BottomUp
,
TopDown
,
MiddleOut
,
MinTrace
and
ERM
,
as well as probabilistic coherent predictions including
Normality
,
Bootstrap
,
and
PERMBU
.
π Features
- 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.
- Probabilistic coherent methods:
Normality
: Uses MinTrace variance-covariance closed form matrix under a normality assumption.Bootstrap
: Generates distribution of hierarchically reconciled predictions using Gamakumaraβs bootstrap approach.PERMBU
: Reconciles independent sample predictions by reinjecting multivariate dependence with estimated rank permutation copulas, and performing a Bottom-Up aggregation.
- Temporal reconciliation methods:
- All reconciliation methods (except for the insample methods) are available to use with temporal hierarchies too.

π Why?
Short: We want to contribute to the ML field by providing reliable baselines and benchmarks for hierarchical forecasting task in industry and academia. Hereβs the complete paper. Verbose:HierarchicalForecast
integrates publicly available
processed datasets, evaluation metrics, and a curated set of statistical
baselines. In this library we provide usage examples and references to
extensive experiments where we showcase the baselineβs use and evaluate
the accuracy of their predictions. With this work, we hope to contribute
to Machine Learning forecasting by bridging the gap to statistical and
econometric modeling, as well as providing tools for the development of
novel hierarchical forecasting algorithms rooted in a thorough
comparison of these well-established models. We intend to continue
maintaining and increasing the repository, promoting collaboration
across the forecasting community.
π» Installation
PyPI
We recommend usinguv
as Python package manager, for which you can
find installation instructions
here.
You can then install the released version of HierachicalForecast
:
Conda
Also you can install the released version ofHierarchicalForecast
from conda with:
Dev Mode
If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps here.𧬠How to use
The following example needsstatsforecast
and datasetsforecast
as
additional packages. If not installed, install it via your preferred
method, e.g.Β pip install statsforecast datasetsforecast
. The
datasetsforecast
library allows us to download hierarhical datasets
and we will use statsforecast
to compute base forecasts to be
reconciled.
You can open this example in Colab