A Graph Attention Enhanced Multi-Scale Transfer Learning Method for Few-Shot Sales Prediction in Supply Chain

For the challenge of few-shot sales prediction in supply chain applications, traditional methods often struggle with data scarcity and fail to effectively leverage related information across multiple domains. In response, this study introduces a novel Graph Attention Enhanced Multi-Scale Transfer Learning (GAT-MSTL) method to enhance predictive accuracy in small-sample sales forecasting. GAT-MSTL constructs temporal similarity graphs using Graph Attention Networks (GAT) to dynamically capture cross-domain temporal dependencies and performs multi-scale source domain pre-training to facilitate effective knowledge transfer. Our experimental validation on the Corporación Favorita Grocery Sales Forecasting dataset demonstrates that GAT-MSTL outperforms various existing methods, delivering superior average forecasting, highlighting its effectiveness in addressing data scarcity problem in supply chain sales forecasting.