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

Dong Zhang, Pengkun Wu (), Chong Wu and Eric W. T. Ngai
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Dong Zhang: Sun Yat-Sen University
Pengkun Wu: Sichuan University
Chong Wu: Harbin Institute of Technology
Eric W. T. Ngai: The Hong Kong Polytechnic University

Annals of Operations Research, 2024, vol. 339, issue 1, No 32, 887 pages

Abstract: Abstract 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.

Keywords: Duty-free shopping; Demand forecasting; Deep learning; Cross-correlation; Time series; Multisource data (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-024-05830-y

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