Hierarchical Forecasting in Python

A vast amount of time series datasets are organized into structures with different levels or hierarchies of aggregation. Examples include categories, brands, or geographical groupings. Coherent forecasts across levels are necessary for consistent decision-making and planning. In this talk, we will introduce the open-source HierarchicalForecast library, which contains different reconciliation algorithms, preprocessed datasets, evaluation metrics, and a compiled set of statistical baseline models. This Python-based framework aims to bridge the gap between statistical modeling and Machine Learning in the time series field.