hierarchicalforecast (conda): **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/](https://pypi.org/project/hierarchicalforecast/)
Keywords: bottomup|coherent|econometrics|erm|forecasting|forecasting-models|hierarchical|hierarchical-forecast|middleout|mint|permbu|probabilistic-models|python|reconciliation|statistics|time-series|time-series-forecasting|timeseries|topdown