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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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    1. Song, Haiyan & Wen, Long & Liu, Chang, 2019. "Density tourism demand forecasting revisited," Annals of Tourism Research, Elsevier, vol. 75(C), pages 379-392.
    2. 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.
    3. He, Kaijian & Tso, Geoffrey K.F. & Zou, Yingchao & Liu, Jia, 2018. "Crude oil risk forecasting: New evidence from multiscale analysis approach," Energy Economics, Elsevier, vol. 76(C), pages 574-583.
    4. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Anna Serena Vergori, 2017. "Patterns of seasonality and tourism demand forecasting," Tourism Economics, , vol. 23(5), pages 1011-1027, August.
    6. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    7. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    8. Bi, Jian-Wu & Li, Hui & Fan, Zhi-Ping, 2021. "Tourism demand forecasting with time series imaging: A deep learning model," Annals of Tourism Research, Elsevier, vol. 90(C).
    9. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    10. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    11. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
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    Cited by:

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    2. Wen Zhang & Xiaofeng Xu & Jun Wu & Kaijian He, 2023. "Preface to the Special Issue on “Computational and Mathematical Methods in Information Science and Engineering”," Mathematics, MDPI, vol. 11(14), pages 1-4, July.
    3. 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.
    4. Jessie Bravo & Roger Alarcón & Carlos Valdivia & Oscar Serquén, 2023. "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, MDPI, vol. 15(11), pages 1-25, June.

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