Deep Learning Framework for Forecasting Tourism Demand

Accurate Tourism demand forecasting plays an important role to make decision and plan policy. However, tourism demand is characterized by complexity and non-linearity. Traditional tourism demand forecasting techniques are Linear methods and unable to fully simulate the nonlinear characteristics of tourism demand. Deep learning (DL) methods can be a promising solution to achieve an accurate forecast. These models are able to evaluate the non-linear relationship, without the drawbacks of Time Series and econometric models. In this paper, a deep learning Models are proposed to accurately predict tourist arrivals for Morocco from 2010 to 2019. The proposed framework uses a long short-term memory (LSTM) and gated recurrent unit (GRU). Experiments demonstrate that the LSTM and GRU methods perform better than support vector regression (SVR) and artificial neural network models (ANN).