I working on a classification problem that needs to detect patterns on a time serie. Basically, there's a catch-all class that means "no pattern detected", the other are for the specific patterns. The data is imbalanced (ratio 1/10 at least), but I adapted the class weights.
I'm able to overfit successfully on a few days of data, but when I train on 2 years of data, the model seems stuck on class1 "no pattern detected" for a veeeery long time. I've tried several learning rates, but it doesn't make the convergence happen significatively faster.
Is it a better starting point for my training to use the overfitting model's weight as a starting point? Could this allow the model to converge faster?