A cognitively inspired feature-level fusion framework for interpretable retail sales forecasting using integration of...

Accurate retail sales forecasting in modern data-intensive environments requires models that not only achieve high predictive accuracy but also scale efficiently as data volume and feature complexity increase. This study proposes a novel cognitively inspired hybrid framework, XGB-ANN-Attn, for interpretable and scalable retail analytics.

The model introduces feature-level fusion by integrating XGBoost-derived leaf embeddings with deep neural representations, enabling joint modeling of low-order statistical dependencies and high-order nonlinear feature interactions. A lightweight attention mechanism dynamically assigns instance-specific feature importance, enhancing both predictive performance and interpretability while maintaining linear computational complexity with respect to feature dimensionality. Unlike conventional ensemble approaches that operate at the decision level, the proposed framework integrates representations at the level of representations, reducing redundancy and improving computational efficiency. The model is designed for scalability, combining the log-linear complexity of gradient boosting with the linear scaling properties of neural networks and attention mechanisms.

Experimental evaluation on the BigMart and Walmart datasets demonstrates superior performance compared to state-of-the-art models, achieving RMSE = 0.1584 and R ² = 0.9946 on BigMart, and RMSE = 0.8652 and R ² = 0.9568 on Walmart. Furthermore, the framework supports parallelization and distributed deployment, making it suitable for large-scale retail systems.

The alignment between attention weights and SHAP explanations provides transparent and actionable insights. The results confirm that the proposed approach offers a scalable, interpretable, and high-performance solution for AI-driven decision support systems in big-data retail environments.