I have a group of lists detailing temperatures over differing amounts of time. My goal is to use machine learning to identify periods in which a machine is turned on and off, where turning on the machine drastically increases temperature, and turning off the machine returns it to idle temperature.
The rub is that there is a level of temperature inertia- take the following example.
Among my control data is the following list (truncated for simplicity's sake):
At minute intervals, the temperature starts at 73 degrees. It is turned off after 12 minutes, when the temperature is 83 degrees. However, the peak is reached 3 minutes after the stopping point, at 86 degrees.
Given a control group of a list with labelled starts, ends, and peaks, how would I go about using supervised learning to create an algorithm that could predict stops using a list with only starts and peaks?