Forecasting the Evolving Composition of Guest Origin Markets in Platform Bookings: A Bayesian Compositional Time-Series Approach Using Airbnb Data

Tourism-demand forecasting overwhelmingly targets aggregate volumes, leaving the question of where demand will come from largely unaddressed. This paper forecasts that question directly. We make three contributions. First, we apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to guest origin composition in large-scale platform booking data, which to our knowledge is the first use of Bayesian compositional time-series methods on booking-origin shares across multiple global destination regions. Second, we introduce seasonal structure in the Dirichlet precision parameter, and we isolate its contribution through an ablation against an otherwise identical constant-precision specification: seasonal precision lowers mean absolute error in all four destination regions, by between 13% and 35%. Third, the forecast target is the monthly composition of bookings indexed by booking date rather than stay date, which makes it observable ahead of realized arrivals and therefore usable for decisions with long lead times. Using proprietary Airbnb reservation data spanning 2017–2025 across four destination regions, we document substantial pandemic-era shifts in booking composition with heterogeneous recovery patterns. In rolling-origin evaluation, BDARMA achieves the lowest forecast error for EMEA, the most compositionally diverse region, reducing mean absolute error by 27% relative to naïve forecasts ( p < 0.001 ). Performance elsewhere is mixed: simple benchmarks remain hard to beat, and exponential smoothing on isometric log-ratio-transformed data attains the lowest error averaged across the four regions. The EMEA pattern suggests that direct compositional modeling is most valuable where several origin markets hold material shares, although four destination regions are too few to establish this as a general rule. The methodology yields probabilistic forecasts of source market shares that can inform marketing allocation, concentration-risk monitoring, and forward-looking operational planning.