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.

翻译：理解客源市场构成随时间演变的规律对目的地营销组织、酒店业经营者和旅游规划者至关重要。我们开发并应用贝叶斯狄利克雷自回归移动平均（BDARMA）模型，利用涵盖2017-2025年四大主要目的地地区的Airbnb专有预订数据，对客源市场份额的构成动态进行预测。分析揭示了疫情引发的客源构成结构性突变，各市场呈现异质性恢复模式。在构成复杂的市场中，BDARMA框架对EMEA地区实现最低预测误差，并在各目的地地区均表现出竞争力，其性能优于对数比率变换数据上的朴素预测、指数平滑和SARIMA等标准基准方法。对于EMEA目的地，BDARMA较朴素方法的预测误差降低27%（p < 0.001），在5-25%份额区间内多客源市场竞争激烈的地区增益最为显著。通过直接在单纯形上以狄利克雷似然对成分进行建模，并在均值和精度参数中纳入季节性变化，本方法既能生成遵循总和约束的连贯预测，又能捕捉复杂的时间依赖关系。该研究方法为目的地利益相关方提供客源市场份额的概率预测，从而为营销资源分配、基础设施投资和危机应对等战略规划提供更明智的决策依据。