  Conformal prediction offers a practical framework for distribution-free
uncertainty quantification, providing finite-sample coverage guarantees under
relatively mild assumptions on data exchangeability. However, these assumptions
cease to hold for time series due to their temporally correlated nature. In
this work, we present a novel use of conformal prediction for time series
forecasting that incorporates time series decomposition. This approach allows
us to model different temporal components individually. By applying specific
conformal algorithms to each component and then merging the obtained prediction
intervals, we customize our methods to account for the different
exchangeability regimes underlying each component. Our decomposition-based
approach is thoroughly discussed and empirically evaluated on synthetic and
real-world data. We find that the method provides promising results on
well-structured time series, but can be limited by factors such as the
decomposition step for more complex data.
