  To quantify uncertainty, conformal prediction methods are gaining
continuously more interest and have already been successfully applied to
various domains. However, they are difficult to apply to time series as the
autocorrelative structure of time series violates basic assumptions required by
conformal prediction. We propose HopCPT, a novel conformal prediction approach
for time series that not only copes with temporal structures but leverages
them. We show that our approach is theoretically well justified for time series
where temporal dependencies are present. In experiments, we demonstrate that
our new approach outperforms state-of-the-art conformal prediction methods on
multiple real-world time series datasets from four different domains.
