Articles
| Open Access |
DOI:
https://doi.org/10.37547/tajet/Volume06Issue09-09
OPTIMIZING RETAIL DEMAND FORECASTING: A PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS INCLUDING LSTM AND GRADIENT BOOSTING
Md Shujan Shak , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Shahin Alam Mozumder , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Amit Hasan , Master of Science in Information Technology, Washington University of Science and Technology, USA Ashim Chandra Das , Master of Science in Information Technology, Washington University of Science and Technology, USA Md Rashel Miah , Department of Digital Communication and Media/Multimedia, Westcliff University, USA Salma Akter , Department of Public Administration, Gannon University, Erie, PA, USA Md Nur Hossain , Master’s in information technology management, Webster University, USAAbstract
Effective demand forecasting is vital for inventory management in retail. This study evaluates five machine learning models—Linear Regression (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), Gradient Boosting (GB), and Long Short-Term Memory (LSTM)—for predicting retail demand. Utilizing a dataset with transactional sales, promotions, calendar events, and external factors like weather and economic indicators, we applied rigorous preprocessing and feature engineering. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). Results show that LSTM outperforms other models with an MAE of 9.53, RMSE of 14.67, and R² of 0.90, excelling in capturing temporal dependencies and complex demand patterns. Gradient Boosting and Random Forest also performed well, while Linear Regression and Decision Tree Regressor showed limitations. This study highlights the effectiveness of advanced models, particularly LSTM, for enhancing demand forecasting accuracy and offers valuable insights for optimizing retail inventory and operations.
Keywords
Retail Demand Forecasting, Machine Learning Models, Linear Regression
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Copyright (c) 2024 Md Shujan Shak, Md Shahin Alam Mozumder, Md Amit Hasan, Ashim Chandra Das, Md Rashel Miah, Salma Akter, Md Nur Hossain
This work is licensed under a Creative Commons Attribution 4.0 International License.