Software and packages

  1. FoCo2 2026-06-25 2026-06-23

    FoCo2 (Coherent Forecast Combination) is a forecasting package designed to handle multiple time series forecasts from different experts, subject to linear constraints. It offers both optimal and heuristic methods for combining expert forecasts and reconciling them through a multi-task approach. This process either simultaneously (in the optimal case) or sequentially (in the heuristic cases) integrates forecasts from multiple experts while incorporating a priori constraints to produce coherent forecasts. In addition, linear inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.

  2. FoReco 2026-06-25 2026-06-22

    Forecast Reconciliation is a a post-forecasting process aimed to improve the accuracy and align forecasts for a system of linearly constrained (e.g. hierarchical/grouped) time series. The FoReco package provides a comprehensive set of classical (bottom-up, top-down and middle-out), and modern (optimal and heuristic combination) forecast reconciliation procedures in different frameworks including cross-sectional, temporal, or cross-temporal settings. Additionally, FoReco provides various functions for different aspects of forecast reconciliation, including aggregating time series, balancing hierarchies, computing projection and covariance matrices, and more.

  3. FoRecoML 2026-06-25 2026-06-23

    Provides nonlinear forecast reconciliation methods based on machine learning for cross-sectional, temporal, and cross-temporal frameworks. Starting from potentially incoherent base forecasts, the package learns nonlinear relationships across levels and produces coherent forecasts that satisfy hierarchical and temporal aggregation constraints. It supports multiple machine learning algorithms through a unified interface, enabling flexible and data-driven reconciliation of hierarchical and grouped time series while integrating with existing forecasting workflows.

  4. FoRecoPy 2025-12-23 2025-10-13

    Forecast reconciliation is a post-forecasting process aimed at improving the accuracy and coherence of forecasts for a system of linearly constrained time series (e.g., hierarchical, grouped, or temporal structures). The FoRecoPy package is inspired by the R package FoReco and brings similar functionality to Python. It is designed for researchers, practitioners, and data scientists who use Python for time series forecasting and want access to state-of-the-art reconciliation methods.

  5. nlcReco 2025-10-28

    Non linearly constrained forecast reconciliation is a a post-forecasting process aimed to improve the accuracy and align forecasts for a system of non-linearly constrained time series. The nlcReco package provides optimal combination forecast reconciliation procedures for multiple time series with linear and non-linear constraints in cross-sectional framework.

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Others

  • haR: Heterogeneous AutoRegressive (HAR) model [github]
  • TimeGPTapi: Interact with the TimeGPT API [github]