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Optimal Forecast Combination for Japanese Tourism Demand

Author

Listed:
  • Yongmei Fang

    (College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
    College of Economics and Management, South China Normal University, Guangzhou 510631, China)

  • Emmanuel Sirimal Silva

    (Glasgow School for Business and Society, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Bo Guan

    (Cardiff Business School, University of Cardiff, Cardiff CF10 3EU, UK)

  • Hossein Hassani

    (International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria)

  • Saeed Heravi

    (Cardiff Business School, University of Cardiff, Cardiff CF10 3EU, UK)

Abstract

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models.

Suggested Citation

  • Yongmei Fang & Emmanuel Sirimal Silva & Bo Guan & Hossein Hassani & Saeed Heravi, 2025. "Optimal Forecast Combination for Japanese Tourism Demand," Tourism and Hospitality, MDPI, vol. 6(2), pages 1-19, May.
    • Handle: RePEc:gam:jtourh:v:6:y:2025:i:2:p:79-:d:1650642
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    References listed on IDEAS

    as
    1. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    2. Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi, 2020. "Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 877-886, September.
    3. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    4. Lindsay W. Turner & N. Kulendran & H. Fernando, 1997. "Univariate Modelling Using Periodic and Non-Periodic Analysis: Inbound Tourism to Japan, Australia and New Zealand Compared," Tourism Economics, , vol. 3(1), pages 39-56, March.
    5. Li, Gang & Wu, Doris Chenguang & Zhou, Menglin & Liu, Anyu, 2019. "The combination of interval forecasts in tourism," Annals of Tourism Research, Elsevier, vol. 75(C), pages 363-378.
    6. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
    Full references (including those not matched with items on IDEAS)

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