Hello - I think you will have to deal with this type of threshold as a post process, similar to what SAS Forecast Studio does for constraining forecasts to non-negative values. First create the models and then set the forecasts to 0 if predictions are negative. The idea is to model "unconstraint" and apply constraints afterwards. If you like to avoid "exploding" models you may have to come up with your own model repository. Example: if your models feature a LOG transformation, the predictions might be behaving in an unexpected manners, as we have to back-transform the predictions. If you data is limited on lower levels, you may decide to only create statistical models on higher aggregations and reconcile these predictions down to the level you need. This can happen using profiles for example. Thanks, Udo
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