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--2024 across four major destination regions. Our analysis reveals substantial pandemic-induced structural breaks in origin composition, with heterogeneous recovery patterns across markets. The BDARMA framework achieves the lowest average forecast error across all destination regions, outperforming standard benchmarks including naïve forecasts, exponential smoothing, and SARIMA on log-ratio transformed data. For EMEA destinations, BDARMA achieves 23% lower forecast error than naive methods, with statistically significant improvements. 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：理解客源市场构成如何随时间演变，对目的地营销组织、酒店企业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2017-2024年四大目的地区域的专有Airbnb预订数据，预测客源市场份额的组合动态。分析揭示了疫情导致的客源构成存在显著结构性断点，且各市场呈现异质性复苏模式。在所有目的地区域中，BDARMA框架实现了最低的平均预测误差，其表现优于包括朴素预测法、指数平滑法以及对数比变换数据的SARIMA在内的标准基准方法。对于欧洲、中东和非洲（EMEA）目的地，BDARMA的预测误差比朴素方法低23%，且改进具有统计显著性。通过在单纯形上直接使用狄利克雷似然对组合进行建模，并在均值与精度参数中均纳入季节性变化，我们的方法能生成满足单位总和约束且捕捉复杂时间依赖性的协调预测。该方法为目的地利益相关者提供了客源市场份额的概率预测，从而为营销资源配置、基础设施投资和危机应对等战略规划提供更科学的决策依据。