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Accurate uncertainty measurement is a key step to building robust andreliable machine learning systems. Conformal prediction is a distribution-freeuncertainty 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 limitedto single-step prediction without considering the temporal dependency. In thispaper we propose a Copula Conformal Prediction algorithm for multivariate,multi-step Time Series forecasting, CopulaCPTS. We prove that CopulaCPTS hasfinite sample validity guarantee. On several synthetic and real-worldmultivariate time series datasets, we show that CopulaCPTS produces morecalibrated and sharp confidence intervals for multi-step prediction tasks thanexisting techniques.

Copula Conformal Prediction for Multi-step Time Series Forecasting