FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R

arXiv stat.ML / 5/1/2026

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Key Points

The paper introduces the R packages FoReco and FoRecoML to fill a gap in software for forecast reconciliation across cross-sectional, temporal, and cross-temporal settings.
FoReco focuses on classical linear reconciliation methods, while FoRecoML provides regression-based linear approaches and non-linear machine-learning-based methods.
The toolkit is designed to be both beginner-friendly and expert-friendly, offering sensible defaults for quick use and configurable options for advanced, state-of-the-art experimentation.
The goal is to improve both forecast accuracy and coherence for linearly constrained multiple time series, including hierarchical and grouped series.
Overall, the packages aim to support both practitioners and researchers who need a unified reconciliation workflow in R.

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. 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 and cross-temporal frameworks. Designed for accessibility and flexibility, these packages provide sensible default options that allow new users to apply reconciliation methods with minimal effort, while still giving expert users full control to explore state-of-the-art extensions through customized settings. With this dual focus, FoReco and FoRecoML are versatile tools for practitioners and researchers working on forecast reconciliation.