Tourism Forecasting in the Multimodal Data Era

Dear Colleagues,

The global tourism sector has reached a strategic inflection point where Travel & Tourism’s total contribution to global GDP reached US$11.6 trillion in 2025. As the industry navigates uneven post-pandemic recovery and rising sustainability demands, the ability to accurately forecast has evolved from an administrative task into a critical pillar of governance and resource management. Forecasting in the tourism sector has evolved through a range of methodologies that includes univariate time-series models, econometric models with explanatory variables, multivariate systems, and forecast combinations, without any single approach dominating across all forecasting contexts. Against this background, the growing availability of high-frequency, heterogeneous data, including search behavior, online reviews, social and news content, mobility signals, weather, and sensor streams, creates new opportunities to enhance timeliness and robustness when these signals add demonstrable value over strong benchmark models.

This Special Issue seeks contributions that advance tourism forecasting through rigorous multi-source and mixed-frequency data integration, transparent out-of-sample evaluation, comparative benchmarking, interpretable AI, and policy-relevant model design. We particularly welcome work on spatial and spatiotemporal heterogeneity, resilience and early warning under crises, reproducible forecasting workflows, and governance-oriented applications for sustainable destination planning and crowd management. These priorities fit Forecasting’s aims as an outlet for theoretical, practical, computational, and methodological forecasting research and its encouragement of detailed reproducible materials.

This Special Issue will welcome manuscripts that link the following themes:

• Hybrid forecasting with behavioral, digital, and macro signals;
• Cross-platform digital-trace and content analytics, including search queries, reviews, social media, news, image, or video signals, assessed for their incremental predictive value over strong benchmarks;
• Behavioral sensing, biometrics, and wearable data for short-horizon operational forecasting, personalization, or experience analytics;
• Spatial-temporal forecasting and territorial heterogeneity;
• Sustainability, carrying capacity, and overtourism indicators;
• Comparative benchmark studies, interpretable AI, and forecasting governance;
• Crisis management, resilience and early warning systems for tourism and hospitality.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Alkiviadis Panagopoulos
Prof. Dr. Ioannis Nikas
Guest Editors

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