I have a labelled time-series dataset; for example, a sequential input length nn from kk sensors corresponds to class '1' (see below for n=50,k=2n=50,k=2). How can I correctly classify it?
If I use RNN with infinite input length, the input layer will have 2 units (2 channels), but what about the output? Label '1' applies to the full sequence, not just a single input.
Therefore, there are two related questions: 1) how do I design an output unit? It has to reflect the input of the full sequence, 2) how do I compute an error? It also needs to account for the error of the whole sequence.
If I use ANN with a sliding window, the problem remains virtually the same - different classes can have comparable values in the window, so the output must correspond to the full sequence.
In other words, how do I match a time series to just 1 number?
For example, there is an $n \times k$ array labelled '1' (this can be for example readings off $n$ sensors, which match 1 class, like a gesture: 'snap'), and another $m \times j$ array labelled '0', where $n$ is the number of features/input neurons and $k \neq j$ is the length/size of the input data. If I use an RNN to feedforward the first array I do it $k$ times, obviously. But how do I label it? Do I use a unity vector length $k$, get an error each time and them average out to get the sample error and backprop? I don't think so, but not too sure.