arXiv:2606.15950v1 Announce Type: cross Abstract: Conformal prediction gives prediction intervals with finite-sample coverage when the data are exchangeable. Many time-indexed datasets are not exchangeable. They have seasons, recurring regimes, changing frequencies, or other forms of structured dependence. This paper studies a simple way to use that structure. We propose spectral adaptive conformal prediction, a method that forms weighted conformal quantiles using local spectral similarity and then updates the target miscoverage level online. The spectral weights choose calibration residuals t
This research addresses a fundamental limitation of conformal prediction in real-world time-indexed datasets, which often exhibit structured dependence.
Improved predictive accuracy and reliability for non-exchangeable data can enhance decision-making across various AI applications, from finance to scientific forecasting.
The ability to generate reliable prediction intervals for complex, non-exchangeable datasets becomes more robust, broadening the applicability of conformal prediction.
- · AI researchers
- · Machine learning engineers
- · Financial modeling
- · Climate science
- · Systems relying on less robust uncertainty quantification
- · Models making assumptions of data exchangeability
More accurate and trustworthy AI models, particularly in dynamic environments, lead to better operational decisions.
Increased adoption of conformal prediction in industries where data is inherently non-exchangeable, such as time-series analysis.
Enhanced trust in AI systems due to improved uncertainty quantification, potentially accelerating AI integration into critical infrastructure.
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Read at arXiv cs.LG