Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals

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The Tourism Forecasting Competition (2011, background): “and two scaled error measures suggested by Hyndman and Koehler (2006): the mean absolute scaled error (MASE) MASEh = 1 S S ∑”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “More recently, Kim et al. (2011) cannot confirm the superior forecasting accuracy of the ARIMA-type models.”
A meta-analysis of international tourism demand forecasting and implications for practice (2014, background): “Makridakis and Hibon (1979) argue that they produce little improvement in forecasting accuracy, and Kim et al. (2011) conclude that the Seasonal ARIMA (SARIMA) model tends to underestimate the future uncertainty in interval forecasting.”
Testing the efficacy of the economic policy uncertainty index on tourism demand in USMCA: Theory and evidence (2020, methodology): “Kim et al. (2011) used (ARIMA) model explaining a variable’s past and a random disturbance terms statistically.”
New developments in tourism and hotel demand modeling and forecasting (2017, methodology): “When the coverage rates of interval forecasts from two models are equivalent, the model with narrower or tighter width is assumed to have superior forecasting property (Kim et al., 2011).”