I am trying to understand the advantage of a RNN has over a regular FF NN that is fed all the time series data.
It seems that advantage a RNN has over a feed forward network if you wanted to train the network after an initial training, with new datasets, if would be computationally much more efficient, since the state is saved. Where as you in a FF NN, you would need change the structure of the network itself, and train over all the time steps again.
But as for having one set of time-series data, I don't see how a RNN would have an advantage, since the neurons in a FF NN can learn relationships between the data points. My intuition doesn't how how the structure of a RNN would do this more efficiently.
I have searched questions and the closest thing I can find is this one Mathematical justification for using recurrent neural networks over feed-forward networks
Which gives some great thoughts related to the question I asked, but doesn't give an exact answer to my question.