<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>

CC BY-SA 4.0
2020-04-04T21:37:35.400Z
2020-04-04T21:37:35.400Z
Quan Le
<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>

CC BY-SA 4.0
2021-08-28T02:06:22.707Z
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.
Saurav Maheshkar
<neural-networks><long-short-term-memory><overfitting>
Is this LSTM model underfitting?
Compute0283
# 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.  

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## 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.


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