Tourism Demand Forecasting by Improved Dynamic Process Neural Network

This paper provides a tourism demand analysis of the national economy and the improvement of people' income in order to predict the tendency of the tourism demand, which influence and determine the development of the industry. A time series prediction method based on dynamic process neural network(DPNN) is proposed to solve this problem,which is developed by an improved particle swarm optimization (IPSO). The proposed DPNN is utilized to predict the tourism demand, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.