title: Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.
abstract: With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.
authors.affiliation: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan.
authors.forename: Ying
authors.initials: Y
authors.lastname: Yu
authors.affiliation: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan.
authors.forename: Yirui
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan.
authors.forename: Shangce
authors.identifier: 0000-0001-5042-3261
authors.initials: S
authors.lastname: Gao
authors.affiliation: Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Tang
mesh_terms: D005544:Forecasting / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer