Conformal predictions allow one to add prediction intervals to arbitrary machine learning regression models. For more information see Algorithmic Learning in a Random World.
An assumption of conformal predictions is that the observations are exchangeable, a weaker version of i.i.d. I would like to apply conformal predictions to a model where the input data consists of events that take place during the year. The expectation is that there are some seasonal effects. So these observations are neither exchangeable nor i.i.d.
In the model though we do take into account seasonal attributes like month of year and other indicators. Our assumption is that the errors of the model predictions are more or less i.i.d. through time. Since the conformal predictions hinge on order statistics calculated on known prediction errors, could conformal predictions in this case still work?
For those interested, roughly conformal predictions work on the observation that if one draws a random sample, and then orders the outcome descending then it is possible to assess with which probability a next random observation will for example be smaller than the 8th observation in the original sample.