I'm new to LSTMs, and I'm trying to do a basic timeseries prediction using stock prices. However, I'm a bit confused as to how the LSTM is supposed to remember outputs from previous timesteps when it has a many to one shape.

For example, let's say we're at timestep n, and the following timeseries is part of my input:

[[100, 10], [300, 30], [200, 20]]

And it maps to some output, let's say 1

Great. But let's say at timestep n - 1, when the input was just [[100, 10], [300, 30]], the output was 0. How will the LSTM know this?

Should I include the same data at different timesteps (using something like zero padding) with the corresponding output? Or am I totally misunderstanding something about how LSTMs work?



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.