Software and packages

  1. FoReco 2025-10-30 2025-06-07

    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.

  2. 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.

  3. FoRecoPy 2025-10-13 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.

  4. FoCo2 2025-09-18 2025-06-14

    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.

No matching items

Others

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