I am currently building a LSTM model to predict only the daily Open price of a stock.

Is there a difference in the prediction of the Opening price if I include other parallel series (High, Low, Close, technical indicators etc) using a Multiple Parallel Series Model compared to using a univariate LSTM only on Open prices?

In other words, a Multiple Parallel Series LSTM with N series, just runs N different univariate LSTMs?

Thank you in advance!

The code for Multiple Parallel Series LSTM is from this tutorial: https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

Since you are predicting and can't take into account real world impacts like legal wins and losses along with market sentiment all these are fairly compared to junk science. I'd google a bit more and not put money down on such predictions. That said, there are those that hype and sell these stock market apps to help you pick the winners or if you are out to short a stock, the losers.

What has worked for me over the years are companies with a story. Microsoft, Cisco, Red Hat, Amazon, Apple were good bets I took. Today it's Tesla but your MPS LSTM won't get this one right as this one is the story of bears and competing industries. Old industries are going out of their way to maintain the status quo with FUD along with legal wranglings to stifle and slow the change.

If you are looking for this to predict the market, it's far too short of the mark.