title: Tensorflow LSTM: poor performance of the multivariate model compared to the univariate and analyzing the Influence of variables on the target var
body: <p>I'm building two LSTM using Tensorflow:</p>
  <p>1 - Univariate: Time series with only the target variable</p>
  <p>2 -Multivariate: Time series with several other variables, including the target.</p>
  <p>However, the multivariate model, contrary to what I thought, does not perform better than the univariate one.</p>
  <p><strong>Question 1:</strong> Can this be explained by the fact that the variables used in the multivariate model have low and very low correlation?</p>
  <p>Therefore, I thought it would be interesting to analyze the Influence of the various variables on the target variable.</p>
  <p>I tried to use this code, but it doesn't return what I'm looking for.</p>
  <pre><code>model.trainable_variables
  </code></pre>
  <p><strong>Question 2:</strong> Is it possible to verify on tensorflow how the weights of the variables intervene in the construction of the ANN by identifying the input variables that have a greater impact on the result? Something similar that happens with the <a href="https://www.researchgate.net/post/Garson-algorithm-in-ANN" rel="nofollow noreferrer">method of Garson</a></p>