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Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (<jats:bold>J<\/jats:bold>oint <jats:bold>A<\/jats:bold>daptive predictio<jats:bold>N<\/jats:bold>-region <jats:bold>E<\/jats:bold>stimation for <jats:bold>T<\/jats:bold>ime-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled <jats:italic>K<\/jats:italic>-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET\u2019s superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.  <\/jats:p>","DOI":"10.1007\/s10994-025-06812-2","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T16:33:06Z","timestamp":1750696386000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["JANET: Joint Adaptive predictioN-region Estimation for Time-series"],"prefix":"10.1007","volume":"114","author":[{"given":"Eshant","family":"English","sequence":"first","affiliation":[]},{"given":"Eliot","family":"Wong-Toi","sequence":"additional","affiliation":[]},{"given":"Matteo","family":"Fontana","sequence":"additional","affiliation":[]},{"given":"Stephan","family":"Mandt","sequence":"additional","affiliation":[]},{"given":"Padhraic","family":"Smyth","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Lippert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"6812_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2023.107821","volume":"187","author":"N Ajroldi","year":"2023","unstructured":"Ajroldi, N., Diquigiovanni, J., Fontana, M., et al. 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