I'm trying to develop a stock predictor.
I'm using LSTM but I am unsure about the structure of the Neural Network. For example, I'm assuming that the Neural Network is a many-to-one since we have many inputs (i.e Open, Close etc) and one output (stock price).
By misunderstanding is coming with how to construct the nodes. For example, what input goes into the "Cell" (or node)? I.e does say 60 timestep mean 60 days of 'Open Price' are fed into the Neural Network at t and then 60 days of 'Close' into t + 1 until we use all input to produce an output?
If someone could explain the process of how LSTM are used with stock predictions that would be appreciated.