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Forecasting Chinese outbound tourism recovery: A Triple-layer forecast combination framework

  • Hanyuan Zhang
  • , Ying Liu
  • , Xinyang Liu
  • , Anyu Liu
  • , Vera Shanshan Lin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Forecast combinations became particularly significant in the post-pandemic era due to heightened uncertainty. This study introduces a Triple-layer Forecast Combination Framework to predict Chinese outbound tourism recovery from August 2023 to July 2024 across 20 destinations. The framework integrates baseline quantitative models, expert-based model selection, and real-time judgmental adjustments to enhance forecast accuracy in post-crisis contexts. Results show Chinese visitor arrivals rebounding, on average, to 80% of July 2019 levels by mid-2024, with East and Southeast Asia—particularly Hong Kong SAR, Macao SAR, and Thailand—recovering faster than long-haul markets such as Hawaii, Canada, and the Czech Republic. By combining statistical rigor with contextual insight, the framework supports replicable, adaptive forecasting under uncertainty for tourism recovery planning.
Original languageEnglish
Article number104079
JournalAnnals of Tourism Research
Volume116
Early online date4 Dec 2025
DOIs
Publication statusPublished - 1 Jan 2026

Keywords

  • forecast combination
  • delphi method
  • judgmental adjustments
  • chinese outbound tourism
  • recovery pattern

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