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Tourist Arrival Forecasting Using Multiscale Mode Learning Model

Author

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  • Kaijian He

    (College of Tourism, Hunan Normal University, Changsha 410081, China
    These authors contributed equally to this work.)

  • Don Wu

    (School of Tourism Management, Macao Institute of Tourism Studies, Macao, China
    Department of Marketing, City University of Hong Kong, Hong Kong, China
    These authors contributed equally to this work.)

  • Yingchao Zou

    (College of Tourism, Hunan Normal University, Changsha 410081, China
    These authors contributed equally to this work.)

Abstract

The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival forecasting model is proposed to exploit different multiscale data features in tourist arrival movement. Two popular Mode Decomposition models (MD) and the Convolutional Neural Network (CNN) model are introduced to model the multiscale data features in the tourist arrival data The data patterns at different scales are extracted using these two different MD models which dynamically decompose tourist arrival into the distinctive intrinsic mode function (IMF) data components. The convolutional neural network uses the deep network to further model the multiscale data structure of tourist arrivals, with the reduced dimensionality of key multiscale data features and finer modeling of nonlinearity in tourist arrival. Our empirical results using daily tourist arrival data show that the MD-CNN tourist arrival forecasting model significantly improves the forecasting reliability and accuracy.

Suggested Citation

  • Kaijian He & Don Wu & Yingchao Zou, 2022. "Tourist Arrival Forecasting Using Multiscale Mode Learning Model," Mathematics, MDPI, vol. 10(16), pages 1-12, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2999-:d:892673
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    References listed on IDEAS

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    Cited by:

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    2. Pawnrat Thumrongvut & Kanchana Sethanan & Thitipong Jamrus & Chuleeporn Wongloucha & Rapeepan Pitakaso & Paulina Golinska-Dawson, 2022. "Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
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    5. Ke Xu & Junli Zhang & Junhao Huang & Hongbo Tan & Xiuli Jing & Tianxiang Zheng, 2024. "Forecasting Visitor Arrivals at Tourist Attractions: A Time Series Framework with the N-BEATS for Sustainable Tourism," Sustainability, MDPI, vol. 16(18), pages 1-31, September.

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