Prediction-powered inference for time series across space
Using old data and imperfect predictions to forecast local trends reliably
When you have only a few years of actual measurements but decades of related data (like weather records), machine learning predictions can help fill gaps—but they introduce bias. Researchers adapted a statistical technique called prediction-powered inference to handle this problem across many locations, producing accurate forecasts and reliable confidence intervals even when time patterns and prediction errors vary by place.
Agricultural agencies, climate researchers, and resource managers often face exactly this situation: recent crop or environmental measurements at many farms or regions, paired with longer historical records of weather or other predictors. This method lets them make better forecasts and trust their uncertainty estimates, which matters for decisions about resource allocation, risk planning, and climate adaptation where underestimating uncertainty can lead to costly surprises.