A cross-view model for tourism demand forecasting with artificial intelligence method

Abstract

Forecasting always plays a vital role in modern economic and industrial fields, and tourism demand forecasting is an important part of intelligent tourism. This paper proposes a simple method for data modeling and a combined cross-view model, which is easy to implement but very effective. The method presented in this paper is commonly used for BPNN and SVR algorithms. A real tourism data set of Small Wild Goose Pagoda is used to verify the feasibility of the proposed method, with the analysis of the impact of year, season, and week on tourism demand forecasting. Comparative experiments suggest that the proposed model shows better accuracy than contrast methods.