Tourism forecasting: A review of methodological developments over the last decade

Abstract

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

New tourism product forecasting – application of Bass Diffusion Model and Grey Forecasting Model

“…Most of the literature of tourism demand concentrate on the tourist arrivals into the country or region. This is supported by the review studies from Song and Li [4] and Jiao and Chen [5] where most studies involving tourism demand forecasting use tourist arrivals as dependent variable even though a decade has passed. For example, Hamzah et al [6] forecasted tourism demand of Malaysia using Box-Jenkins approach using monthly data from 1998 until 2017.…”

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

“…Selection of appropriate key words is an important step in augmenting search data into forecasting models (Jiao & Chen, 2019;Park et al, 2017). We selected around 10 search terms that may explain the travel decisions to visit Sri Lanka and checked the Google trends for each search term.…”

“…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).…”

“…Accurate tourism forecasts are key inputs for effective government policy planning to enable tourism development and economic growth (Jiao & Chen, 2019;Li, 2009). Number of tourist arrivals are the most commonly used indicator to measure overall changes in tourism demand (Wu et al, 2017).…”

“…Potential tourists widely use only search engines to get information on potential destinations, cost of travel, accommodation, attractions, etc. (Jiao & Chen, 2019).…”