I came across RNN's a few minutes ago, which might solve a problem with sequenced data I've had for a while now.
Let's say I have a set of input features, generated every second. Corresponding with these input features, is an output feature (also available every second). One set of input features does not carry enough data to correlate with the output feature, but a sequence of them most definitely does.
I read that RNN's can have node connections along sequences of inputs, which is exactly what I need, but almost all implementations/explanations show prediction of the next word or number in a text-sentence or in a sequence of numbers.
They predict what would be the next input value, the one that completes the sequence. However, in my case, the output feature will only be available during training. During inference, it will only have the input features available.
Is it possible to use RNN in this case? Can it also predict features that are not part of the input features?
Thanks in advance!