science, Westcliff University, Irvine, California
  • Mujiba Shaima Department of Computer Science, Monroe College, New Rochelle, New York, USA
  • Badruddowza Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
  • Md Shohail Uddin Sarker Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
  • Rumana Shahid Department of Management Science and Quantitative Methods, Gannon University, USA
  • Norun Nabi Department of Information Technology, Washington University and Science and Technology, Alexandria, Virginia
  • Md Nasir Uddin Rana Department of Computer Science, Monroe College, New Rochelle, New York, USA
  • Md Ahnaf Shahriyar Department of Information Technology, Westcliff University, Irvine, California, USA
  • Mehedi Hasan Master of Science, Management- Business Analytics, St. Francis College, USA
  • Md Jubayar Hossain Master of Science, Management- Business Analytics, St. Francis College, USA

DOI:

https://doi.org/10.32996/jcsts.2024.6.1.23

Keywords:

Sales forecasting, LSTM, LGBM, RMSE, MAE, WMAPE, Retail sales data

Abstract

This article compares sales forecasting models, LSTM and LGBM, using retail sales data from an American multinational company. The study employs a meticulous methodology, optimizing memory, performing feature engineering, and adjusting model parameters for both LSTM and LGBM. Evaluation metrics, including RMSE, MAE, WMAPE, and WRMSEE, demonstrate that LGBM consistently outperforms LSTM in capturing and predicting sales patterns. The analysis favors LGBM as the preferred model for retail sales demand forecasting, emphasizing the importance of model selection. This study contributes to practical machine learning applications in retail sales forecasting, highlighting LGBM as an effective choice.

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Published

2024-02-27

Issue

Vol. 6 No. 1

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jewel, R. M., Linkon, A. A., Shaima, M., Badruddowza, Md Shohail Uddin Sarker, Rumana Shahid, Norun Nabi, Md Nasir Uddin Rana, Md Ahnaf Shahriyar, Mehedi Hasan, & Md Jubayar Hossain. (2024). Comparative Analysis of Machine Learning Models for Accurate Retail Sales Demand Forecasting. Journal of Computer Science and Technology Studies, 6(1), 204-210. https://doi.org/10.32996/jcsts.2024.6.1.23