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

2026-04-30 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, quick

Summary

The R packages FoReco and FoRecoML, submitted on April 30, 2026, provide a unified toolbox for forecast reconciliation. They address the previous lack of comprehensive software covering cross-sectional, temporal, and cross-temporal reconciliation for linearly constrained multiple time series, including hierarchical and grouped series. FoReco and FoRecoML implement classical, regression-based linear, and machine learning approaches. Designed for both new and expert users, the packages offer sensible default options for ease of use and full control for advanced state-of-the-art extensions. This makes them versatile tools for researchers and practitioners in forecast reconciliation.

Key takeaway

For data scientists and researchers working with linearly constrained time series, FoReco and FoRecoML offer a robust solution to improve forecast accuracy and coherence. You should explore these R packages to streamline the application of diverse reconciliation methods, from classical to machine learning, across cross-sectional, temporal, and cross-temporal dimensions. This can significantly enhance the reliability of your forecasting models.

Key insights

FoReco and FoRecoML unify forecast reconciliation methods for diverse time series in R.

Principles

• Forecast reconciliation improves accuracy.
• Unified frameworks enhance accessibility.

Method

The packages implement classical, regression-based linear, and machine learning approaches for cross-sectional, temporal, and cross-temporal forecast reconciliation.

In practice

• Apply reconciliation to hierarchical series.
• Use default options for quick setup.
• Customize settings for advanced research.

Topics

• Forecast Reconciliation
• R Packages
• Time Series Analysis
• Machine Learning
• Hierarchical Forecasting
• Statistical Computing

Best for: Machine Learning Engineer, AI Scientist, Research Scientist, Data Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.