Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series
arXiv:2605.26569v1 Announce Type: new Abstract: We present Distribution-aware Conformal Prediction (DCP), a unified framework integrating probabilistic predictors like Monte Carlo dropout, deep ensembles, and quantile regression with score-agnostic conformal calibration to produce valid and efficient prediction intervals. Leveraging a numerical inversion approach to construct interval bounds, DCP accommodates arbitrary combinations of distribution generating predictors and nonconformity scores. Benchmark analysis on synthetic and real-world time series data demonstrate DCP's ability to adaptiv
The increasing sophistication of AI models, particularly in time series prediction, necessitates more robust and reliable uncertainty quantification methods.
Accurate prediction intervals are crucial for deploying AI in high-stakes environments like financial markets, healthcare, and infrastructure management, ensuring trustworthiness and reducing risk.
The ability to generate valid and efficient prediction intervals for diverse probabilistic AI models will accelerate their adoption and improve decision-making in real-world applications.
- · AI/ML researchers
- · Industries relying on time series predictions (finance, healthcare, energy)
- · Deep learning practitioners
- · Developers of Monte Carlo dropout and deep ensembles
- · Companies relying on less rigorous uncertainty quantification methods
- · Traditional statistical forecasting methods without robust interval generation
Improved reliability and broader deployment of AI systems in critical applications.
Increased investor and public confidence in AI-driven decision support systems.
Potential for new regulations and standards around AI uncertainty quantification as systems become more pervasive.
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Read at arXiv cs.LG