What if we input sequence data to feedforward network?
A finite impulse response (FIR) RNN can be expressed as a directed acyclic graph (DAG), hence it can be represented as a FFNN. So, you could theoretically make an equivalent FFNN to an RNN in this situation.
In terms of performance between non-equivalent models, an RNN would probably be better because, as they are inherently designed for sequential data. In the case of weather stat prediction, the temporal nature of the sequence elements would need to be learned by a FFNN, whereas an RNN would have in-built knowledge of such relationships. The RNN would also be able to more easily take advantage of &quot;memory&quot; via the common gating mechanisms as seen in LSTMs and the like.