Forecasting product demand in retail supply chains presents a complex challenge due to noisy, heterogeneous features and rapidly shifting consumer behavior. While traditional gradient boosting decision trees (GBDT) offer strong predictive performance on structured data, they often lack adaptive mechanisms to identify and emphasize the most relevant features under changing conditions. In this work, we propose AttnBoost, an interpretable learning framework that integrates feature-level attention into the boosting process to enhance both predictive accuracy and explainability. Specifically, the model dynamically adjusts feature importance during each boosting round via a lightweight attention mechanism, allowing it to focus on high-impact variables such as promotions, pricing, and seasonal trends. We evaluate AttnBoost on a large-scale retail sales dataset and demonstrate that it outperforms standard machine learning and deep tabular models, while also providing actionable insights for supply chain managers. An ablation study confirms the utility of the attention module in mitigating overfitting and improving interpretability. Our results suggest that attention-guided boosting represents a promising direction for interpretable and scalable AI in real-world forecasting applications.

翻译：零售供应链中的产品需求预测因存在噪声、异构特征以及快速变化的消费者行为而面临复杂挑战。传统梯度提升决策树（GBDT）虽然在结构化数据上表现出强大的预测性能，但通常缺乏自适应机制以识别和强调变化条件下最相关的特征。本研究提出AttnBoost，一种可解释的学习框架，它将特征级注意力集成到提升过程中，以同时提高预测准确性和可解释性。具体而言，该模型通过轻量级注意力机制在每一轮提升中动态调整特征重要性，使其能够聚焦于促销、定价和季节趋势等高影响力变量。我们在一个大规模零售销售数据集上评估AttnBoost，结果表明其性能优于标准机器学习及深度表格模型，同时能为供应链管理者提供可操作的洞察。消融研究证实了注意力模块在缓解过拟合和提升可解释性方面的有效性。我们的研究结果表明，注意力引导的提升方法为现实世界预测应用中可解释且可扩展的人工智能提供了一个有前景的方向。