I have limited experience with machine learning, I trained a few networks, but nothing out of the ordinary. I have the following problem but I am not quite sure how to approach it and I'm hoping to get some advice here.
I have an series of time dependent data (timestamp + value) which I want to use to predict the next couple of outcomes. Usually this problem could be solved by using an LSTM. Every few minutes I get a new datapoint which I want to store and also take into consideration for future predictions.
The problem I think I have is that I'd need to train the model every few minutes with changing input size (I want to train the model with the entire history at once, I don't know if that makes sense) and also I don't want to overfit the model over time.
I'm not sure which architecture or concept is suited best for such a problem, so I hope to find some advice here. Thanks!