AI RESEARCH
Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting
arXiv CS.AI
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ArXi:2506.05941v2 Announce Type: replace-cross 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.