title: keras lstm time series data
body: <p>I am trying to implement a lstm model with data measured once a day over time at different moments of the day.</p>
  
  <p>For example, let's say the last input of my data set has been measured on May 16, 2018. My data is like this:</p>
  
  <pre><code>        Velocity        Time 
  0        56.122         3600
  1        56.114         3601
  ...      ...            ...
  3599     75.043         7199
  </code></pre>
  
  <p>The time is in seconds. From 3600 to 7199 means from 1:00 am to 2:00 am.</p>
  
  <p>Let's say the input before has been measured the day before (May 15) from 00:00 to 00:15.</p>
  
  <pre><code>        Velocity        Time 
  0        6.232           0
  1        6.197           1
  ...      ...             ...
  899      5.507           899
  </code></pre>
  
  <p>The problem is I don't know how to deal with the 'Time' feature when I am creating my LSTM model.  </p>
  
  <p>At the moment, I have padded my data so that they all have the same shape. For example, for the input of May 15, I have now</p>
  
  <pre><code>        Velocity        Time 
  0        6.232           0
  1        6.197           1
  ...      ...             ...
  899      5.507           899
  900      -1              -1
  ...      ...             ...
  3599     -1              -1
  </code></pre>
  
  <p>I assumed in the example that 1 hour was the maximum time length of the inputs.</p>
  
  <p>Do I need to transform the time into a categorical data ? Because I normalized my data (not seen here) with sklearn.preprocessing.MinMaxScaler (I did it before the padding). If not, do I need to scale the time ?</p>
  
  <p>I have 11200 inputs. Each input (X_train) has a shape of (3600, 2). There is one output for each input (it's a boolean True or False). </p>
  
  <p>Thanks.</p>