I'm working on a project where the dataset contains time series of three classes, depending on the shape of the series. I want to learn the representations of these series as vectors, so naturally I use AutoEncoder for the task (precisely, I use LSTM-AutoEncoder to better handle the sequential data).
My question is: should I train one model for all classes or one model for each class? If possible, could you also point out what are the pros and cons of each approach? One thing that worries me about the latter approach is that the AE will simply memorize the data without any learning (again, would that be a concern?)
Thank you very much in advance!