Let’s say for a given problem we have a big stable model that uses a lot of data to train – let’s mark it as model A. We will also devise another model, a more lightweight model that trained on smaller and more recent data – it can have the same type. We’ll call it model B.
The idea: Find the time windows where model B outperforms model A. As model A is stable and encapsulates more data than model B, we would expect it to outperform it. However, if model B outperforms model A that might suggest that a concept drift has occurred.