How to apply neural pattern recognition to evaluate time-series data?
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I have a time-series dataset of a dynamic system that I would like to validate. So far that was done manually, so I have available a wide variety of trained data and I would like to check out, if it is possible to automatize this evaluation through a neural pattern recognition network.
For instance, I have a numeric array that represents longitudinal velocity and its time reference. By plotting it, it is quite easy to conclude, if the behaviour is what it should.
As you see, the main point is that instead of having a set of "scalar" variables like size or weight that represent my data, I have vectors. In other words, my input dataset for the neural network would be a 3D array (features x entries x timesteps) whereas my target variable is still a binary vector (ok or not ok).
Is that possible? Or should I just preprocess every vector to characterize it as scalar magnitudes such as maximum and minimum values, mean, standard deviation, etc?
2 Comments
ampaul
on 13 Jun 2017
Did you ever find your answer to this question? I am having the same trouble... Thank you
Answers (1)
Greg Heath
on 15 Jun 2017
You do not mention any dimensions. So it is hard to make an informed comment.
If you can use feature extraction to represent each input by a reasonably short vector,
then do so.
Hope this helps.
Thank you for formally accepting my answer
Greg
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