New Forecast plugin - select the best model to predict for each identifier

AngelsF Registered Posts: 6 ✭✭✭✭


I have been testing the new Forecast plugin. I must say that it is really fast and has several good models to predict with no need to partition the data. Congratulations for that!

I have two doubts about it. I want to predict the monthly sales for many stores and in the first recipe (train and evaluate forecasting models) I configure all the requested information. I select "Expert-Choose Algorithms" as the Forecasting mode and all the available Statical and Deep Learning models. It seems that for some specific stores the AutoARIMA model fails:

Error in Python process: At line 63: <class 'gluonts_forecasts.model.ModelTrainingError'>: GluonTS 'autoarima' model crashed during training. Full error: Seasonality of AutoARIMA can't be set to 12. Error when testing seasonality with nsdiffs: shapes (4,2) and (1,) not aligned: 2 (dim 1) != 1 (dim 0)

So the whole execution fails.
I'm wondering if there is any way to avoid the failure for the rest of stores. Or as it is a "unique" process, the solution would be to deselect the AutoARIMA model.

On the other hand, in the second recipe (Forecast future values) I select the "Automatic" selection mode and the MAPE as Performance metric (in the first recipe I had chosen Expert - Choose Algorithms and all the models except AutoARIMA). When I see the results I realise that all the stores have been predicted with the FeedForward model. I imagine this is so because in the aggregated performace metrics the winner is indeed FeedForward. Am I right?


But in fact, I would like the behaviour was to select the best model for each store, not in the overall, as each one is predicted indepently. Is that possible?

Let me know if you need any further information.

Thank you very much in advanced.


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