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.

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