Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting

Accurate demand forecasting is critical for brick-and-mortar retailers to
optimize inventory management and minimize costs. This study evaluates
statistical baselines, tree-based ensembles (XGBoost and LightGBM), and deep
learning architectures (N-BEATS, N-HiTS, and the Temporal Fusion Transformer)
on retail sales data characterized by intermittent demand, substantial
missingness, and frequent product turnover. Models are compared across four
configurations varying by aggregation level and imputation strategy, using
evaluation protocols that reflect typical deployment patterns for each model
class. Localized tree-based methods achieve superior performance, with XGBoost
attaining the lowest RMSE of 4.833. While SAITS-based imputation improved
neural network performance in aggregated settings, these models remained
inferior to ensemble methods. The results suggest that, under the studied
constraints, model selection should prioritize alignment with problem
characteristics over architectural sophistication.