The combination of interval forecasts in tourism

Li, Gang; Wu, Doris Chenguang; Zhou, Menglin; Liu, Anyu

doi:10.1016/j.annals.2019.01.010

Cited by 63 publications

(67 citation statements)

References 40 publications

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“…To this end, interval forecasting is desirable because it predicts a likely range of future outcomes, thus allowing for contingency planning (Christoffersen, 1998). Interval forecasting has been applied in tourism and hospitality studies, such as those by Athanasopoulos et al (2011), Kim et al (2010), Kim et al (2011), and Li et al (2019). Interval forecasts provide a range of outcomes instead of a single point of forecasting for future tourism demand at a preset confidence level/probability (Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Scenario Forecasting for Global Tourism

Wu¹,

Cao

Wen

et al. 2020

Journal of Hospitality & Tourism Research

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This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting.

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Section: Introductionmentioning

confidence: 99%

Scenario Forecasting for Global Tourism

Wu¹,

Cao

Wen

et al. 2020

Journal of Hospitality & Tourism Research

Self Cite

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“…As a consequence, the interval provides more useful information for tourism practitioners and policymakers to formulate relevant strategies and policies (Wu, Song, & Shen, 2017). Referring the research in Li et al (2019), this study produces both point and interval forecasts for tourist arrivals. In point forecasting, the training data sets are the first 80% (175 observations) of the tourist arrival time series from each source market (covering the period from January 2001, to July 2015), while the testing data sets are the latter 20% (44 observations, covering the period from August 2015, to March 2019).…”

Section: Data and Experiments Designmentioning

confidence: 98%

“…Then, interval forecasts are generated, with point forecasts as the means. For more information about interval forecasting, please refer to Li, Wu, Zhou, and Liu (2019). G. Xie, et al Annals of Tourism Research 81 (2020) 102891 Complete ensemble empirical mode decomposition with adaptive noise…”

Section: Procedures Of the Proposed Approachmentioning

confidence: 99%

“…Following the predictive accuracy measurement in Li et al (2019), we evaluate the models in both point and interval forecasts. In point forecasting, mean absolute percentage error (MAPE) is used as the evaluation criteria: 17) where N is the number of observations in the testing set.…”

Section: Data and Experiments Designmentioning

confidence: 99%

“…Just like the research in Li et al (2019), interval forecasting accuracy is measured based on the natural logarithm form of the tourist arrival data. Similarly, the performance of interval forecasting is better when the Winkler score is lower.…”

Section: Data and Experiments Designmentioning

confidence: 99%

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A decomposition-ensemble approach for tourism forecasting

Xie

Qian

Wang

2020

Annals of Tourism Research

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A B S T R A C TWith the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.

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Bayesian bootstrap aggregation for tourism demand forecasting

Song

Liu

et al. 2021

Journal of Tourism Research

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Limited historical data are the primary cause of the failure of tourism forecasts. Bayesian bootstrap aggregation (BBagging) may offer a solution to this problem. This study is the first to apply BBagging to tourism demand forecasting. An analysis of annual and quarterly tourism demand for Hong Kong shows that BBagging can, in general, improve the forecasting accuracy of the econometric models obtained using the general‐to‐specific (GETS) approach by reducing, relative to the ordinary bagging method, the variability in the posterior distributions of the forecasts it generates.

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The combination of interval forecasts in tourism

Cited by 63 publications

References 40 publications

Scenario Forecasting for Global Tourism

Scenario Forecasting for Global Tourism

A decomposition-ensemble approach for tourism forecasting

Bayesian bootstrap aggregation for tourism demand forecasting

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