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Existing studies on tourism seasonality have been mainly identified at annual or monthly level and the seasonality in tourism demand forecasting has always been addressed by modeling season patterns. However, annual or monthly seasonality is coarse-grained and can’t capture the subtle changes emerging both in tourism theory and practice. This study recognizes tourism seasonality based on intra-day patterns and inter-day similarity and suggests a novel approach to addressing seasonality in tourism demand forecasting. The proposed three-step method contains tourism seasonality recognition, tourism seasonality matching, and tourism demand forecasting. The empirical findings, based on two attractions in China, demonstrate that the proposed method based on dynamic time warping and density-peak clustering can precisely capture tourism seasonality at the daily level. The method can also detect special tourism periods or subtle changes in seasonality, such as staggered peak travel phenomenon. Superior forecasting performance with seasonality matching is also revealed. This study sheds new light on tourism seasonality recognition and contributes to forecasting methodology.
Wang et al. (Sun,) studied this question.