Comments (2)
Great questions! Could you provide some more detail about the "product specific models" you'd like to train? Silverkite is univariate, but you can share basic information between forecasts using common regressors or hyperparameters.
For the second question, the ReconcileAdditiveForecasts
class can be used to reconcile forecasts to satisfy additive constraints, such as your additive product hierarchy. In brief, it takes the base (independent) forecasts and adjusts them to minimize forecast error, reconstruction error, and a few other terms. It also supports the standard bottom up and OLS methods. This is a newer feature so we don't have a tutorial yet, but you are welcome to try it out. See the docstrings and test cases for reconcile_forecasts.py
and hierarchical_relationship.py
.
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