A time series attention mechanism based model for tourism demand forecasting

An accurate estimation of tourism demand is of great significance to tourism management. The seasonal and non-stationary features present a significant challenge in developing tourism demand estimation. An effective tourism demand forecasting model is important to address this problem. This paper proposes a novel model for tourism demand forecasting based on developed mechanism-guided attention. The model consists of three sections: the first section defines the degree of stationarity of the demand time series, the second section develops a guided attention mechanism for improving the feature recognition of neural networks; the third section generates forecasting results of tourism demand, in the meanwhile, the developed fully connected recurrent neural network is adopted to identify complex features of tourism demand time series. The proposed model is helpful in identifying the features of seasonality and non-linearity in tourism demand data, the model maintains good forecasting accuracy with the appropriate training process. Case studies show that the proposed method can forecast the daily tourism demand of Macau, China accurately compared with other traditional forecasting methods.