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I am using N-Grams to predict future character input. For n+1, it performs really well.

I've been looking, but I have not been able to find any information on what the feasible maximum distance into the future N-Grams are capable of.

One way I could do this is to predict the next character (n+1) and then use that result to predict n+2, use n+2 to predict n+3... etc... but obviously an incorrect prediction will cascade and impact the future predictions as there is a dependency.

Is there a better (more accurate, reliable) way to do this? Also, I can easily use N-Grams to predict the next (n+1) character in an incoming stream, but what about n+2? How about n+3? When does it become unrealistic to predict further?

I'm sure there is some research on this, so I'd love it if you could link me to a paper or give me a brief overview to get me going.

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There's no simple answer to "how far into the future can you predict?". Obviously, the further you go, the less your ability to predict. For English text, I would expect that ability to predict would decrease rapidly.

You asked if there's a better wya to do this. You can of course consider a generalization of $n$-grams where you use the characters at positions $i-n+1,i-n+2,\dots,i-1,i$ to predict the character at position $i+k$ (instead of predicting the character at position $i+1$), for any fixed $k$. This will probably perform worse the larger $k$ is.

You might also be interested in skipgrams.

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  • $\begingroup$ Great, thanks. I had thought about doing what skip grams does, but I had no idea what to call! $\endgroup$ – pookie May 20 '17 at 23:34

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