Online conformal prediction with decaying step sizes
Anastasios N. Angelopoulos Rina Foygel Barber Stephen Bates
We introduce a method for online conformal prediction with decaying step
sizes. Like previous methods, ours possesses a retrospective guarantee of
coverage for arbitrary sequences. However, unlike previous methods, we can
simultaneously estimate a population quantile when it exists. Our theory and
experiments indicate substantially improved practical properties: in
particular, when the distribution is stable, the coverage is close to the
desired level for every time point, not just on average over the observed
sequence.