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.

本文提出了一种新颖的使用具有时间序列分解的符合预测时间序列预测方法，在每个分量上应用特定的符合算法，然后合并所得预测区间，以适应不同分量之间的不同可交换性情况。该分解方法在合成和现实数据上进行了详细讨论和经验评估，发现该方法对结构良好的时间序列提供了有希望的结果，但在处理较复杂数据时，受到分解步骤等因素的限制。