Forecasting the Evolving Composition of Inbound Tourism Demand: A Bayesian Compositional Time Series Approach Using Platform Booking Data

Paper: arXiv 2602.18358

Authors: Harrison Katz

Abstract

Understanding how the composition of guest origin markets evolves over time is critical for destination marketing organizations, hospitality businesses, and tourism planners. We develop and apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to forecast the compositional dynamics of guest origin market shares using proprietary Airbnb booking data spanning 2017–2025 across four major destination regions. Our analysis reveals substantial pandemic-induced structural breaks in origin composition, with heterogeneous recovery patterns across markets. In our analysis, the BDARMA framework achieves the lowest forecast error for EMEA and competitive performance across destination regions, outperforming standard benchmarks including naïve forecasts, exponential smoothing, and SARIMA on log-ratio transformed data in compositionally complex markets. For EMEA destinations, BDARMA achieves 27% lower forecast error than naïve methods ($p < 0.001$), with the greatest gains where multiple origin markets compete in the 5-25% share range. By modeling compositions directly on the simplex with a Dirichlet likelihood and incorporating seasonal variation in both mean and precision parameters, our approach produces coherent forecasts that respect the unit-sum constraint while capturing complex temporal dependencies. The methodology provides destination stakeholders with probabilistic forecasts of source market shares, enabling more informed strategic planning for marketing resource allocation, infrastructure investment, and crisis response.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper applies a rigorous Bayesian Dirichlet autoregressive moving average (BDARMA) model to proprietary Airbnb booking data to forecast guest origin compositions on the simplex. It combines strong statistical methodology, robust empirical evaluation against classic benchmarks across multiple global regions, and clear framing around structural breaks such as COVID-19.

Research Flowchart

  flowchart TD
    A["<b>Research Goal</b><br>Forecast evolving composition of inbound tourism guest origin market shares"] --> B["<b>Data Inputs</b><br>Proprietary Airbnb booking data (2017–2025) across 4 destination regions"]
    
    B --> C["<b>Data Transformation & Setup</b><br>Map market shares directly to the compositional simplex"]
    
    C --> D["<b>Bayesian Modeling (BDARMA)</b><br>Fit Dirichlet ARMA models incorporating seasonal variations in mean & precision"]
    
    D --> E["<b>Performance Evaluation</b><br>Benchmark against Naïve, Exponential Smoothing, & Log-ratio SARIMA models"]
    
    E --> F["<b>Key Findings</b><br>BDARMA outperforms benchmarks (27% lower error in EMEA, p < 0.001)"]
    
    F --> G["<b>Strategic Outcomes</b><br>Provides coherent probabilistic forecasts for DMO marketing & resource allocation"]

Cited References (58)

1. A review of research on tourism demand forecasting: Launching the Annals of Tourism Research curated collection on tourism demand forecasting (2019) — link
2. Tourism demand forecasting: A time varying parameter error correction model (2006) — link
3. Forecasting tourism demand: A review of empirical research (1995) — link
4. Tourism demand modelling and forecasting: A review of recent research (2008) — link
5. Modeling and forecasting regional tourism demand using the B ayesian global vector autoregressive ( BGVAR ) model (2019) — link
6. Tourism forecasting: A review of methodological developments over the last decade (2019) — link
7. New developments in tourism and hotel demand modeling and forecasting (2017) — link
8. Economic analysis of tourism consumption dynamics: A time-varying parameter demand system approach (2012) — link

0 of 58 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.