The One Idea Worth Stealing From Foundation Forecasting
Chronos, MOMENT, and Moirai made forecasting feel like a language modeling problem, which is a fun trick but a heavy way to ask "what usually happens after a week that looks like this one." chronopatch keeps that one idea and throws out the billion-parameter checkpoint: similar recent patches predict the next patch.
The synthetic demand series has trend, seasonality, promotions, and drift, which is enough to expose a lazy seasonal baseline while keeping the whole benchmark reproducible on a laptop.
A Point Forecast Without Uncertainty Is A Guess In A Lab Coat
The forecaster retrieves historical contexts that look like the current one, averages what came next, and blends that with a recent trend correction.
Then it calibrates residuals on held-out data and wraps the forecast in a conformal interval. That interval is not decoration. Ship a forecast with no honest uncertainty band and the person reading it has to invent one, badly, usually at 2am.
The Number
Seasonal naive, the thing most dashboards do by default without telling you, scores 1.167 MASE. chronopatch scores 0.987, a 15.44% relative gain. Its conformal interval actually covers 93% of test points instead of just claiming to, with a mean width of 15.11.
4 tests pass locally and on GitHub Actions across Python 3.9, 3.11, and 3.13.