Creating categorical variable, without knowing true categories (through binning time series data)?
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.