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

TL;DR

This paper introduces a multi-head attention CNN model for tourism demand forecasting, effectively capturing external factors and temporal patterns, and outperforms existing deep learning models in predicting inbound tourists to South Korea.

Contribution

The paper proposes a novel multi-head attention CNN framework that better handles multivariate external factors for tourism demand prediction.

Findings

Outperforms existing deep learning models in accuracy

Effectively captures external factors like politics and culture

Demonstrates superior forecasting in South Korea tourism data

Abstract

Developing an accurate tourism forecasting model is essential for making desirable policy decisions for tourism management. Early studies on tourism management focus on discovering external factors related to tourism demand. Recent studies utilize deep learning in demand forecasting along with these external factors. They mainly use recursive neural network models such as LSTM and RNN for their frameworks. However, these models are not suitable for use in forecasting tourism demand. This is because tourism demand is strongly affected by changes in various external factors, and recursive neural network models have limitations in handling these multivariate inputs. We propose a multi-head attention CNN model (MHAC) for addressing these limitations. The MHAC uses 1D-convolutional neural network to analyze temporal patterns and the attention mechanism to reflect correlations between input variables. This model makes it possible to extract spatiotemporal characteristics from time-series data of various variables. We apply our forecasting framework to predict inbound tourist changes in South Korea by considering external factors such as politics, disease, season, and attraction of Korean culture. The performance results of extensive experiments show that our method outperforms other deep-learning-based prediction frameworks in South Korea tourism forecasting.