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
References
Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
Mozumder, M. A. S., Sweet, M. M. R., Nabi, N., Tusher, M. I., Modak, C., Hasan, M., ... & Prabha, M. (2024). Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management. Journal of Business and Management Studies, 6(3), 111-118.
Ferdus, M. Z., Anjum, N., Nguyen, T. N., Jisan, A. H., & Raju, M. A. H. (2024). The Influence of Social Media on Stock Market: A Transformer-Based Stock Price Forecasting with External Factors. Journal of Computer Science and Technology Studies, 6(1), 189-194
Bajari, P., Chernozhukov, V., Hortacsu, A., & Suzuki, J. (2019). The Impact of Big Data on Firm Performance. Journal of Applied Econometrics, 34(4), 725-746.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Breiman, L. (2017). Classification and regression trees. Routledge.
Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs, and LSTMs in Python. Machine Learning Mastery.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.
Chopra, S., & Meindl, P. (2016). Supply Chain Management: Strategy, Planning, and Operation. Pearson.
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., & Reis, J. (2021). Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, 47(4), 547-553.
Fildes, R., Ma, S., & Kolassa, S. (2019). Retail forecasting: Research and practice. International Journal of Forecasting, 35(2), 645-655.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
Hyndman, R. J., Bergmeir, C., Caceres, G., & O'Hara-Wild, M. (2021). Forecasting with Machine Learning. Journal of Business Research.
Keerthi, S. S., & Lin, C. J. (2020). Decision trees for retail demand forecasting: A case study. European Journal of Operational Research, 185(2), 789-802.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Livieris, I. E., Drakopoulou, K., & Kiriakidou, N. (2020). Demand forecasting using machine learning models: A case study. Expert Systems with Applications, 145, 113089.
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889.
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7, 21.
Farabi, S. F., Prabha, M., Alam, M., Hossan, M. Z., Arif, M., Islam, M. R., ... & Biswas, M. Z. A. (2024). Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation. Journal of Business and Management Studies, 6(3), 252-259.
Mozumder, M. A. S., Sweet, M. M. R., Nabi, N., Tusher, M. I., Modak, C., Hasan, M., ... & Prabha, M. (2024). Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management. Journal of Business and Management Studies, 6(3), 111-118.
Bhuiyan, M. S., Chowdhury, I. K., Haider, M., Jisan, A. H., Jewel, R. M., Shahid, R., ... & Siddiqua, C. U. (2024). Advancements in early detection of lung cancer in public health: a comprehensive study utilizing machine learning algorithms and predictive models. Journal of Computer Science and Technology Studies, 6(1), 113-121.
Nabi, N., Tusher, M. I., Modak, C., Hasan, M., ... & Prabha, M. (2024). Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management. Journal of Business and Management Studies, 6(3), 111-118.
Rahman, M. A., Modak, C., Mozumder, M. A. S., Miah, M. N. I., Hasan, M., Sweet, M. M. R., ... & Alam, M. (2024). Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness. Journal of Business and Management Studies, 6(3), 103-110.
Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. Journal of Computer Science and Technology Studies, 6(3), 35-41.
Arif, M., Hasan, M., Al Shiam, S. A., Ahmed, M. P., Tusher, M. I., Hossan, M. Z., ... & Imam, T. (2024). Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning. International Journal of Advanced Science Computing and Engineering, 6(2), 52-56.
Md Al-Imran, Salma Akter, Md Abu Sufian Mozumder, Rowsan Jahan Bhuiyan, Md Al Rafi, Md Shahriar Mahmud Bhuiyan, Gourab Nicholas Rodrigues, Md Nazmul Hossain Mir, Md Amit Hasan, Ashim Chandra Das, & Md. Emran Hossen. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22–33. https://doi.org/10.37547/tajet/Volume06Issue09-04
Md Abu Sufian Mozumder, Fuad Mahmud, Md Shujan Shak, Nasrin Sultana, Gourab Nicholas Rodrigues, Md Al Rafi, Md Zahidur Rahman Farazi, Md Razaul Karim, Md. Sayham Khan, & Md Shahriar Mahmud Bhuiyan. (2024). Optimizing Customer Segmentation in the Banking Sector: A Comparative Analysis of Machine Learning Algorithms. Journal of Computer Science and Technology Studies, 6(4), 01–07. https://doi.org/10.32996/jcsts.2024.6.4.1
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