A meta-analysis of international tourism demand forecasting and implications for practice

Abstract

：Numerous studies on tourism forecasting have now been published over the past five decades. However, no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980-2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modelling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context. The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings.

Forecasting tomorrow’s tourist

“…Such result contrasts with the literature review by Peng et al (2014), where only fourteen papers from the total of 2,584 were found for Africa. Table 9 exhibits the modeling techniques adopted for the fifty articles considered.…”

“…However, artificial intelligence techniques such as neural networks (used for thirteen times) and support vector machines (adopted six times) appear now as the second dominant method, as opposed to the review from Peng et al (2014); such finding reveals a shift in the most recent years (i.e., after 2013) as opposed to the period before 2011, analyzed the paper of Peng et al (2014), in which only around 17% adopted these advanced artificial intelligence techniques. Also, the percentage weight of the econometrics-based methods (e.g., Markov regime-switching model) has decreased, when compared to the cited study.…”

“…Two of them are particularly relevant to the present analysis, considering these identified also the methods used: the studies by Song & Li (2008), encompassing the years between 2000 and 2006; and by Peng et al (2014), which analyzed the 1980-2011 period. The former included also qualitative (i.e., non-data-based) approaches, and only 9.1% of the studies included adopted approaches linked to artificial intelligence techniques.…”

Modeling and Forecasting of International Tourism Demand in ASEAN Countries

“…In recent years, there has been a growing interest in analyzing and forecasting tourism demand (Zhou-Grundy and Turner, 2014;Witt and Witt, 1995;Claveria and Torra, 2014;Peng et al, 2014). Researchers have used different quantitative methods for forecasting tourism demand.…”

Common trends in international tourism demand: Are they useful to improve tourism predictions?

“…See Song, Dwyer, Li and Cao (2012) and Peng, Song, and Crouch (2014) for a thorough review of tourism economics research and tourism demand forecasting studies. Nevertheless, the need for more accurate forecasts has led to an increasing use of AI techniques, such as fuzzy time series models and support vector machines (SVMs), or a mix of them (Pai, Hung, & Lin 2014;Cang & Yu 2014), in order to obtain more refined predictions of tourist arrivals at the destination level.…”