Nixtla's Open Source Time Series Ecosystem.

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Hi <@U05S70V5AJD>! Your support means a lot to us :slightly_smiling_face:

`cross_validation` is faster because it is not doing daily re-training. In the case of the auto model, it first do hyperparmater selection to select the best configuration. Then it uses the best model to forecast all the 3000 days. You can see it as an efficient batched for loop to make multiple predictions on your test set

If you want to test the model with re-training, then you need to do a for loop, like the one you already implemented

Got it and thanks. One more questions is that if I want to use one model for the next 250 days is there a way to mimic the batch prediction as in the cv function? In my trial of doing the for loop to deal with the daily prediction it was extremely slow even without refitting the model. Just like in other RNN or tabular ML I can feed in all the data for a batch prediction.