Copula Conformal Prediction for Multi-step Time Series Forecasting
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.
Code (1)
Tasks
Conformal PredictionPredictionTime SeriesTime Series AnalysisTime Series ForecastingUncertainty QuantificationSimilar Papers 제목 키워드 기반
Copula-based conformal prediction for Multi-Target Regression
There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., freq…
Conformal PredictionMulti-target regressionMulti-Task LearningPrediction+2CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
We propose a novel framework that harnesses the power of generative artificial intelligence and copula-based modeling to address two critical challenges in multivariate time-series analysis: delivering accurate predictio…
Dimensionality ReductionOutlier DetectionAnomaly DetectionLocalized Anomaly Detection via Differentiable D-vine Copulas
Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copulas. Fitting a D-vine requires selecting a copula family and paramete…
Anomaly DetectionFiltered Conformal Ellipsoids for Graph-Native Time Series
Joint prediction sets for multivariate time series should control a single event while adapting to cross-coordinate dependence. We study filtered conformal ellipsoids: a frozen state-space filter emits a one-step predict…
Copula-Based Aggregation and Context-Aware Conformal Prediction for Reliable Renewable Energy Forecasting
The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggregated (fleet or system) levels. In practice, however, system operators oft…