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Large collections of time series data are commonly organized into structureswith different levels of aggregation; examples include product and geographicalgroupings. It is often important to ensure that the forecasts are coherent sothat the predicted values at disaggregate levels add up to the aggregateforecast. The growing interest of the Machine Learning community inhierarchical forecasting systems indicates that we are in a propitious momentto ensure that scientific endeavors are grounded on sound baselines. For thisreason, we put forward the HierarchicalForecast library, which containspreprocessed publicly available datasets, evaluation metrics, and a compiledset of statistical baseline models. Our Python-based reference framework aimsto bridge the gap between statistical and econometric modeling, and MachineLearning forecasting research. Code and documentation are available inhttps://github.com/Nixtla/hierarchicalforecast.

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python