Spectral Adaptive Conformal Prediction Handles Time Series with Seasons and Changing Frequencies

Conformal prediction is widely used for uncertainty quantification because it provides finite-sample coverage guarantees—but only under exchangeability, where data order is irrelevant. Real-world time series, however, often exhibit seasons, recurring regimes, or gradually changing frequencies, breaking exchangeability and invalidating standard methods. In response, Jeffery Opoku and David Banahene propose Spectral Adaptive Conformal Prediction, published as arXiv:2606.15950. The method addresses this gap with a two-pronged approach: it forms weighted conformal quantiles using localized spectral similarity to select relevant calibration residuals, then adjusts the target miscoverage level online to correct long-run error rates as uncertainty evolves.

The hybrid design improves on earlier spectral-only weighting by adding an adaptive update that accounts for shifts in prediction difficulty. The paper includes a theoretical coverage result for the fixed spectral quantile and a deterministic calibration guarantee for the adaptive update. In experiments—including simulations with regime-switching and slowly changing frequencies, plus three U.S. real datasets (likely from finance, climate, or energy)—the hybrid method consistently reduced interval width while maintaining target coverage. A critical finding is that spectral weighting must be monitored via effective sample size diagnostics; otherwise, too few relevant residuals can hurt performance. The work has immediate implications for any domain where reliable prediction intervals are needed despite non-stationary patterns.