# When history fails you, borrow from geography

DevFeed: [When history fails you, borrow from geography](<https://devfeed.tech/articles/when-history-fails-you-borrow-from-geography-1224.md>)

Original publisher: [Read original article](<https://medium.com/airbnb-engineering/when-history-fails-you-borrow-from-geography-915a72b91b5c?source=rss----53c7c27702d5---4>)

Author: Harrison Katz

Published: 2026-06-02T17:01:04Z

Content type: article

Language: en

Sources: [The Airbnb Tech Blog - Medium](<https://devfeed.tech/sources/the-airbnb-tech-blog-medium.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [building](<https://devfeed.tech/tags/building.md>), [data](<https://devfeed.tech/tags/data.md>), [data-modeling](<https://devfeed.tech/tags/data-modeling.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [post](<https://devfeed.tech/tags/post.md>), [technology](<https://devfeed.tech/tags/technology.md>), [travel-industry](<https://devfeed.tech/tags/travel-industry.md>)

## AI overview

Airbnb describes a forecasting approach for the uneven post-COVID travel recovery, using sequential signals from other geographies and propagating prior information to produce corridor-level forecasts when local post-shock data was scarce.

## Source excerpt

How Airbnb used sequential geographic recovery signals and prior propagation to generate reliable corridor-level forecasts when local data was scarce. By: Harrison Katz The problem with unprecedented shocks Almost every forecasting system is built on the same implicit assumption: the future will resemble the past. You train on historical data, you validate on holdout periods, and you trust that past patterns will at least roughly indicate future performance. When this assumption breaks, the model does not gracefully degrade; it fails confidently. It produces precise, well-calibrated intervals around the wrong answer. The acute phase of COVID, from early to late 2020, was a clear illustration of this, and we wrote about it in a previous post. But the more interesting forecasting problem was not the shutdown. It was everything that came after. The period from late 2020 through 2022 was not a single coherent regime. It was a sequence of overlapping, asynchronous changes: vaccine rollouts that reached some markets months before others, border reopenings that followed their own country-level timelines, reclosures triggered by new variants that hit different corridors (a pairing of the traveler's origin city and destination city) at different moments. Demand was not recovering uniformly. It was rebounding unevenly across every corner of the world, in ways that had no historical precedent and no single governing pattern. The standard response to a shock is to wait for each affected market to accumulate its own post-shock data and retrain locally. But Covid was among the biggest shocks the travel industry has faced in decades. With markets worldwide reopening and reclosing on staggered schedules, waiting for markets to settle meant forecasting blind for months at a time, across all markets, just when timely projections were most needed, in the circumstances. So we started building something different. When we could not simply look backward in time for relevant examples, we