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?