I am building a multivariate time series prediction. I want to train and use the network with fixed length series of n events. I know that I could use a RNN for this. What I do not understand though is why this should perform any better than a regular deep network, e.g. with a 1d convolutions. I understand that LSTMs and GRUs have a hidden state. However, if I always use a fixed length series of events, this hidden state should be lost after each prediction. I guess I could keep it, but I am not interested in this because I only want to take the given n events into consideration. So, if the hidden state is lost after each prediction, I do not see why a RNN should help at all. Is not this just limiting my network from learning to detect even more powerful patterns?
Bonus: Apart from TCNs (Temporal Convolutional Networks), are there other archtectures which I should explore?
Edit: To use a regular network with a 3d tensor, I would just reshape it first.