FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in RDaniele Girolimetto et al. · 2026-06-26
arXiv:2604. 27696v2 Announce Type: replace-cross Abstract: 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.
Why now: Read next because FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)". Matching terms: class, soft, line, implement, control, full, trained. Source: arxiv stat.ML (Machine Learning).