Demand Forecasting Models of Tourism Based on ELM

Abstract

In order to realize the more accurate prediction of annual tourism, use the synthetic index method to calculate the tourism market boom index, after timing phase space reconstruction, merge the original travel data and the tourism market boom index to get the sample, using extreme learning machine algorithm to train sample data, finally get the demand forecasting model of tourism in Liaoning province based on ELM. By comparing the support vector regression algorithm show that: the model based on extreme learning machine algorithm make higher precision, better fitting degree, can more accurately estimate and forecast the tourism market, the application of this model can provide guidance for the tourism market to achieve a reasonable allocation of resources and healthy development.