title: LSTM model approach for time series (future prediction)
body: <p>I am new to tensorflow/tflearn and deep learning so these may be basic questions but I would appreciate any input.</p>
  
  <ul>
  <li>Question 1:  I have been able to successfully run a LSTM model using tflearn on a set of 2 years of time series data/sequence.  I can run the model via variations of "look_back" (e.g. 1 day, 7 days, 30 days) but it will output a single value at each iteration.  Running the LSTM for a larger look back improves the RMSE of my test data set.  Question I have is, if my goal is to to predict the "next 30 days" given a set of historical daily values, how do I modify the model?  I presume I need to either modify my OUTPUT tensor to be a sequence or somehow feed the decoder output at each iteration to be the input to the next?  Or do I modify the model to output a full sequence?  I cannot find any clear example of how this may be done.  </li>
  <li>Question 2:  After a model is trained, how exactly do you productionize the model. Suppose in my case I trained/tested a model using a year of data to predict the next 30 days. How exactly can I now implement this so that as i get daily values they get integrated with the model. Again, any example of this would be great.</li>
  </ul>
  
  <p>I have tried to go through the tensorflow tutorials but I am not sure they address these points.</p>
  
  <p>Thanks</p>