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hierarchicalforecast

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Hierarchical Methods Time series forecasting

Installation

To install this package, run one of the following:

Conda
$conda install conda-forge::hierarchicalforecast

Usage Tracking

1.3.1
1.3.0
1.2.1
1.2.0
1.1.0
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Total downloads: 0

Description

HierarchicalForecast offers a collection of reconciliation methods, including BottomUp, TopDown, MiddleOut, MinTrace and ERM.

  • Classic reconciliation methods:

    • BottomUp: Simple addition to the upper levels.
    • TopDown: Distributes the top levels forecasts trough the hierarchies.
  • Alternative reconciliation methods:

    • MiddleOut: It anchors the base predictions in a middle level. The levels above the base predictions use the bottom-up approach, while the levels below use a top-down.
    • MinTrace: Minimizes the total forecast variance of the space of coherent forecasts, with the Minimum Trace reconciliation.
    • ERM: Optimizes the reconciliation matrix minimizing an L1 regularized objective.

PyPI: https://pypi.org/project/hierarchicalforecast/

About

Summary

Hierarchical Methods Time series forecasting

Information Last Updated

Nov 13, 2025 at 18:06

License

Apache-2.0

Total Downloads

18.7K

Platforms

noarch Version: 1.3.1