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I am trying to create a neural network using time series as input, in order to train it based on the type of each series. I read that using RNNs you can split the input into batches and use every point of the time series into individual neurons and eventually train the network.

What I am trying to do though is use multiple time series as an input. So for example you might receive input from two sensors. (So two time series), but I want to use both of them in order to get a final result.

Also I am not trying to predict future values of the time series, I am trying to a get a classification based on all of them.

How should I approach this problem?

Is there a way to use multiple time series as an input to an RNN?

Should I try to aggregate the time series into one?

Or should i just use two different neural networks? And if this last approach is correct, if the number of time series increases wouldn't that be too computer intensive?

If you use multiple time series, how will the network react if for some reason for sample1 you have 5 series but for sample2 you have 4, (maybe because you have no data from last sensor). Is it necessary that if you start with 5 series, it should always be 5? Should you include a 5th time series for sample2 with fake averaged data i order to have all 5? – Ploo – 2017-10-13T23:42:32.673

1oh well there are different approaches to missing data. I would recommend you to use the value 0 when you have no values. It is often used when we don't have the whole sequence X_t but we still have to input a sequence of length t. It is called padding if you wish to know more about this. – Daerken – 2017-10-15T12:58:31.463