JANET: Joint Adaptive predictioN-region Estimation for Time-series
This addresses the need for reliable uncertainty quantification in sequential data, particularly for multi-step predictions, but it is incremental as it builds on existing conformal prediction methods.
The paper tackled the problem of applying conformal prediction to time series data, which is limited by exchangeability assumptions and multi-step prediction challenges, by proposing JANET, a framework that constructs valid joint prediction regions with controlled error rates, showing superior performance in multi-step tasks across diverse datasets.
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.