Conformal Prediction for Time Series with Modern Hopfield Networks
This addresses uncertainty quantification for time series data, which is crucial in domains like finance or healthcare, but appears incremental as it builds on existing conformal prediction frameworks.
The paper tackles the challenge of applying conformal prediction to time series by proposing HopCPT, a method that leverages temporal dependencies, and demonstrates it outperforms state-of-the-art methods on multiple real-world datasets.
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.