RNN for regression layers
<p>I want to train an RNN to solve a simple regression problem. I have an array <code>X_train</code> of shape <code>(35584,)</code> that represents measures each hour for several years. I also have the correspondent <code>Y_train</code> with shape <code>(35584,)</code> as the expected values. The values of an hour <code>h</code> are affected by values of, say, the previous 6 hours, so I want to use a RNN.</p>

<p>In Keras, I understand that for my case: <code>timesteps = 6</code> and <code>nb_samples = 35584</code>. In my case, <code>nb_features = 1</code>. </p>

<p>How can I program this in Keras? Should I use <code>Embedding</code> layer? And how to do it?</p>