A Hybrid Tourism Demand Forecasting Model Based on Fuzzy Times Series

Abstract

In previous studies, time series model has been the most common method and fuzzy time series model is an improvement on time series model. However, the accuracy in fuzzy time series model has been inevitably affected by interval length and the partitioning method for formulating effective interval can be very difficult. In addition to time series model, the grey model has also been widely studied, but it may have bad curve-fitting effects when data show great randomness. Therefore, both fuzzy time series model and grey model have problems in accuracy for forecasting tourism flow. Based on fuzzy time series model and grey model, this paper proposed a hybrid model which has been optimized by entropy and Markov model. In this hybrid model, the weight is calculated by entropy method which has been used to balance the performance of two single models. Markov model with its stable property is used for processing data sequence with large fluctuation. The result of the experiment clearly shows that this proposed hybrid model optimized the interval partition and therefore ensured the prediction accuracy.