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arXiv Machine Learning By Ruihang Han, Li-Hsiang Lin

State-Matched Sequence Conformal Prediction

Read the original on arXiv Machine Learning →

State-Matched Sequence Conformal Prediction (SMSCP) is a new method for sequential conformal prediction that builds a one‑sided local‑linear state from pre‑target data and uses state similarity to select a matched reference set of past calibration scores. By applying an ordinary unweighted conformal order statistic to this matched set, SMSCP aims to better capture the target score quantile when comparable states recur at nonadjacent times. Experiments on solar irradiance and bike‑demand data show that SMSCP improves interval scores over recency, weighted, adaptive, and HopCPT methods, and achieves coverage closer to the nominal 0.90 level than ResCP on most benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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