Collections of time series often exhibit hierarchical structures, which are linear constraints that allow higher levels of series to be disaggregated to lower levels. The property that the forecasts also add up corresponding to the aggregation structure is referred to as coherency.
Traditional approaches achieve coherency by forecasting a single level of time series and rearranging it to obtain forecasts of all levels based on the structure. In contrast to traditional approaches, forecasting reconciliation combines forecasts for all levels of time series in the collection.