IF I am using cross validation for neuralforcast

Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,
                               test_size=test_size, n_windows=None)

in the librery said we can choose between the test size,val size or n-widows so we are divide the data to two train and test right not to three train and test and validation

hey @marah othman! Thanks for using NeuralForecast. The

val_size

argument of the

cross_validation

method in neuralforecast is used by the

Auto

models to perform hyperparameter optimization. The

test_size

and

n_windows

are interchangeable and work the same as in StatsForecast and MLForecast.

so if i am doing cross validation for non AUTOModels i dont need to valdation size right ?

Yes, exactly, if you’re not using the auto models you can skip defining

val_size

🙂

okay thanks