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

arXiv: https://arxiv.org/abs/2604.27696 · HTML: https://mlsanity.com/paper/2604.27696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 60 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Probabilistic forecast reconciliation under the Gaussian framework](https://mlsanity.com/paper/2103.11128/) (2021)
- [Hierarchical forecast reconciliation with machine learning](https://mlsanity.com/paper/2006.02043/) (2020)
- [Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives](https://mlsanity.com/paper/2006.08570/) (2020)
- [Point and probabilistic forecast reconciliation for general linearly constrained multiple time series](https://mlsanity.com/paper/2305.05330/) (2023)
- [Probabilistic Reconciliation of Count Time Series](https://mlsanity.com/paper/2207.09322/) (2022)
- [Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning](https://mlsanity.com/paper/2402.09033/) (2024)
- [Optimal reconciliation with immutable forecasts](https://mlsanity.com/paper/2204.09231/) (2022)
- [Efficient probabilistic reconciliation of forecasts for real-valued and count time series](https://mlsanity.com/paper/2210.02286/) (2022)
- [Properties of the reconciled distributions for Gaussian and count forecasts](https://mlsanity.com/paper/2303.15135/) (2023)

## Abstract (arXiv)

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.