Year: 2024
Type: preprint
Abstract: Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound. For forecasting problems in supervised learning, most CP methods focus on building prediction intervals for univariate responses. In this work, we develop a sequential CP method called $\texttt{MultiDimSPCI}$ that builds prediction $\textit{regions}$ for a multivariate response, especially in the context of multivariate time series, which are not exchangeable. Theoretically, we estimate $\textit{finite-sample}$ high-probability bounds on the conditional coverage gap. Empirically, we demonstrate that $\texttt{MultiDimSPCI}$ maintains valid coverage on a wide range of multivariate time series while producing smaller prediction regions than CP and non-CP baselines.
Source: arXiv (Cornell University)
Authors: Xu, Chen, Jiang, Hanyang, Xie, Yao
Institution: Georgia Institute of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection, Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency, CAREER: Quick Detection for Streaming Data Over Dynamic Networks, G3387330441, G4324459455, Data-Driven Optimal Police Patrol Zone Districting and Staffing, Sequential Detection and Prediction for Solar Situation Awareness in Power Networks, AI Institute for Advances in Optimization, G6127310570, G6671297155, G7238632903, G7911581280, G975068223
OpenAlex ID: W4392576552