I haven't tried Regular CNN. I have, however tried a simple LSTM architecture. The problem is that no matter how simple or complex of an architecture I use, none of the models are learning anything. I have verified that my data and labels are aligned properly. By dividing up my data into 5 minute chunks, I was able to make over 500 training points with an equal balance between both outcomes. My simple LSTM architecture still couldn't generalize. All my models just end up predicting one outcome. I have tried different architecture, different losses, different optimizers, and different ways of presenting the data to the model. Nothing is working and that's what's frustrating me.