Bayesian bootstrap aggregation for tourism demand forecasting

Song, Haiyan; Liu, Anyu; Li, Gang; Liu, Xinyang

doi:10.1002/jtr.2453

Cited by 21 publications

(13 citation statements)

References 43 publications

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“…Jiao et al (2020) forecasted annual tourism demand in European countries using spatial econometrics. Song et al (2021) applied Bayesian bootstrap aggregation (BBagging) into tourism demand forecasts, tried to off a solution for limited historical data in this field. Bi et al (2020) applied long short‐term memory networks with a time series model to forecast daily tourism volume, which proved the effectiveness of this model and search engine data.…”

Section: Literature Reviewmentioning

confidence: 99%

Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach

Xing

Sun

et al. 2022

Journal of Tourism Research

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In this study, we propose an adaptive multiscale ensemble (AME) learning approach, which consists of variational mode decomposition (VMD) and least square support vector regression (LSSVR) for seasonal and trend forecasting of tourist arrivals. In the formulation of AME learning approach, the original tourist arrival series is decomposed into the trend, seasonal, and remaining volatility components. Then, ARIMA is used to forecast the trend component, SARIMA is used to forecast the seasonal component, and LSSVR is used to forecast the remaining volatility components. The empirical results demonstrate that our proposed AME learning approach can achieve higher forecasting accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach

Xing

Sun

et al. 2022

Journal of Tourism Research

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“…However, while mode decomposition can extract several modes from the original sequence (Li et al, 2022), it is highly sensitive to sample size. Song et al (2021)…”

Section: Tourism Forecastingmentioning

confidence: 99%

“…However, while mode decomposition can extract several modes from the original sequence (Li et al , 2022), it is highly sensitive to sample size. Song et al (2021) also tried to solve the problem arising from limited historical data by applied Bayesian bootstrap aggregation into tourism demand forecasts.…”

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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“…Final predictions are calculated by averaging all generated predictions. This is useful for decision tree and arti cial neural network techniques that are sensitive to small changes in training data [23].…”

Section: Vamentioning

confidence: 99%

Computational Intelligence to Study the Importance of Predictors in White Oat ( <i>Avena Sativa</i> L.)

Júnior¹,

Sant’Anna²,

Silva³

et al. 2021

SSRN Journal

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Bayesian bootstrap aggregation for tourism demand forecasting

Cited by 21 publications

References 43 publications

Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach

Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach

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

Computational Intelligence to Study the Importance of Predictors in White Oat ( <i>Avena Sativa</i> L.)

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