I have a temperature dataset (data every 15 mins) to build a supervised classification/prediction algorithm, but only know one of the true classes (when data is nearly flatlining around 35deg)
However, given the academic literature on the subject and the data visualization it looks like there are three "true" classes: regulated, sub-ideal regulation, and no regulation. Here is a graph with vertical lines delineating 3 potential categories. What is the best practice to build this classification? First attempt binned data every 6hrs and built categories based off stdev, but that seemed a bit arbitrary and did not align well with the three hypothesized categories.