When we tune a model to optimize parameters for a strategy using past data, even if controlling for overfitting (checking out of sample performance) and refreshing the analysis from time to time, we are assuming the mechanism generating the past data was (is) static. At least if we are using a naive approach. My question is if there is something better than this naive approach.
As much as statistical models in time series have their dynamic counterparts (in which parameters generating the past data are assumed to be time-dependent and more weight is given to nearby information rather than to distant information), is there a dynamic counterpart to a (naive) model tunneling (and an associated mechanism to avoid overfitting)?
Can someone indicate literature? Best regards. LA.