Copula Conformal Prediction for Multi-step Time Series Forecasting

arXiv:2212.03281v4 Announce Type: replace
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 …

abstract algorithm algorithms arxiv building copula coverage cs.lg distribution forecasting free however implementation key learning systems machine machine learning measurement popular prediction quantification robust series stat.ap statistical systems time series time series forecasting type uncertainty