Forecasting international tourism demand from the US, Japan and South Korea to Malaysia: A SARIMA approach

Borhan, Nurbaizura; Arsad, Zainudin

doi:10.1063/1.4887719

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…For instance, Petrevska (2017) tested several specifications of the ARIMA model to provide a short-run estimation of international tourism demand in Macedonia. Similarly, Borhan and Arsad (2014) adopted a SARIMA approach in forecasting international tourism demand to Malaysia from the USA, Japan and South Korea. Moreover, Rufino (2011) developed time series models using SARIMA on the top 12 tourist generating countries for Philippine tourism.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Abellana

Rivero

Aparente

et al. 2020

JTF

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Abellana

Rivero

Aparente

et al. 2020

JTF

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“…Inevitably, one of the greater focuses is on intercultural skills [4], which can be cultivated from international experiences. Many studies have been conducted on the forecasting and modeling financial markets, electricity consumption, and other related issues [5] by using various empirical approaches [6]. However, using forecasting methods to accurately predict outbound student mobility is groundbreaking not only in Taiwan but worldwide.…”

Section: Introductionmentioning

confidence: 99%

Forecasting outbound student mobility: A machine learning approach

Yang

Chen

et al. 2020

PLoS ONE

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Background A country's ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital. Methods To analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy. Results The proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature. Conclusion With the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum.

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“…In 1990, 2.6 million tourists visited Egypt which increased to more than 9 million visitors in 2006 (Zaki, 2008). In 2012, the Malaysian tourism industry contributed to 5.6 percent of the country's GDP (Borhan and Arsad, 2014).…”

Section: Introductionmentioning

confidence: 99%

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

Ghalehkhondabi

Ardjmand

Young

et al. 2019

JTF

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Purpose The purpose of this paper is to review the current literature in the field of tourism demand forecasting. Design/methodology/approach Published papers in the high quality journals are studied and categorized based their used forecasting method. Findings There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods. Originality/value This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

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Forecasting international tourism demand from the US, Japan and South Korea to Malaysia: A SARIMA approach

Cited by 7 publications

References 4 publications

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Forecasting outbound student mobility: A machine learning approach

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

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