Keras LSTM Input Dimension understanding each other

Question

but I have been trying to play around with it for awhile. I've seen a lot of guides on how Keras is used to build LSTM models and how people feed in the inputs and get expected outputs. But what I have never seen yet is, for example stock data, how we can make the LSTM model understand patterns between different dimensions, say close price is much higher than normal because volume is low.

Point of this is that I want to do a test with stock prediction, but make it so that each dimensions are not reliant on previous time steps, but also reliant on other dimensions it haves as well.

Sorry if I am not asking the question correctly, please ask more questions if I am not explaining it clearly.

Answer 1

First: Regressors will replicate if you input a feature that gives some direct intuition about the predicted input might be to secure the error is minimized, rather than trying to actually predict it. Try to focus on binary classification or multiclass classification, whether the closing price go up/down or how much.

Second: Always engineer the raw features to give more explicit patterns to the ML algorithm. Think on inputs as Volume(t) - Volume(t-1), close(t)^2 - close(t-1)^2, technical indicators(RSI, CCI, OBV etc.) Create your own features. You can use the pyti library for technical indicators.