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arXiv · 2212.03281

Copula Conformal Prediction for Multi-step Time Series Forecasting

Sophia Sun·Rose Yu
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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 Conformal Prediction algorithm for multivariate, multi-step Time Series forecasting, CopulaCPTS. We prove that CopulaCPTS has finite sample validity guarantee. 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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Sophia Sun, Rose Yu. 2024-03-18. Copula Conformal Prediction for Multi-step Time Series Forecasting. https://arxiv.org/abs/2212.03281

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