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.

翻译：预测协调已成为提升线性约束多元时间序列（如层次序列与分组序列）预测准确性与一致性的关键手段。然而，目前尚缺乏同时覆盖截面、时间及跨时间协调的综合性软件。R语言包FoReco与FoRecoML通过提供统一框架填补了这一空白，二者分别实现了基于经典回归的线性协调方法与基于机器学习的非线性协调方法，可适用于截面、时间及跨时间框架。这些软件包兼具易用性与灵活性：既提供合理的默认参数使新用户能以最小投入应用协调方法，也允许高级用户通过定制化设置探索前沿扩展方案。FoReco与FoRecoML凭借这一双重定位，成为面向预测协调领域实践者与研究者的多功能工具。