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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rasel Mahmud Jewel & Ahmed Ali Linkon & Mujiba Shaima & Badruddowza & Md Shohail Uddin Sarker & Rumana Shahid & Norun Nabi & Md Nasir Uddin Rana & Md Ahnaf Shahriyar & Mehedi Hasan & Md Jubayar Hossai, 2024. "Comparative Analysis of Machine Learning Models for Accurate Retail Sales Demand Forecasting," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 6(1), pages 204-210, January.
    • Handle: RePEc:alk:jcsts0:v:6:y:2024:i:1:id:6877
    DOI: 10.32996/jcsts.2024.6.1.23
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