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Developing an accurate tourism forecasting model is essential for makingdesirable policy decisions for tourism management. Early studies on tourismmanagement focus on discovering external factors related to tourism demand.Recent studies utilize deep learning in demand forecasting along with theseexternal factors. They mainly use recursive neural network models such as LSTMand RNN for their frameworks. However, these models are not suitable for use inforecasting tourism demand. This is because tourism demand is strongly affectedby changes in various external factors, and recursive neural network modelshave limitations in handling these multivariate inputs. We propose a multi-headattention CNN model (MHAC) for addressing these limitations. The MHAC uses1D-convolutional neural network to analyze temporal patterns and the attentionmechanism to reflect correlations between input variables. This model makes itpossible to extract spatiotemporal characteristics from time-series data ofvarious variables. We apply our forecasting framework to predict inboundtourist changes in South Korea by considering external factors such aspolitics, disease, season, and attraction of Korean culture. The performanceresults of extensive experiments show that our method outperforms otherdeep-learning-based prediction frameworks in South Korea tourism forecasting.

A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea