are we using the expanding window or without expanding i mean the training data it will be the same size for each fold ?

The training data is not moving, is fixed to end at the beginning of the validation set. What is moving is the input, so the model is using the latest data to make predictions (without updating parameters).

are you talking about cross validation ?

yes, is none of the images you showed

i am sorry but i cant understand how it works

Training and using data as inputs are different processes. When training the model we update its parameters, and only the data before the validation period is used. The parameters of the model are fixed during validation.

does this behavior change when we put

refit_with_val=True

?

@Bradley de Leeuw we dont have the

refit_with_val

argument

AutoNHITS has (had?) that parameter and I used it in combination with

NeuralForecast.cross_validation

with the following settings:

h=15
step_size=15
n_windows=4 
freq="W"
refit_with_val=True

So if I understand correctly, the last 60 weeks of data are never used to update the parameters of the model in this case? It simply trains on

n - (4*15)

datapoints and only the input data changes over the 4 folds.

the

refit_with_val

is for training the best model afterwards. If true, it will include the validation set. But this is not part of the

cross_validation

Got it, thanks! Out of curiosity, what’s the reason for not (optionally) refitting the model during crossval? In real life, I’d argue updating the parameters with some frequency e.g. every month, or whatever is best for your model makes sense right? This would also make it easier to do backtesting and mimic a real world scenario where you e.g. retrain every 4 weeks as new data comes in. PS: is this functionality the same in StatsForecast and MLForecast’s cross_validation?