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.
Max is the CEO and Co-Founder of Nixtla, a time-series research and deployment startup. He is also a seasoned entrepreneur with a proven track record as the founder of multiple technology startups. With a decade of experience in the ML industry, he has extensive expertise in building and leading international data teams. Max has also made notable contributions to the Data Science field through his co-authorship of papers on forecasting algorithms and decision theory. In addition, he is a co-maintainer of several open-source libraries in the Python ecosystem. Max's passion lies at the intersection of business and technology.