Answers.Body: <p>You can try to work with Gated Recurrent Units or GRU. This will solve your problem of with too much latency time that LSTM required. LSTM also doesn't give preference on newer data too. For more information, you can follow more on this great article</p>
  
  <p><a href="https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be" rel="nofollow noreferrer">GRU Cells</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-12T19:09:40.440Z
Answers.Text: How do GRUs help labelling the dataset?
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-12T15:15:12.047Z
Answers.LastActivityDate: 2020-04-12T15:15:12.047Z
Answers.UserDisplayName: techPirate99
Body: <p>I have a big amount of light curves (image below). </p>
  
  <p><a href="https://i.sstatic.net/aKmwP.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/aKmwP.png" alt="enter image description here"></a></p>
  
  <p>I am trying to label the points as signal or background (the signal appears usually periodically, several times, for a given light curve). </p>
  
  <p>More precisely, I want to identify the downward spikes (class label = 1) from the background (class label = 0).</p>
  
  <p>However, the data is not labeled. I tried labeling it by hand, and using a bi-directional LSTM succeeds in labeling the data points properly. However, there are thousands of light curves and labeling all of them would take very long. </p>
  
  <p>Is there any good unsupervised approach to do this (unsupervised LSTM maybe, but any other method that might work on time series would do just fine)? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-04-28T22:03:55.390Z
Comments.Text: It sounds like you’ve labeled some of the data by hand. Can you use a clustering algorithm trained on the small amount of data you’ve already labeled? If the dataset is quite imbalanced, you may be able to separate the data into two clusters and check some of the results for signal by hand? Or maybe you can set it up as an “anomaly detection” network, which you can find many example implementations of online. That way you could separate signal (which would appear as anomaly) from noise (which seemingly dominates the signal).
Comments.UserDisplayName: Hanzy
Tags: <long-short-term-memory><datasets><unsupervised-learning><data-preprocessing>
Title: Is there an LSTM-based unsupervised learning algorithm to label a dataset of curves?
UserDisplayName: Alex Marshall
ThreadText: # Is there an LSTM-based unsupervised learning algorithm to label a dataset of curves?
  
  (Asked by: username_0 on 2019-04-24)
  
  I have a big amount of light curves (image below).
  
  [![enter image description here](https://i.sstatic.net/aKmwP.png)](https://i.sstatic.net/aKmwP.png)
  
  I am trying to label the points as signal or background (the signal appears usually periodically, several times, for a given light curve).
  
  More precisely, I want to identify the downward spikes (class label = 1) from the background (class label = 0).
  
  However, the data is not labeled. I tried labeling it by hand, and using a bi-directional LSTM succeeds in labeling the data points properly. However, there are thousands of light curves and labeling all of them would take very long.
  
  Is there any good unsupervised approach to do this (unsupervised LSTM maybe, but any other method that might work on time series would do just fine)?
  
  **Tags:** long-short-term-memory, datasets, unsupervised-learning, data-preprocessing
  > **Comments:**
  > **username_1**: It sounds like you’ve labeled some of the data by hand. Can you use a clustering algorithm trained on the small amount of data you’ve already labeled? If the dataset is quite imbalanced, you may be able to separate the data into two clusters and check some of the results for signal by hand? Or maybe you can set it up as an “anomaly detection” network, which you can find many example implementations of online. That way you could separate signal (which would appear as anomaly) from noise (which seemingly dominates the signal).  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2020-04-12. Score: -1
  
  You can try to work with Gated Recurrent Units or GRU. This will solve your problem of with too much latency time that LSTM required. LSTM also doesn't give preference on newer data too. For more information, you can follow more on this great article
  
  [GRU Cells](https://towardsdatascience.com/understanding-gru-networks-2ef37df6c9be)
  
  > **Comments:**
  > **username_3**: How do GRUs help labelling the dataset?  
  
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