I am new to neural networks and after some research i read about CNN and RNN neural networks. The data that i am having is multiple different time series of numbers.
So for example instead of input 1 being for example a single image to be classified it is a number of time series:
input 1: time series type A, time series type B, time series type C
input 2: time series type A, time series type B, time series type C
From these time series i would like to have two outputs. If a single time series looks abnormal or normal based on already classified training data for each time series type, and finally based on all time series a single result for the input, if it is normal or abnormal. (If any of the time series of the input are abnormal then the result is abnormal)
My questions are:
1) Should i use a CNN or a RNN for this problem? I read that RNNs are good in sequences, for example languages, but what about time series data. On the other hand i found other articles using CNN for time series classification.
2) How should i approach this problem? Should i use all time series as inputs on my neural network of choice? or should i use multiple neural networks for each time series type? Note here that each time series describes a different thing, but at the end i would like to have a single result based on all time series, for each input.