Reconciling forecasts for hierarchical and grouped time series
نویسندگان
چکیده
Time series can often be naturally disaggregated in a hierarchical structure using attributes such as product type. For example, the total number of bicycles sold by a cycling warehouse can be disaggregated into a hierarchy of bicycle types. Such a warehouse will sell road bikes, mountain bikes, children bikes or hybrids. Each of these can be disaggregated into finer categories. Children’s bikes can be divided into balance bikes for children under 4 years old, single-geared bikes for children between 4 and 6 and multi-geared bikes for children over the age of 6. Hybrid bikes can be divided into city, commuting, comfort, and trekking bikes; and so on. Such disaggregation imposes a hierarchical structure. We refer to these as hierarchical time series.
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Fast computation of reconciled forecasts for hierarchical and grouped time series
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