Answers.Body: <p>So it seems any time series modeling would do the trick. If you new to neural nets and seems you want to play with keras, maybe start by throwing it into a simple LSTM.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-31T19:25:25.697Z
Answers.LastActivityDate: 2019-05-31T19:25:25.697Z
Answers.UserDisplayName: mshlis
Body: <p>I have a problem where I have 9 data points that are collected every minute for 40 minutes, and, by the 40th minute, the solution would be either end up being black or white.</p>
  <p>I would like to set up a neural network, which would take the live input of every minute; and I was hoping within the 25-30 minute mark to predict the outcome of what the results would be at 40 minute; which is a classification.</p>
  <p>I have over 3000 historical runs of this experiment; each containing 40 rows of 9 columns data per experiment.</p>
  <p>What network would I need to set up; so that it can learn from each run at every minute mark per experiment with the results; and then set it up for live input, when the experiment is running again.</p>
  <p>I feel like I might need more than one system to accomplish this; any help in pointing me towards the right path would be greatly appreciated</p>
  <p>I am using python (keras) to try to solve this problem.</p>
  
Tags: <neural-networks><long-short-term-memory><keras><time-series>
Title: What type of network for a repeated experiment
UserDisplayName: Rad D
ThreadText: # What type of network for a repeated experiment
  
  (Asked by: username_0 on 2019-05-31)
  
  I have a problem where I have 9 data points that are collected every minute for 40 minutes, and, by the 40th minute, the solution would be either end up being black or white.
  
  I would like to set up a neural network, which would take the live input of every minute; and I was hoping within the 25-30 minute mark to predict the outcome of what the results would be at 40 minute; which is a classification.
  
  I have over 3000 historical runs of this experiment; each containing 40 rows of 9 columns data per experiment.
  
  What network would I need to set up; so that it can learn from each run at every minute mark per experiment with the results; and then set it up for live input, when the experiment is running again.
  
  I feel like I might need more than one system to accomplish this; any help in pointing me towards the right path would be greatly appreciated
  
  I am using python (keras) to try to solve this problem.
  
  **Tags:** neural-networks, long-short-term-memory, keras, time-series
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-05-31. Score: 0 | Accepted answer
  
  So it seems any time series modeling would do the trick. If you new to neural nets and seems you want to play with keras, maybe start by throwing it into a simple LSTM.
  
  
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