title: lstm for prediction of future time series values with Keras
body: <p>I want to make prediction of future values for a multivariate time series. I use this code as the template, but modified the output activation to linear</p>
  
  <pre><code>https://gist.github.com/karpathy/587454dc0146a6ae21fc
  </code></pre>
  
  <p>The input data is a sequence of 8 dimensional vector of double values. The output is the same 8 dimensional vector one time period ahead of the input sequence. This is the reason for choosing the linear activation. </p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(512, input_dim=len(chars),return_sequences=True)) #minesh witout specifying the input_length
  model.add(LSTM(512, return_sequences=True)) #- original
  model.add(Dropout(0.2))
  model.add(TimeDistributed(Dense(len(chars))))
  model.add(Activation('linear'))
  model.compile(loss='mean_squared_error', optimizer='rmsprop')
  </code></pre>
  
  <p>I am new to Keras and RNN. Could someone confirm this design?</p>