  We consider the problem of forming prediction sets in an online setting where
the distribution generating the data is allowed to vary over time. Previous
approaches to this problem suffer from over-weighting historical data and thus
may fail to quickly react to the underlying dynamics. Here we correct this
issue and develop a novel procedure with provably small regret over all local
time intervals of a given width. We achieve this by modifying the adaptive
conformal inference (ACI) algorithm of Gibbs and Cand\`{e}s (2021) to contain
an additional step in which the step-size parameter of ACI's gradient descent
update is tuned over time. Crucially, this means that unlike ACI, which
requires knowledge of the rate of change of the data-generating mechanism, our
new procedure is adaptive to both the size and type of the distribution shift.
Our methods are highly flexible and can be used in combination with any
baseline predictive algorithm that produces point estimates or estimated
quantiles of the target without the need for distributional assumptions. We
test our techniques on two real-world datasets aimed at predicting stock market
volatility and COVID-19 case counts and find that they are robust and adaptive
to real-world distribution shifts.

Methodology;Machine Learning