Data source combination for tourism demand forecasting

Mingming Hu · Haiyan Song

63 citations0/0 files storedTourism Economics

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Forecasting Techniques and Applications Diverse Aspects of Tourism Research Information Retrieval and Search Behavior

Abstract

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

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References

1971Time Series Analysis Forecasting and Control 2007Tourism demand modelling and forecasting—A review of recent research 1996Testing for the 'Existence of a Long-run Relationship'2019A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting 2005Recent Developments in Econometric Modeling and Forecasting 2019Tourism demand forecasting: A deep learning approach 2014Forecasting Chinese tourist volume with search engine data 2002Impact of Climate on Tourist Demand

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