I am confused in using walking forward validation. I have found several pages that have different or not clear wording on "validation" and "test".
Basically, when I apply it to time series forecasting, I divide the data into training, validation and test set. The validation is then a part of the training set. I understood that. Obviously, for time series, time has to be taken into account, so we have to resort to the walking forward method. My understanding is that the walking forward method is only applied in the training set so that, for example, hyperparameters can be optimized when they are created. The test set plays no role and is only used at the end to evaluate the model. Is this correct? Or does the walking forward method split the test set? I see many examples that do not consider this forward method.
If it is the walking_forward method, it is the validation set: Another question is, if I don't split the validation and training set before the backpropagation and I choose in the "model.fit" the settings:
model.fit(
"data=X_train, y_train",
"validation_data = X_train, y_train",
"valdidation_split=0.1",
"shuffle=False")
is this model similar to the walking_forward method?
