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I have a high frequency time series data that I want to fit into a CNN network for identification work. However I need to decide which frequency I would want to look at, so a typical way to do it is :

time_step = 10
A = cumsum(input_data)
B = A[0 : len(A): time_step].
C = B[1:] - B[:len(B)-1]

,which samples at every 10 time points.

However, I wonder if I can make a Keras customer layer that the time_step is a parameter of the network? So that the network would tell me what is the optimal value for time_step parameter.

Jack2019
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  • Could you please explain what is the sampling rate within this context? Is the idea about selecting timepoints separated by p timesteps? – rvinas Dec 10 '18 at 10:39
  • @rvinas Yes I want the network to learn the optimal interval P. – Jack2019 Dec 10 '18 at 21:31

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