HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
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Large collections of time series data are commonly organized into cross-sectional structures with different levels of aggregation; examples include product and geographical groupings. A necessary condition for coherent decision-making and planning, with such datasets, is for the dis-aggregated series' forecasts to add up exactly to the aggregated series forecasts, which motivates the creation of novel hierarchical forecasting algorithms. The growing interest of the Machine Learning community in cross-sectional hierarchical forecasting systems states that we are in a propitious moment to ensure that scientific endeavors are grounded on sound baselines. For this reason, we put forward the HierarchicalForecast library, which contains preprocessed publicly available datasets, evaluation metrics, and a compiled set of statistical baseline models. Our Python-based framework aims to bridge the gap between statistical, econometric modeling, and Machine Learning forecasting research. Code and documentation are available in https://github.com/Nixtla/hierarchicalforecast.
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