Time series forecasting is a common problem in machine learning (ML) and statistics. Some common day-to-day use cases of time series forecasting involve predicting product sales, item demand, component supply, service tickets, and all as a function of time. More often than not, time series data follows a hierarchical aggregation structure. For example, in retail, weekly sales for a Stock Keeping Unit (SKU) at a store can roll up to different geographical hierarchies at the city, state, or country level. In these cases, we must make sure that the sales estimates are in agreement when rolled up to a higher level. In these scenarios, Hierarchical Forecasting is used. It is the process of generating coherent forecasts (or reconciling incoherent forecasts) that allows individual time series to be forecasted individually while still preserving the relationships within the hierarchy. Hierarchical time series often arise due to various smaller geographies combining to form a larger

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