Copula Conformal Prediction for Multi-step Time Series Forecasting

• URL: http://arxiv.org/abs/2212.03281v4
• Date: Mon, 18 Mar 2024 21:49:35 GMT
• Title: Copula Conformal Prediction for Multi-step Time Series Forecasting
• Authors: Sophia Sun, Rose Yu,
• Abstract summary: We propose a Copula Conformal Prediction algorithm for time series forecasting, CopulaCPTS. We show that CopulaCPTS produces more calibrated and sharp confidence intervals for multi-step prediction tasks than existing techniques.
• Score: 18.298634240183862
• License: http://creativecommons.org/licenses/by/4.0/
• 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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