Mona, have you by any chance solved this issue?
I'm trying to do the same exact thing with similar data as yours except my output is also a sequence.
I got the add/concatonate layer idea from a post I read online and a paper which shows the architecture as shown below:
My architecture looks similar to yours as below:
My training code looks something like this (to show it needs 2 inputs to train):
net = trainNetwork({XTrain(1:510,:);Static_features(1:510,:)'},YTrain(1:510,:),lgraph,options);
I use the same lgraph object as plotted above and XTrain (for temporal inputs) and Static_features (for static inputs) and a single output "YTrain" that should be a sequence similar to XTrain with multiple cells (similar to your XTrain). This is a prediction network that predicts the next time step value (YTrain) from the previous time step (XTrain).
However, when I try to train the network I get this error (next line of error says: if sequenceInputLayer is present, it can't accept any other type of input layer):
Network: Incompatible layer types. The network contains layer types not supported with recurrent layers.
Detected recurrent layers:
layer 'bilstm1' (BiLSTM)
layer 'bilstm2' (BiLSTM)
layer 'bilstm3' (BiLSTM)
layer 'bilstm4' (BiLSTM)
and 1 other layers.
For static_input I use an imageInputLayer but, I tried every layer that exists in Matlab 2020a (apparently featureInputLayer is not avaiable in 2020a) and it still gives me the same error.
I searched for every post I believe and can't seem to find a tutorial nor an example showing how to achieve this (if it is possible). The closest thing I found was using CNN-RNN but, that is with images (frames of a video) and has a single input layer.
