Airbnb's forecasting models, crucial for financial planning, faced severe disruption during the COVID-19 pandemic. The core challenge was that the relationship between bookings and travel dates, historically stable, became highly volatile. To address this, Airbnb separated its forecasting into two components: gross booking volume and lead-time composition. They developed B-DARMA models specifically designed to handle the changing proportions associated with booking lead times. Surprisingly, even after gross bookings recovered, lead-time compositions exhibited persistent shifts, not returning to pre-pandemic patterns. Airbnb used a distributional divergence metric to monitor and quantify these long-term changes, which became a vital tool for model health diagnosis. These durable shifts directly affected revenue forecasting, cash flow, and operational decisions, highlighting the importance of accurate distributional modeling. To improve forecasting they developed the capacity to learn from structural breaks, allowing the models to adapt to changes. By separating and analyzing these components, Airbnb created more resilient forecasting models, able to detect and adapt to structural shifts.