<!channel>: Yesterday we released our new 👑HierarchicalForecast library. 🔥 Show some support by giving us a 🌟 https://lnkd.in/eiCSKaPi.  Now you can reconcile forecasts for hierarchical problems in a simple way using statistical approaches such as Bottom Up, Top Down, Middle Out, Minimum Trace, and Empirical Risk Minimization. Moreover, you can achieve state-of-the-art results. See Benchmarks: https://lnkd.in/e8p3ntbM 🚀🚀🚀 Hierarchical 👑 forecasting is important where time series data can be grouped or aggregated at various levels. Classical examples include aggregation of sales from product level to brand levels or geographical aggregations from zip code to country. Since these categories are nested within the larger group categories, the collection of time series is said to follow a hierarchical structure.   🔥🔥🔥