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Decomposition Methods for Tourism Demand Forecasting: A Comparative Study
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DOI：10.1177/00472875211036194.png)
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Abstract
En 中文
Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data's intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.
Keywords:
tourism demand forecasting
decomposition methods
variational mode decomposition
decomposition and ensemble
machine learning
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1.6K
Citations:
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