title: LSTM - Use deltaTime as a feature? How to handle irregular timestamps?
body: <p>I'm trying to create a LSTM for classification of data sequences.</p>
  
  <p>The data structure of every training input that I would use is:</p>
  
  <pre><code>[[
  [deltaX,deltaY,deltaTime],
  [deltaX,deltaY,deltaTime],...
  ],class]
  </code></pre>
  
  <p>Where deltaX and deltaY reflect the change of X and Y in a given time deltaTime.
  <br>deltaTime is not the same everytime, it can vary from 40ms to 50ms to sometimes 1000ms. The 'class' at the end is a binary classification, that can be either 0 or 1.</p>
  
  <p><strong>Question 1 (regular LSTM):</strong> Should I include deltaTime as a feature input if I'll use a basic LSTM (Keras LSTM)?</p>
  
  <hr>
  
  <p>I know that LSTMs have timestamps, but these are fixed to one given length.  I have had a closer look at phasedLSTMs, but they seem to be more for 2 periodical timeframes which are shifted.</p>
  
  <hr>
  
  <p>The only thing I encountered that could suit are Time-Aware LSTMs. </p>
  
  <p><strong>Question 2 (Time-Aware LSTM):</strong> Would you recommand them for my problem? If so: Is there a keras implementation, as I've not worked with tensorflow before.</p>