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

TL;DR

FoReco and FoRecoML are comprehensive R packages that unify various forecast reconciliation methods, including classical, regression-based, and machine learning approaches, for improved accuracy in constrained time series.

Contribution

They provide the first unified software framework covering cross-sectional, temporal, and cross-temporal forecast reconciliation in R.

Findings

Implement classical and regression-based reconciliation methods.

Incorporate machine learning approaches for non-linear reconciliation.

Offer user-friendly defaults and advanced customization options.

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.