Multiple-output vs single-output NNs I'm trying to build a 5 input-5 output model using LSTM, where all the outputs are the same features as the inputs, predicted in the future.

My question is: is it better to build 5 models, each with the same 5 inputs, but predicting just 1 of the 5 sequences at a time, or is it the same as building 1 model predicting all 5 sequences?
In other words, is the accuracy per predicted sequence going to be higher with 5 separate models or will it be the same as 1 model with 5 outputs. 

The reason for my confusion is that, in the case of the multiple output model, the hidden layer will be the same; so how would the algorithm go about optimizing the weights so as to minimize error for all output sequences?
In general, you cannot know which is better until you try both with lots of data and evaluate them with a strong statistical test.
I know you won't like this answer but this is how neural networks work.
Hope this helped