I will use an example from your documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast#preparing-data
What is the difference between train two models per store or one model with store grain? Will be the accuracy of one model with grains increased due to a larger amount of the data, etc?
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Building one large model will often be better than one model per series, but not always. If the series are quite heterogeneous (exhibit different patterns, span orders of magnitude), then it makes sense to fit multiple models. If they are similar, one model will generally do well.
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Building one large model will often be better than one model per series, but not always. If the series are quite heterogeneous (exhibit different patterns, span orders of magnitude), then it makes sense to fit multiple models. If they are similar, one model will generally do well.