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Daily tourism demand forecasting: the impact of complex seasonal patterns and holiday effects

Yunhao Liu, Gengzhong Feng, Kwai-Sang Chin, Shaolong Sun*, Shouyang Wang

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Prophet shows its ability to handle the influence of seasonal patterns and holiday effects. The forecasting results show that considering seasonal patterns, holiday effects, and other predictors can significantly improve the forecasting performance, and FB Prophet outperforms other methods.
Original languageEnglish
Pages (from-to)1573-1592
Number of pages20
JournalCurrent Issues in Tourism
Volume26
Issue number10
Online published14 Apr 2022
DOIs
Publication statusPublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

Research Keywords

  • Tourism demand forecasting
  • daily tourism demand
  • holiday effects
  • seasonal patterns
  • FB Prophet
  • EMPIRICAL MODE DECOMPOSITION
  • TIME-SERIES
  • NEURAL-NETWORK
  • OCCUPANCY
  • ACCURACY
  • TRAVEL

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