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A web-based Hong Kong tourism demand forecasting system

Author

Listed:
  • Haiyan Song
  • Zixuan Gao
  • Xinyan Zhang
  • Shanshan Lin

Abstract

Accurate predictions of future business activities are important for business decision-making. As a consequence, powerful and simple forecasting processes are urgently pursued by decision-makers. This study presents a tourism demand forecasting system for Hong Kong based on the web techniques to help relevant stakeholders make better decisions within the tourism industry. The system generates the forecasts of tourist arrivals, tourist expenditure, demand for hotel rooms, sectoral demand and outbound tourist flows. The autoregressive distributed lag (ADL) model is employed by this web-based forecasting system. ADL model relates a set of influencing factors to the demand for tourism, and generates both statistical as well as scenario forecasts of tourism demand in Hong Kong. In addition, the system also allows users' adjustments to the statistical forecasts.

Suggested Citation

  • Haiyan Song & Zixuan Gao & Xinyan Zhang & Shanshan Lin, 2012. "A web-based Hong Kong tourism demand forecasting system," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 10(3/4), pages 275-291.
    • Handle: RePEc:ids:ijnvor:v:10:y:2012:i:3/4:p:275-291
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    Cited by:

    1. Buda Baji'c & Sr{dj}an Mili'cevi'c & Aleksandar Anti'c & Slobodan Markovi'c & Nemanja Tomi'c, 2024. "Neural Network Modeling for Forecasting Tourism Demand in Stopi\'{c}a Cave: A Serbian Cave Tourism Study," Papers 2404.04974, arXiv.org.
    2. Aurora Skrame & Claudio Ciancio & Vincenzo Corvello & Roberto Musmanno, 2020. "A Quantitative Model Supporting Socially Responsible Public Investment Decisions for Sustainable Tourism," IJFS, MDPI, vol. 8(2), pages 1-9, June.

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