Tourism forecast combination using the stochastic frontier analysis technique

Wu, Ji; Cheng, Xian; Liao, Stephen Shaoyi

doi:10.1177/1354816619868089

Cited by 17 publications

(17 citation statements)

References 61 publications

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“…Nevertheless, the scope of literature and sample size was limited as most data were collected through self-reported surveys or interviews. As underscored by multiple researchers (Li and Wu, 2019; Wen et al , 2020; Wu et al , 2019), forecasting consumer behavior in hospitality and tourism is becoming the foundation of business decisions. Data mining techniques, in conjunction with machine learning algorithms, will act as a stepping stone for future studies forecasting traveler behavior patterns.…”

Section: Discussionmentioning

confidence: 99%

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

Lee

Kim

2020

IJCHM

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Purpose Using the decision tree model, this study aims to understand the online travelers booking behaviors on Expedia.com, by examining influential determinants of online hotel booking, especially for longer-stay travelers. The geographical distance is also considered in understanding the booking behaviors trisecting travel destinations (i.e. Americas, Europe and Asia). Design/methodology/approach The data were obtained from American Statistical Association DataFest and Expedia.com. Based on the US travelers who made hotel reservation on the website, the study used a machine learning algorithm, decision tree, to analyze the influential determinants on hotel booking considering the geographical distance between origin and destination. Findings The results of the findings demonstrate that the choice of package product is the prioritized determinant for longer-stay hotel guests. Several similarities and differences were found from the significant determinants of the decision tree, in accordance with the geographic distance among the Americas, Europe and Asia. Research limitations/implications This paper presents the extension to an existing machine learning environment, and especially to the decision tree model. The findings are anticipated to expand the understanding of online hotel booking and apprehend the influential determinants toward consumers’ decision-making process regarding the relationship between geographical distance and traveler’s hotel staying duration. Originality/value This research brings a meaningful understanding of the hospitality and tourism industry, especially to the realm of machine learning adapted to an online booking website. It provides a unique approach to comprehend and forecast consumer behavior with data mining.

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Section: Discussionmentioning

confidence: 99%

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

Lee

Kim

2020

IJCHM

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“…Wu & Blake, 2022). SA has been proved to be a robust, stable, and easy-to-use way, often outperforming more sophisticated combination methods, and hence is always used in combination forecasting studies (Makridakis & Winkler, 1983;Stock & Watson, 1998, 2004J. Wu et al, 2020).…”

Section: Candidate Forecasting Methodsmentioning

confidence: 99%

The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China

Blake

2023

Sage Open

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This paper applies a three-step framework to quantify COVID-19’s impacts on China’s domestic and international air travel demand and to evaluate how the impacts evolve from January 2020 to January 2022. Time series techniques and combination forecasting are tested to identify the best-performing model to generate baseline forecasts, with which actual demands are compared to assess the impact of COVID-19. The results demonstrate that during the period under study, China’s domestic aviation sector experienced two V-shape recoveries, while its international counterpart was devastated and showed no sign of revival. It suggests that to mitigate the impact of COVID-19, containing virus spread and removing mobility controls are essential; and when travel restrictions are lifted or loosened, governments play important roles in accelerating the rate of demand recovery.

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“…Linear combinations often require the sum of weights of individual models to be equal to one for simplicity. Wu et al (2019) used a stochastic frontier analysis technique to determine combination weights. To forecast visitor arrivals amid COVID-19, Qiu et al (2021) applied stacking models to combine forecasts from different models.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

2022

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Purpose Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set. Design/methodology/approach Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set. Findings The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM. Practical implications With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting. Originality/value To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

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Tourism forecast combination using the stochastic frontier analysis technique

Cited by 17 publications

References 61 publications

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance

The Impact of the COVID-19 Crisis on Air Travel Demand: Some Evidence From China

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

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