So I have 82 different sets of data, each with varying length where each point has one feature and a label (0 or 1).
I'm trying to use Keras LSTM to be able to predict the class of a point depending on the previous values before it.
Currently I've padded each set to be of the same length and plan on using a masking layer.
What I'm confused with after reading many examples is how I should reshape my data and supply the correct input shape to the model.
If each set is of length 55, and I want to consider the previous 5 (just throwing a number out there) points to predict the class at each step, how would I do that?
Should I manually use a moving window to break each set into arrays of length 5, then have
input_shape=(5, 11, 1)
since 55/5 = 11??
Is this the best approach? How would this work for the remaining 80+ sets?
Just to clarify:
say one of the 82 sets looks like this
[1.1, 4.8, ... 2.1] (length of 55) I want the model to predict the class of each point
[0, 1, ... 1] (x55) except for the padded points (-1).