Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21621
Title: Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system
Authors: Song, H 
Gao, BZ
Lin, VS
Keywords: Domain knowledge
Econometric model
Hong Kong
Judgmental adjustment
Tourism demand forecasting
Issue Date: 2012
Publisher: Elsevier
Source: International journal of forecasting, 2012 How to cite?
Journal: International journal of forecasting 
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.
URI: http://hdl.handle.net/10397/21621
ISSN: 0169-2070
DOI: 10.1016/j.ijforecast.2011.12.003
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