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Narxnet with Many timesteps to one time step

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amr alanwar
amr alanwar on 12 Mar 2017
Commented: Greg Heath on 15 Mar 2017
Hi All,
I have sensor readings with high frequency for an hour for example and I would like to predict one value out of these input measurements. How can I use narxnet to do that?
I do not want to concatenate all the inputs in one array and feed it to the network as one time step. In short how can I implement Many time steps to one value using narxnet.
consider Input is 100,000 * 3 where 100,000 is time steps and 3 is the number of sensors.
output is 10
i.e each 10,000 reading correspond to one output.
Thanks in advance

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Accepted Answer

Greg Heath
Greg Heath on 13 Mar 2017
Edited: Greg Heath on 13 Mar 2017
1. Plot subsampled series so that you can see what range of subsampling rates makes sense.
2. Plot the autocorrelation function of the chosen subsampled series to obtain a subset of significant lags.
3. Given the chosen subsampled series and significant lags, the formidable problem is now reduced to a standard one.
Hope this helps.
Thank you for formally accepting my answer
Greg

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Greg Heath
Greg Heath on 14 Mar 2017
It is very hard to believe that subsampling 100,000 points will lead to irrelevance.
Greg
amr alanwar
amr alanwar on 14 Mar 2017
Thanks for follow up. I can do subsampling with a relevant subsampling. But I do not want to formulate one to one problem as it does not make sense. Until now I see that Matlab does not support timesteps in the input. right?
For example, The LSTM network in Keras expects the input data (X) to be provided with a specific array structure in the form of: [samples, time steps, features]. Thank you
Greg Heath
Greg Heath on 15 Mar 2017
Until now I see that Matlab does not support timesteps in the input. right?
> Yes.

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