Prediction for Tourism Flow based on LSTM Neural Network

Abstract Accurate tourism flow prediction is one of the most difficult problems in the Intelligent Tourism System (ITS), especially in the short-term forecast. Existing models such as ARMA, ARIMA which are mainly linear models and cannot describe the stochastic and non-linear nature of tourism flow. In recent years, deep-learning-based methods have been applied as novel alternatives for tourism flow prediction. However, which kind of deep neural networks is the most appropriate model for tourism flow prediction remains unsolved. In this paper, we use Long Short Term Memory Neural Network (LSTM) methods to predict tourism flow, and experiments demonstrate that LSTM methods perform better than Auto Regressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network(BPNN). To the best of our knowledge, this is the first time that LSTM NN is applied to tourism flow prediction.