STAD-GCN: Spatial–Temporal Attention-based Dynamic Graph Convolutional Network for retail market price prediction

Abstract

As technology advances, competition among market players intensifies, highlighting the importance of comprehending both one’s own and competitors’ pricing strategies. Traditional approaches often rely on static factors for price forecasting, disregarding the dynamic nature of market competition. In contrast to methods utilizing a static graph structure and uniform weights for predictions, we introduce STAD-GCN (Spatial–Temporal Attention-based Dynamic Graph Convolutional Network). This innovative model dynamically incorporates competitive factors into price prediction by employing attention mechanisms and graph convolution operations. Such an approach allows for the adaptation of graph structures and node relationships in response to temporal and spatial changes, offering a more nuanced understanding of market dynamics. To test STAD-GCN, we utilized international and domestic oil price data alongside specific gas station details from the South Korea National Oil Corporation, focusing on Seoul and Busan. The model presents remarkable performance, achieving mean absolute errors of 12.648 in Seoul and 11.242 in Busan, surpassing state-of-the-art models. Based on the findings of our research, we present some academic and practical implications, as well as several future research directions.