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My question is only with regards to the feedforward part of an RNN. I am following these steps.

I am working on prediction of a time series. The time series is a toy model generated by me. It is composed by 200 numbers: 150 for train and 50 for validation. Given a sequence of 50 numbers, it should predict the 51st number. If x1=1,2,....50, then y1=51. If x2=2,3,.....51, then y2=52 and so on. I have 100 inputs and 100 outputs.

I don't understand how this sequence is related to the simple architecture of an RNN. In this architecture the hidden(t) is obtained by input * hiddenmat * hiddenmat(t-1). Do I have to sequentially feed each input to the different RNNs extended in time and calculate all the outputs with the global loss over the time span? Then I need 100 input neutrons? Given an input sequence length, what is the number of input neurons I need?

Thank you for your help! It seems a silly question but I got stuck conceptually on this.

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