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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Dong Zhang & Pengkun Wu & Chong Wu & Eric W. T. Ngai, 2024. "Forecasting duty-free shopping demand with multisource data: a deep learning approach," Annals of Operations Research, Springer, vol. 339(1), pages 861-887, August.
  • Handle: RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-024-05830-y
    DOI: 10.1007/s10479-024-05830-y
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    Cited by:

    1. Ru-Xin Nie & Chuan Wu & He-Ming Liang, 2024. "Exploring Appropriate Search Engine Data for Interval Tourism Demand Forecasting Responding a Public Crisis in Macao: A Combined Bayesian Model," Sustainability, MDPI, vol. 16(16), pages 1-21, August.

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