Body: <p>I am implementing VAE based anomaly detection for multivariate timeseries using keras,
  I have ELBO (Evidence lower bound) which is combination of</p>
  <p><span class="math-container">$$-\ D_{KL}\left({\ q}_\varphi\left(z\middle| x^i\right)\parallel p_\theta\left(z\right)\ \right)\ +\ \ E_{q_\varphi(z|x^i)}\left[\log{p_\theta\left(x\middle| z\right)}\right]$$</span></p>
  <p>The loss function is combination of minimizing the <strong>KL[Q(z|x)|p(z)]</strong> and maximizing <strong>log [P(X|z)]</strong>
  how do I implement the above loss function for the multivariate timeseries of shape (batch_size,time_steps,features) using Tensorflow ?
  appreciate any suggestions</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-02-15T15:02:08.847Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: vl_knd
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-02-28T20:52:59.083Z
Comments.Text: Hi yeah i am basically struggling with  the implementation part of Loss function for VAE and my data is multivariate timeseries data.
Comments.UserDisplayName: mmv_87
Tags: <keras><variational-autoencoder><anomaly-detection>
Title: VAE ( variational autoencoder) for timeseries anomaly detection ,
UserDisplayName: mmv_87
ThreadText: # VAE ( variational autoencoder) for timeseries anomaly detection ,
  
  (Asked by: username_0 on 2024-02-04)
  
  I am implementing VAE based anomaly detection for multivariate timeseries using keras,
  I have ELBO (Evidence lower bound) which is combination of
  
  $$-\ D\_{KL}\left({\ q}\_\varphi\left(z\middle| x^i\right)\parallel p\_\theta\left(z\right)\ \right)\ +\ \ E\_{q\_\varphi(z|x^i)}\left[\log{p\_\theta\left(x\middle| z\right)}\right]$$
  
  The loss function is combination of minimizing the **KL[Q(z|x)|p(z)]** and maximizing **log [P(X|z)]**
  how do I implement the above loss function for the multivariate timeseries of shape (batch\_size,time\_steps,features) using Tensorflow ?
  appreciate any suggestions
  
  **Tags:** keras, variational-autoencoder, anomaly-detection
  > **Comments:**
  > **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  > **username_0**: Hi yeah i am basically struggling with  the implementation part of Loss function for VAE and my data is multivariate timeseries data.  
  
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  ## 0 answers
  
  