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

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.