After reading a while, I am confused now about my LSTM data structure. Assuming that I have a supervised learning problem with 1000 samples and 40 features as input. Now I want to create 10 timesteps of x. My resulting dimension of Keras data structure is
x: (1000, 10, 40)
with every two-dimensional matrix (1000, 40) shifted row-wise and ten times.
The question is now:
Which dimension has my target y to be?
My dimension is
y=[1000,1] for one resulting target, but I also read
y=[1000,10,1]. I suppose, the second dimension with value 10 should be the row-wise shifted target-vector. But, do I really need this? Shouldn't my solution be the right one?