Sequential probability integral transform ($U_t=F_t(Y_t)\mid Y_{<t}\sim \mathrm{Unif}(0,1)$)

For correct continuous conditional forecasts, the conditional probability integral transform is uniform given the entire past. Iterating conditional probabilities makes the transformed sequence independent and uniform. Uniformity and serial checks assess absolute predictive calibration using only the forecast distributions and observations.