Abstract
Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.
Highlights
The tourism industry is a growing economic sector in the globe
This paper proposes the use of a hybrid support vector regression (SVR)-seasonal autoregressive integrated moving averages (SARIMA) model in forecasting tourism demand due to its capability to handle the linear, nonlinear and seasonal components of the tourism demand data
This paper finds that in the case when the benchmark models outperform such non-hybrid models, their hybridization significantly enhances the forecasting accuracy and outperforms the accuracy of the benchmark models
Summary
The tourism industry is a growing economic sector in the globe. According to the World Travel and Tourism Council (WTTC), it has contributed to 10% of the global gross domestic product (GDP) (Ghalehkhondabi et al, 2019). 1 out of 11 jobs was relevant to tourism. With such a contribution to the globe, tourism is considered an important economic sector. Tourism demand forecasting is one of the most active research areas. The perishable nature of tourism services primarily motivates the need for generating forecasts; not selling it at the right time would lead to lost sales for all related businesses (Ghalehkhondabi et al, 2019). Scholars maintain that requirement of destination countries for substantial infrastructure investments and promotional activities, which aid in j j PAGE 78 JOURNAL OF TOURISM FUTURES VOL. 7 NO. 1 2021, pp. 78-97, Emerald Publishing Limited., ISSN 2055-5911
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