Creating categorical variable, without knowing true categories (through binning time series data)? 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.
So, if I understood you correctly, you want to classify your data based on the frequency and amplitude of the temperature change, right? 

I think your attempt with stdev is not so far away, but your interval is a little off, 6 hours might be to much. Think about Shannon, your sample rate must be at least twice the highest frequency in your dataset, if you want to reconstruct your signal completely. If your sample rate is 6 hours you might miss the big leaps at the end of the image.

My recommendation would be to use a sliding window and use Fast Fourier Transformation to get to the frequency spectrum. With those datapoints you can try to cluster them with SVM or kNN approaches. I think SVM is the way to go, since your class should be cleanly seperateble. And since you know how many classes you have there is no need for hypertuning this parameter.

I certainly don't know if this is best practice, but I think this would be a feasible approach.