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I am training a stacked LSTM that takes as input a sequence [1,...,n] and outputs a sequence [1,...,m], for m<n, to predict stock prices.

Upon training the model with multiple architecture nuances, I am getting the same result: the yhat sequences all have the same pattern.

What might be causing this?

*All input columns are z-scored

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This is probably because there is no benefit is trying more nuanced architectures anyway. Stock prices are usually incredibly noisy data, neural networks excel when you have data with a complex structure yet not noisy (e.g. images, text, speech). Stock prices are the opposite, simple structure a lot of noise.

I would advise trying to model your data with something more simple, such as ARIMA or exponential smoothing.

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