title: How to process panel data for use in a recurrent neural network (RNN)
body: <p>I have been doing some research on recurrent neural networks, but I am having trouble understanding if and how they could be used to analyze panel data (meaning cross-sectional data that is captured at different periods in time for several subjects -- see sample data below for example).Most examples of RNNs I have seen have to do with sequences of text, rather than true panel data, so I'm not sure if they are applicable to this type of data.</p>
  
  <p>Sample data:</p>
  
  <pre><code>ID    TIME    Y    X1    X2    X3
  1     1       5     3     0    10
  1     2       5     2     2    6
  1     3       6     6     3    11
  2     1       2     2     7    2
  2     2       3     3     1    19
  2     3       3     8     6    1
  3     1       7     0     2    0
  </code></pre>
  
  <p>If I want to predict Y at a particular time given the covariates X1, X2 and X3 (as well as their values in previous time periods), can this kind of sequence be evaluated by a recurrent neural network? If so, do you have any resources or ideas on how to turn this type of data into feature vectors and matching labels that can be passed to an RNN (I'm using Python, but am open to other implementations).</p>