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I am confused about how I choose the number of folds (in k-fold CV) when I apply cross validation to check the model. Is it dependent on data size or other parameters?
Blocked time series cross-validation is very much like traditional cross-validation. As you know CV, takes a portion of the dataset and sets it aside only for testing purposes. The data can be taken from any part of the original data, beginning, middle, end, etc. It does not matter where because you assume the variance is the same throughout. But since the time series data IS changing in some ...
time series - What is and why use blocked cross-validation? - Data ...
K-fold cross-validation trains k different models, each being tested on the observations not used in the learning procedure. , as you are not using the same model as in your reference case, neither the same test set. The approaches that you describe are different, although I would not recommend the cross-validation only. Indeed, consider cross-validation as a way to validate your approach ...