Airbnb uses hierarchical Bayesian prior propagation to generate corridor-level demand forecasts when local data is scarce
Standard forecasting models built on the assumption that the future resembles the past failed confidently during COVID — producing precise but wrong outputs — while waiting for each affected market to accumulate its own post-shock data meant forecasting blind for months at a time, just when timely projections were most needed.
Airbnb could generate informative corridor-level demand forecasts throughout the COVID recovery — including for markets where local data was thin — with signals available immediately rather than weeks or months later; the approach has since become standing forecasting infrastructure.
Standard time series models designed for a world with a stable shared regime could not handle asynchronous, corridor-level disruptions and produced confidently wrong forecasts when COVID broke the historical-similarity assumption.
https://medium.com/airbnb-engineering/when-history-fails-you-borrow-from-geography-915a72b91b5c