Copula Conformal Prediction for Multi-step Time Series Forecasting

Sophia Huiwen Sun, Rose Yu

Keywords: Conformal Prediction, time series, uncertainty quantification, calibration, RNN

TL;DR: significantly improve efficiency/sharpness of conformal prediction confidence intervals, for time series forecasting, by modeling dependence of time steps using copulas

Abstract: Accurate uncertainty measurement is a key step to building robust and reliable machine learning systems. Conformal prediction is a distribution-free uncertainty quantification algorithm popular for its ease of implementation, statistical coverage guarantees, and versatility for underlying forecasters. However, existing conformal prediction algorithms for time series are limited to single-step prediction without considering the temporal dependency. In this paper we propose a Copula-based Conformal Prediction algorithm for multivariate, multi-step Time Series forecasting, CopulaCPTS. On several synthetic and real-world multivariate time series datasets, we show that CopulaCPTS produces more calibrated and sharp confidence intervals for multi-step prediction tasks than existing techniques.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Probabilistic Methods (eg, variational inference, causal inference, Gaussian processes)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/copula-conformal-prediction-for-multi-step/code)