title: Recurrent Neural Network Training and Test Sets
body: <p>I'm constructing an rNN optimized by a GA. This happens to be my first project in this area, so  I got confused by certain things. It would be great if someone could help me out with understanding them better.</p>
  
  <p>First of all, training and test data sets look quite different for ANNs and rNNs. If, for example, I'm predicting time series, I have a set of n observations. How do I go about using it? I mean, do I need to have n input neurons or 1 input neuron? How to I calculate an error? Every generation or every k'th generation? Do I have to average over k (i.e. the fitness function is going to be mean squared error over k generations).</p>
  
  <p>After n generations I reach the end of the sample, do I need to replicate it a number of times? In such case, what do I use as a test set, the same sample or a different one?</p>
  
  <p>I understand it's a lot of questions, but I'd be grateful if someone could help me out with at least some of them.</p>