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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.

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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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