Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system

Haiyan Song, Zixuan Gao, Shanshan Lin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This paper introduces a web-based tourism demand forecasting system (TDFS) that is designed to forecast the demand for Hong Kong tourism, as measured by tourist arrivals, total and sectoral tourist expenditures, and the demand for hotel rooms. The TDFS process comprises three stages – preliminary data analysis, the generation of quantitative forecasts and judgmental adjustments – which correspond to the three key system components: the data module, the quantitative forecasting module and the judgmental forecasting module, respectively. These stages (modules) interact with one another. This paper focuses on a recent case study that illustrates the functional ability of the TDFS as a support system, providing accurate forecasts of the demand for Hong Kong tourism. Specifically, the quantitative forecasts are generated by the autoregressive distributed lag model, then adjusted by a panel of experts comprising postgraduate students and academic staff. The results show that this combination of quantitative and judgmental forecasts improves the overall forecasting accuracy.
Original languageEnglish
Pages (from-to)295-310
JournalInternational Journal of Forecasting
Volume29
DOIs
Publication statusPublished - 22 Mar 2012

Keywords

  • Tourism demand forecasting
  • Econometric model
  • Judgmental adjustment
  • Domain knowledge
  • Hong Kong

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