# Data prediction using scikit-learn and a list

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?

• Interesting problem. Is there a way to calculate the "theoretical" temperature at a certain point using physics laws when the machine is on? – Erwan Jan 26 at 23:44
• I've created a derivative function that creates a polynomial derivative to the 20th degree in between the start and peak locations. This offers a fairly precise estimation of what the active temperature is at any given time frame. I'm not sure what laws could act as constraints in this environment, but I looked to see if the maximum slope between two data points was constant, but unfortunately it varies by a significant amount. – iwillc123 Jan 27 at 1:47