# How can I prevent a Recursive Neural Network from performing extremely poorly after a few cycles?

I've trained a neural network that can predict the $$(n+1)^{th}$$ element in a sequence, given the $$n^{th}$$ element.

It does a pretty good job doing this, with very little error.

The problem emerges when I start using the network recursively. When I feed in the $$n^{th}$$ element, I get the $$(n+1)^{th}$$ element with very little error, as I said earlier. I feed the $$(n+1)^{th}$$ element (the prediction) back into the network to get the $$(n+2)^{th}$$ element. I do this multiple times to get whatever element I want to get.

The issue is that each time I feed in a past prediction to get a new prediction, the tiny error in the previous prediction is compounded. The error increases exponentially as I predict successive elements in the sequence.

This was a problem I anticipated before I even began training the model. I figured that I'd be able to make the model so accurate that this error buildup wouldn't be noticeable until the model got far into the predicted sequence. I severely underestimated the exponential error buildup, and I don't think it's possible for me to make my model more accurate than it already is.

I figured that this is a problem that many others would've faced in the past, but I haven't found any good solutions to the problem. Are there useful tricks/methods by which I can fix the issue?

Any help would be much appreciated.

Thank you.