I have eye-tracking data on two subjects -- a teacher, and a student. It's in the form (x, y, time), so there is a series of these for each subject. What the teacher looks at influences what the student looks at. What method would I use to predict what the student is looking at, using only teacher data? Lets say I can train some learning algorithm using a gold standard set of student and teacher data.I was thinking hidden markov model would be appropriate, given the definition in Wikipedia, but I am not sure how to put this into practice over my dataset.More detail: I have data about how a teacher and student each look at a map and some readings. I have 40 of these datasets, which look like [(366,234,0), (386,234,5), ...] which means the teacher looked at point (366,234) at time 0 and then 5 seconds later moved up to look at coordinate (386, 234). I can to learn a model to understand the relationship between how a teacher looks at content, to predict how a student will look at the same content. So maybe the student looks at the content in the same order as the teacher but slower. Or perhaps the student doesn't look around as much but the teacher scans more of the content. I have both sets of data and want to see how accurate of a model I can get -- would I be able to predict the student's looking behavior within 50px of the teacher's looking behavior?
What learning algorithm is appropriate for predicting one time-series from another?
machine learning