I have a dataset for a lot of subjects (current testing dataset around 3000 subject, actual number is a lot bigger >40000).
Each subject has 13 variables. The data was measured once per year for 11-13 years (depends on the subject).
What machine learning technique should I use, to be able to forecast these variables for subjects, that have fewer years of observations (or are younger and we want to predict). So lets say, I have 12 years of observations and would like to model these variables for the 13th year, similar for 11 observations, I would like to model for 12th and 13th year.
The years are consecutive.
The data consists of simple physical traits, as in weight, age, sex(which appears in every row of data), height and fitness scores (time of 60m sprint, 600m sprint, etc).
I was thinking of somehow putting these variables into a neural net and train for every combination (11years of data, train for 12th and 13th separately, etc). But this is somewhat crude, time consuming and bad in general.
What is a better solution to this problem, using machine learning?