Forecasting duty-free shopping demand with multisource data: a deep learning approach

• Published in: Annals of operations research 2024-08, Vol.339 (1-2), p.861-887
• Main Authors: Zhang, Dong, Wu, Pengkun, Wu, Chong, Ngai, Eric W. T.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Business and Management; Combinatorics; Cross correlation; Decision making; Deep learning; Economic factors; Forecasting; Machine learning; Operations management; Operations Research/Decision Theory; Original Research; Shopping; State-of-the-art reviews; Theory of Computation
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-024-05830-y

Accurate forecasting of duty-free shopping demand plays a pivotal role in strategic and operational decision-making processes. Despite the extensive literature on sustainability, operations management, and consumer behavior in the context of duty-free shopping, there is a noticeable absence of an integrated end-to-end solution for precise demand forecasting. Furthermore, existing forecasting models often encounter limitations in effectively leveraging multi-source data as reliable indicators for duty-free shopping demand. To address these gaps, our study introduces a pioneering deep-learning architecture known as the Attention-Aided Interaction-Driven Long Short-Term Memory-Convolutional Neural Network Model (AI-LCM). Designed to capture intricate cross-correlations within multi-source data, encompassing search queries, COVID-19 impact, economic factors, and historical data; this model represents a significant methodological advancement. Rigorous evaluation against state-of-the-art benchmarks conducted on robust real-world datasets confirms the superior forecasting performance exhibited by our AI-LCM model. We elucidate the manifold implications for various stakeholders while illustrating the extensive applicability of our model and its potential to inform data-driven decision-making strategies.