Conformal time series decomposition with component-wise exchangeability

TL;DR

This paper introduces a new conformal prediction method for time series that uses decomposition to handle non-exchangeable data, providing tailored uncertainty quantification for different temporal components.

Contribution

It proposes a novel decomposition-based conformal prediction framework for time series that models components separately to improve uncertainty quantification.

Findings

Effective on well-structured time series

Limitations arise from the decomposition step in complex data

Empirical evaluation on synthetic and real-world datasets

Abstract

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.