<ul><li>Accurate forecasting is crucial for business planning, especially in the retail sector where overestimating or underestimating sales can lead to various costs and issues.</li><li>A study conducted on a high-resolution brick-and-mortar retail dataset compared tree-based ensembles (like XGBoost and LightGBM) with neural network architectures (such as N-BEATS, NHITS, and Temporal Fusion Transformer) for sales forecasting.</li><li>The results indicated that localized modeling strategies using tree-based models on non-imputed data provided better forecasting accuracy and computational efficiency in retail environments.</li><li>Neural network models benefited from advanced imputation methods but struggled with the irregularities present in physical retail data, emphasizing the importance of data preprocessing for improved forecasting performance.</li></ul>

Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting

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