arXiv:2508.13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees. However, existing multi-step methods often struggle to balance coverage validity with efficiency: they either calibrate horizons independently, ignoring temporal correlations, or enforce strict simultaneous coverage, resulting in overly conservative intervals. In this work, we propose O2CP: Optimization-based Online Conformal Prediction, a unified framework for online conformal prediction that ex
The increasing sophistication and widespread adoption of AI in forecasting across various domains demand more robust and reliable uncertainty quantification methods.
Improved online conformal prediction for multi-step forecasting enhances the trustworthiness and practical applicability of AI predictions, critical for automated decision-making systems.
The ability to generate more accurate and efficient prediction intervals for time series models reduces over-conservatism and improves the reliability of real-time forecasts, leading to more optimal operational outcomes.
- · AI/ML developers
- · Financial services
- · Logistics and supply chain management
- · Healthcare
- · Legacy forecasting methods
- · Systems with high tolerance for uncertainty
More reliable AI-driven forecasting leads to better resource allocation and risk management.
Increased automation of decision-making processes that rely on time-series predictions.
Potentially, new services and products built on top of highly accurate predictive uncertainty quantification.
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Read at arXiv cs.LG