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Forecasting tourist arrivals using denoising and potential factors

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  • Li, Cheng
  • Ge, Peng
  • Liu, Zhusheng
  • Zheng, Weimin

Abstract

Precise tourist demand forecasting is crucial owing to its relevance in tourism decision-making. This study proposes a novel model for tourist demand forecasting on the basis of denoising and potential factors. The denoising strategy is proposed to improve the tourist demand forecasting, and an effective and promising denoising method based on Hilbert–Huang transform is developed. Two case studies are conducted to verify the validity and predictability of the proposed model. Results indicate that denoising remarkably improves the forecasting accuracy, and the proposed denoising technique outperforms other approaches. Furthermore, the proposed model exhibits the most satisfactory forecasting performance among all benchmark models, as well as excellent scalability and stability.

Suggested Citation

  • Li, Cheng & Ge, Peng & Liu, Zhusheng & Zheng, Weimin, 2020. "Forecasting tourist arrivals using denoising and potential factors," Annals of Tourism Research, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300876
    DOI: 10.1016/j.annals.2020.102943
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    1. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.
    3. Martins, Luís Filipe & Gan, Yi & Ferreira-Lopes, Alexandra, 2017. "An empirical analysis of the influence of macroeconomic determinants on World tourism demand," Tourism Management, Elsevier, vol. 61(C), pages 248-260.
    4. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    5. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    6. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    7. Chan, Chi Kin & Witt, Stephen F. & Lee, Y.C.E. & Song, H., 2010. "Tourism forecast combination using the CUSUM technique," Tourism Management, Elsevier, vol. 31(6), pages 891-897.
    8. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    9. Marie Poprawe, 2015. "A panel data analysis of the effect of corruption on tourism," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2399-2412, May.
    10. Wan, Shui Ki & Song, Haiyan, 2018. "Forecasting turning points in tourism growth," Annals of Tourism Research, Elsevier, vol. 72(C), pages 156-167.
    11. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    12. Coshall, John T. & Charlesworth, Richard, 2011. "A management orientated approach to combination forecasting of tourism demand," Tourism Management, Elsevier, vol. 32(4), pages 759-769.
    13. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    14. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    15. Christine Lim & Michael McAleer, 2001. "Cointegration analysis of quarterly tourism demand by Hong Kong and Singapore for Australia," Applied Economics, Taylor & Francis Journals, vol. 33(12), pages 1599-1619.
    16. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    17. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    18. Chen, Kuan-Yu, 2007. "Forecasting systems reliability based on support vector regression with genetic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 92(4), pages 423-432.
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    7. Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.
    8. Li, Hengyun & Gao, Huicai & Song, Haiyan, 2023. "Tourism forecasting with granular sentiment analysis," Annals of Tourism Research, Elsevier, vol. 103(C).
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