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.