Combination forecasting using multiple attribute decision making in tourism demand

Hu, Yi‐Chung

doi:10.1108/tr-09-2021-0451

Cited by 11 publications

(14 citation statements)

References 55 publications

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“…As a result, several academics have widely used grey prediction models in a variety of domains, including population structure, energy consumption and demand, and Internet of things technology [49][50][51][52]. Because of these qualities, grey prediction models have been used in tourism, including tourism demand [53,54], annual foreign tourist arrivals [55], and tourism volumes [56]. Te grey prediction models have outperformed in tourism prediction.…”

Section: Grey Prediction In Tourism Predictionmentioning

confidence: 99%

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Lee

Kong

2023

Journal of Mathematics

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The tourism industry has become a major contributor to the economic growth. However, because of the outbreak of the coronavirus disease (COVID-19) pandemic, the year 2020 became an extremely difficult year for the global tourism industry. Since the development of the tourism industry depends largely on changes in travel sentiment, it is important to analyze these changes in light of the pandemic. To determine the development trends of travel sentiment, a hybrid grey prediction model was used to predict travel sentiment globally and in the top 10 destination countries considering the shock effect of COVID-19 and vaccination. The results showed that the grey prediction models integrated with residual modification model contributed to improving the prediction accuracy. In addition, COVID-19 and vaccination were found to have opposite effects on travel sentiment. Based on the predictions, governments should strengthen pandemic prevention and control and administer vaccines to restore travel sentiment and promote tourism recovery.

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Section: Grey Prediction In Tourism Predictionmentioning

confidence: 99%

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Lee

Kong

2023

Journal of Mathematics

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“…Next, commonly used nonlinear combination methods are multilayer perceptron (MLP), SVR and radial basis function network (RBFN). Recently, the fuzzy integral is also used to combine forecasts nonlinearly (Hu et al , 2021; Hu, 2022). Nonlinear combination methods generally outperform linear combinations in modeling economic behavior (Cang, 2011, 2014; Claveria and Torra, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

“…). Recently, the fuzzy integral is also used to combine forecasts nonlinearly (Hu et al, 2021;Hu, 2022). Nonlinear combination methods generally outperform linear combinations in modeling economic behavior (Cang, 2011(Cang, , 2014Claveria and Torra, 2014).…”

Section: Forecast Combinationmentioning

confidence: 99%

“…As for the second issue, empirical results have shown that no single model can come out ahead of all other single models on all occasions in tourism (Song and Li, 2008;Song et al, 2019;Wu et al, 2017). Quantitative evidence has also shown that combined forecasts generated by single models and a certain weighting scheme are, on average, superior to single-model forecasts (Cang, 2014;Hu, 2022;Jiao and Chen, 2018;Qiu et al, 2021;Wu et al, 2017). Empirical results have suggested that encompassing tests may be considered when combining single-model forecasts in tourism forecasting because of their advantage of eliminating uninformative rival models among the available single models (Gunter and O ¨nder, 2016;Shen et al, 2008Shen et al, , 2011.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Examples include the combination of long- and short-term forecasts proposed by Andrawis et al (2011), the combination of neural networks (NNs) and support vector regression by Cang (2014), combined econometric models developed by Shen et al (2011) and the non-additive forecast combination by Hu et al (2021). Evidence shows that no single model can outperform other models on all occasions in this context (Hu, 2022; Song and Li, 2008; Wu et al , 2017). Using statistical analysis, Song et al (2009) have strongly recommended combined forecasts for tourism.…”

Section: Introductionmentioning

confidence: 99%

Constructing interval models using neural networks with non-additive combinations of grey prediction models in tourism demand

Jiang

2022

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PurposeIn contrast to point forecasts, interval forecasts provide information on future variability. This research thus aimed to develop interval prediction models by addressing two significant issues: (1) a simple average with an additive property is commonly used to derive combined forecasts, but this unreasonably ignores the interaction among sequences used as sources of information, and (2) the time series often does not conform to any statistical assumptions.Design/methodology/approachTo develop an interval prediction model, the fuzzy integral was applied to nonlinearly combine forecasts generated by a set of grey prediction models, and a sequence including the combined forecasts was then used to construct a neural network. All required parameters relevant to the construction of an interval model were optimally determined by the genetic algorithm.FindingsThe empirical results for tourism demand showed that the proposed non-additive interval model outperformed the other interval prediction models considered.Practical implicationsThe private and public sectors in economies with high tourism dependency can benefit from the proposed model by using the forecasts to help them formulate tourism strategies.Originality/valueIn light of the usefulness of combined point forecasts and interval model forecasting, this research contributed to the development of non-additive interval prediction models on the basis of combined forecasts generated by grey prediction models.

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Combination forecasting using multiple attribute decision making in tourism demand

Cited by 11 publications

References 55 publications

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

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

Constructing interval models using neural networks with non-additive combinations of grey prediction models in tourism demand

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