One main advantage of RNN is the ability to take input of variable length like the case of sequences. However, what if we neglected this case and assumed some applications that may accept some fixed window of input. For example, the weather prediction. We may input the temperature of the last 300 days for example to allow the network to predict the new temperature. We may fix this window of 300 all the time so that we have a fixed-length input.
My question now is what if we neglect the RNN in this case and input this window of 300 days into a feedforward network with 300 input dimensions and one output that is doing regression for it? What will be the output as compared to RNN? In other words, why I should use RNN in this case?