{"paper":{"title":"FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The R packages FoReco and FoRecoML supply a unified framework for forecast reconciliation that jointly handles cross-sectional, temporal, and cross-temporal cases with both linear and machine learning methods.","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.CO","authors_text":"Daniele Girolimetto, Ines Wilms, Jeroen Rombouts, Yangzhuoran Fin Yang","submitted_at":"2026-04-30T10:38:04Z","abstract_excerpt":"Forecast reconciliation has become key to improving the accuracy and coherence of forecasts for linearly constrained multiple time series, such as hierarchical and grouped series. Yet, comprehensive software that jointly covers cross-sectional, temporal, and cross-temporal reconciliation has so far been lacking. The R packages FoReco and FoRecoML address this gap by offering a comprehensive and unified framework. The packages respectively implement classical and regression-based linear reconciliation approaches, and non-linear approaches based on machine learning for cross-sectional, temporal "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The R packages FoReco and FoRecoML address this gap by offering a comprehensive and unified framework. 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The packages respectively implement classical and regression-based linear reconciliation approaches, and non-linear approaches based on machine learning for cross-sectional, temporal and cross-temporal frameworks.","one_line_summary":"FoReco and FoRecoML are R packages offering a unified toolbox for linear and non-linear forecast reconciliation across cross-sectional, temporal, and cross-temporal hierarchies.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That existing software lacked comprehensive joint support for cross-sectional, temporal, and cross-temporal reconciliation, and that the new packages correctly implement the referenced methods with sensible defaults while remaining flexible for experts.","pith_extraction_headline":"The R packages FoReco and FoRecoML supply a unified framework for forecast reconciliation that jointly handles cross-sectional, temporal, and cross-temporal cases with both linear and machine learning methods."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.27696/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T21:41:06.467084Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:01:47.984306Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"dfc2f66820f0df9a51b3ecba0aa4d1faeec9d5bca4b06d0a3f3218ab43f2488b"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}