Answers.Body: <p>You need to include optimizer you used to make sure it is correct.
  By the way, your drop-out layers are not going to do anything, so you should take them away. </p>
  
  <p>You likely don’t have test and train data in time-series because all data points are connected. It just has prediction value and ground truth of each period. </p>
  
  <p>I recommend you use the whole dataset and rotate changing hyper-parameters of LSTM to find the best model. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-04T21:37:35.400Z
Answers.LastActivityDate: 2020-04-04T21:37:35.400Z
Answers.UserDisplayName: Quan Le
Body: <p>I think this model is underfitting. Is this correct?     </p>
  
  <p><a href="https://i.sstatic.net/MxSJd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MxSJd.png" alt="Model loss"></a></p>
  
  <p><a href="https://i.sstatic.net/yvOa6.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/yvOa6.png" alt="Prediction vs Real"></a></p>
  
  <pre><code>_________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  lstm_1 (LSTM)                (50, 60, 100)             42400     
  _________________________________________________________________
  dropout_1 (Dropout)          (50, 60, 100)             0         
  _________________________________________________________________
  lstm_2 (LSTM)                (50, 60)                  38640     
  _________________________________________________________________
  dropout_2 (Dropout)          (50, 60)                  0         
  _________________________________________________________________
  dense_1 (Dense)              (50, 20)                  1220      
  _________________________________________________________________
  dense_2 (Dense)              (50, 1)                   21        
  =================================================================
  </code></pre>
  
  <p>The above is a summary of the model.<br>
  Any advice on how the model could be improved?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-28T02:06:22.707Z
Comments.Text: I'd recommend renaming the Question Title to "Is this LSTM model underfitting?" rather than "Is this Keras LSTM model underfitting?" because there is nothing specific to keras in this question.
Comments.UserDisplayName: Saurav Maheshkar
Tags: <neural-networks><long-short-term-memory><overfitting>
Title: Is this LSTM model underfitting?
UserDisplayName: Compute0283
ThreadText: # Is this LSTM model underfitting?
  
  (Asked by: username_0 on 2020-04-04)
  
  I think this model is underfitting. Is this correct?     
  
  
  _________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  lstm_1 (LSTM)                (50, 60, 100)             42400     
  _________________________________________________________________
  dropout_1 (Dropout)          (50, 60, 100)             0         
  _________________________________________________________________
  lstm_2 (LSTM)                (50, 60)                  38640     
  _________________________________________________________________
  dropout_2 (Dropout)          (50, 60)                  0         
  _________________________________________________________________
  dense_1 (Dense)              (50, 20)                  1220      
  _________________________________________________________________
  dense_2 (Dense)              (50, 1)                   21        
  =================================================================
  
  The above is a summary of the model.
  Any advice on how the model could be improved?
  
  **Tags:** neural-networks, long-short-term-memory, overfitting
  > **Comments:**
  > **username_1**: I'd recommend renaming the Question Title to "Is this LSTM model underfitting?" rather than "Is this Keras LSTM model underfitting?" because there is nothing specific to keras in this question.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-04-04. Score: 1
  
  You need to include optimizer you used to make sure it is correct.
  By the way, your drop-out layers are not going to do anything, so you should take them away.
  
  You likely don’t have test and train data in time-series because all data points are connected. It just has prediction value and ground truth of each period.
  
  I recommend you use the whole dataset and rotate changing hyper-parameters of LSTM to find the best model.
  
  
  ---
  
  