Given a lengthy sequence of integers in the range of 0-1 I would like to be able to predict the next likely integer.
Example dataset:
1 1 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0
A quick look at the above perhaps shows some obvious patterns which may be recognised by an ML model.
I do have other features available in the dataset but I don't think they correlate to the integer result so the prediction should be based purely on the statistical relevance of the supplied integer dataset.
I'm unsure how to approach this using ML.NET. I have successfully classified models previously but those predictions are all made based on multiple features. In this case if I just supply a 0 or 1 there's no relevant historical sequence to aid the prediction.
How do I train an ML.NET model to return a prediction based on a range of previous data?
Working theory: the above dataset has 100 integers. I could create a class which has 100 properties (Integer0..Integer99) and painstakingly map each field and submit that but it seems really clunky.