Forecasting tourist arrivals using dual decomposition strategy and an improved fuzzy time series method

Tourist arrivals forecasting has become an increasingly hot issue due to its important role in the tourism industry and hence the whole economy of a country. However, owing to the complex characteristics of tourist arrivals series, such as seasonality, randomness, and non-linearity, forecasting tourist arrivals remains a challenging task. In this paper, a hybrid model of dual decomposition and an improved fuzzy time series method is proposed for tourist arrivals forecasting. In the novel model, two stages are mainly involved, i.e., dual decomposition and integrated forecasting. In the first stage, a dual decomposition strategy, which can overcome the potential defects of individual decomposition approaches, is designed to fully extract the main features of the tourist arrivals series and reduce the data complexity. In the second stage, a fuzzy time series method with fuzzy C-means algorithm as the discretization method is developed for prediction. In the empirical study, the proposed model is implemented to predict the monthly tourist arrivals to Hong Kong from USA, UK, and Germany. The results show that our hybrid model can obtain more accurate and more robust prediction results than benchmark models. Relative to the benchmark fuzzy time series models, the hybrid models using traditional decomposition methods and strategies, as well as the traditional single prediction models, our proposed model shows a significant improvement, with the improvement percentages at about 80, 70, and 50%, respectively. Therefore, we can conclude that the proposed model is a very promising tool for forecasting future tourist arrivals or other related fields with complex time series.