I have some questions: 1. Can we save the cross validation model which does fit and predict? My point i want to use the fitted model to predict on new data. I know that we can just use fit() and then predict() but cross validation is super useful considering it can be trained on train set, and val and test set can also be given. So if the fitted model of cross validation can be done it would be great 2. As far as I can see from the documentation is that we can’t predict all of test df if horizon in lower than that. It can’t do the rolling prediction and we need to do it manually right? I saw that it takes much time for my work. Suppose I have train data with 20000 rows and has historical exog and future exog. So for the test set of 5000 rows i have to give futr dr and test data accordingly as i understand that predict uses the last date of dataframe given to predict. Is that correct assumption? And we have to give a rolling window of horizon for both test data and futr dr to the predict method, correct? Is there any other way? 3. Another thing is as we can use robust scaler or standard scaler do we need to inverscaling? Or nixtla does it by itself? Seeing that predict() or cross validation() returns dataframe can we see what data it returns? And what model weights it has?

1. You should be able to save a model after cross-validation. You can refer to this tutorial. 2. You can use cross-validation for that. When you specify

test_size

or

n_windows

this is done automatically. You can refer to this tutorial. 3. No need to manually scale or inverse scale your data. This is handled inside neuralforecast I hope this helps!

So saving the model after cross validation, can we use that to predict? On a new dataframe? Or again have to use cross validation?

You can use the saved model for predictions on a new dataset, yes!