Okay thank you for the feedback! That could be interesting! Model 1 is a Random Forest model and uses different input than the LSTM, and at the moment I think for my skill level it may be a too big of a hassle to make the models predict simultaneously. Or what is meant with stacking the models if I may ask?

Yeah! Currently what I do is that Model 1 makes predictions based on certain input features making predictions timestep for timestep. The LSTM model uses the predicted sequence together with other variable sequences to make the predictions more robust and stable, as well as more making it have more correct trends. Atleast, that is the idea.

Perhaps I should elaborate that the predicted sequence made by model 1 is not the only sequence of the LSTM model. I also use different variable sequences for which I hope the LSTM uses these to understand the correct trends.