Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model

• Main Authors: Yang, Dongchuan, Li, Yanzhao, Guo, Ju’e, Li, Guang, Sun, Shaolong
• Other Authors: Nanyang Business School
• Format: Article
• Language: English
• Published: 2024
• Subjects: Business and Management; International Tourist Arrivals; Deep Learning
• Online Access: https://hdl.handle.net/10356/173592
• Institution: Nanyang Technological University

With the advancement of economic globalization and regional integration, regional tourism flows are more closely linked, which provides new clues for improving forecasting. This study develops a multivariate decomposition deep learning model to forecast tourism demand by capturing spatiotemporal interactions among regional tourism flows. The multivariate decomposition technique is introduced to reduce data complexity, while convolutional neural networks and long short-term memory networks are extracting spatial and temporal correlations of regional tourism flows. The effectiveness of the model is demonstrated in two heterogeneous international tourism cases of tourist arrivals from China or Japan to leading destinations in Southeast Asia.