We develop a method to construct distribution-free prediction intervals for dynamic time-
series, called\Verb| EnbPI| that wraps around any bootstrap ensemble estimator to construct …

We present a new distribution-free conformal prediction algorithm for sequential data (eg,
time series), called the sequential predictive conformal inference (SPCI). We specifically …

To quantify uncertainty, conformal prediction methods are gaining continuously more
interest and have already been successfully applied to various domains. However, they are …

This article presents a novel probabilistic forecasting method called ensemble
conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and …

Conformal prediction is a statistical tool for producing prediction regions of machine learning
models that are valid with high probability. However, applying conformal prediction to time …

We study the problem of uncertainty quantification for time series prediction, with the goal of
providing easy-to-use algorithms with formal guarantees. The algorithms we present build …

Uncertainty quantification of predictive models is crucial in decision-making problems.
Conformal prediction is a general and theoretically sound answer. However, it requires …

We extend conformal inference to general settings that allow for time series data. Our
proposal is developed as a randomization method and accounts for potential serial …

We propose spectral methods for long-term forecasting of temporal signals stemming from
linear and nonlinear quasi-periodic dynamical systems. For linear signals, we introduce an …

Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …