A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka

“…Tourism literature clearly identifies that there is no single forecasting method that performs best in all situations (Ghalehkhondabi et al, 2019;Jiao & Chen, 2019;Khaidi et al, 2019;Song et al, 2019;Song & Li, 2008;C. A. Witt & Witt, 1995).…”

“…The model is also widely used as a benchmark for evaluation and comparison purposes (Jiao & Chen, 2019). A review conducted by H. Song et al (2019) finds that SARIMA models have received substantial attention in tourism forecasting studies in recent years. For Sri Lanka, Thushara et al (2019) find that SARIMA models outperform the ARIMA models in forecasting arrivals from the top 10 source countries.…”

“…These real-time data are generally of high frequency, available free of charge and are sensitive to tourist behaviour (Wu et al, 2017). There is much potential for using search query data and web traffic data in tourism forecasting (Wu et al, 2017), though this approach is still in its infancy (Song et al, 2019). The current paper aims to make extensions to ARIMA and SARIMA models by employing Google search data and develop ARIMAX and SARIMAX models for forecasting.…”

“…The disaggregation is done considering the geographical distribution of origins and major source countries for international tourism. There is limited literature that uses disaggregated data on international tourism (Song et al, 2019;Song & Li, 2008). Turner and Witt (2001) forecast international tourist arrivals to Australia from selected countries including New Zealand, Japan, the UK and the USA.…”

A decomposition-ensemble approach for tourism forecasting

“…The time series of tourist arrivals can be influenced by various underlying factors, such as irregular events, economic crises (Song, Qiu, & Park, 2019) and seasonal variations (Goh & Law, 2002;Hyndman & Athanasopoulos, 2018). For example, the severe acute respiratory syndrome (SARS) epidemic in 2003, and the financial crisis of 2008, both caused structural breaks in the time series of tourist arrivals in many destinations (Cró & Martins, 2017;Narayan, 2005).…”