RNN using multiple time series 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?
Multivariate time series is an active research topic you will find a lot of recent paper tackling the subject. 

To answer your questions, you can use a single RNN. You can input one value for each time step. Nothing keeps you from adding another value at each time step (if your sensor are synchronized). Your model will then learn how to classify with a two dimensional time series.

You check this blog. In your case, only the output is different.

As for the two last points, aggregating the time series into one is risky in the sense that you might lose important information during the process. Finally the main disadvantage of your last point is that you won't be able to use a potential correlation between the two time series for the final classification.