title: Concatenate additional features after LSTM layer for Time Series Forecasting
body: <p>I have the following dataset:</p>
  
  <pre><code>                Feature 1   Feature 2 ...  Feature to Predict
  2015-01-01         1000          8                 12
  2015-01-02         1200          2                 22
  2015-01-03         4000          4                 51
  2015-01-04         2000          8                 33
  2015-01-05         1000          5                 14
  </code></pre>
  
  <p>I want to predict the last feature ("Feature to predict") at time <code>t + 1</code> using the previous <code>n</code> timestamps. To do this I used a multivariate <code>LSTM</code> that is trained using the data from <code>t-n</code> to <code>t</code>. </p>
  
  <p>The fact is that I also have the possibility to obtain the other features ( Feature 1, Feature 2 ...) for the time 't+1' that I want to predict.</p>
  
  <p>What I would like to do is to add these additional features after the <code>LSTM</code> layers and before the <code>Dense</code> layers and use them in the prediction of my 'Feauture to Predict'.</p>
  
  <p>Right now my code, without the additional features but only with the 't-n' to 't' features, looks like this:</p>
  
  <pre><code>mdl = Sequential()
  # create and fit the LSTM network
  mdl.addLSTM(neuronsl1,activation = 'tanh' ,return_sequences=True, input_shape=(lags,n_features))
  mdl.add(Dropout(0.2))
  mdl.addLSTM(neuronsl2,activation = 'tanh' , input_shape=(lags,n_features))
  mdl.add(Dropout(0.2))
  
  ---------&gt;&gt;&gt; At this point i would like to add the additional features at time 't + 1'
  
  mdl.add(Dense(neuronsl3))
  mdl.add(Dense(neuronsl4))
  mdl.add(Dense(1))
  </code></pre>
  
  <p>Any suggestions on how to do that?</p>