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I am an experienced programmer but very new to machine learning.

I have a data set that consists of about 50,000 sets of 2,000 ordered values. All of the values are floats normalised to between 0 and 1. The sets of data are analagous to DNA sequences, where there are common patterns between them but each set is a self-contained, discrete set of data.

I am looking for a NN that will be able to predict the remaining 1,900 values in a set when given the first 100.

It appears that an LSTM RNN will be the best model to learn the data - am I correct in thinking this? If I am correct, what would be the best way to process the data? (inputs/layers/nodes/outputs)
The LSTM models I have seen (such as text prediction) seem a little too open-ended for discrete sets of data such as this.

Apologies if this question is a little broad but info on NN on the net is still a little sparse and I need a jumping off point.

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I am unable to add a comment so I’ll just write my take; you have sequential data and are trying to infer the remaining sequence given a preceding part of the sequence. This problem can be modeled by a Bayesian Network; check out pomegranate if you are using python, I believe it’s one of the few packages that lets you build easy probability models. For example if you have a sequential data like [[A,B,C,D],[A,B,D,F], etc]](in your case shaped 50000 sequences of length 2000) a Bayesian Network can infer missing values like so [[A,B,None, None]]. The network will infer the missing values. I hope this is what you were looking for.

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  • $\begingroup$ hmmm....not heard of that one - I will have to look into it. Thanks for your input $\endgroup$ – chips Sep 13 '19 at 9:15

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