Multiple correlated multivariate time series I have forecast dataset containing multiple multivariate time series that are not independent from each other. A state in one of the the series in time "t" can influence the state in another in time "t+n".
I was initially thinking in doing a LSTM model for each time series, but I ran in the problem of how to make a LSTM speak with another, is that possible? How could I do that?
I also thought in transforming the multiple series in a single one, but the goal is to make a forecast of the  class of each series and I don't think this way I could have the classification of each one as an output, right?
Are there any other solutions? Could I make a CNN-LSTM work for something like this?

For time series classification tasks it's usual to build one model trained on all the samples in your training set, rather than a separate model for each time series. The great multivariate time series classification bake off paper provides a summary and comparison of the latest multivariate classification algorithms, including some CNN (InceptionTime, ResNet, both available in the sktime-dl python package) and CNN/LSTM (TapNet) ones.
