I noticed that one small difference between Statsf...
# statsforecast
I noticed that one small difference between Statsforecast and MLforecast is that in cross validation, the MLforecast allows for refit param to take integer, whereas in statsforecast it is simply true / false, which means I either refit after every single step or I do not refit at all during the CV. • Is this by design ? • Is it possible to set up a daily refit in statsforecast same as in MLforecast, where I can simply set: refit = 24 ?
Copy code
cv_df = sf.cross_validation(
    df = df,
    h = h,
    level = [90],
    step_size = step_size,
    test_size = test_size_adjusted,
    # input_size = 90*96,
    n_windows = None,
    refit = False,
    fitted = True
Hey. The mlforecast implementation was added recently due to a feature request. Can you open a feature request here, please?