  Split conformal prediction is a popular tool to obtain predictive intervals
from general statistical algorithms, with few assumptions beyond data
exchangeability. We show that coverage guarantees from split CP can be extended
to dependent processes, such as the class of stationary $\beta$-mixing
processes, by adding a small coverage penalty. In particular, we show that the
empirical coverage bounds for some $\beta$-mixing processes match the order of
the bounds under exchangeability. The framework introduced also extends to
non-stationary processes and to other CP methods, and experiments corroborate
our split CP's coverage guarantees under dependent data.
