I want to perform an analysis on timeseries data which is measured per second.
To give some context, it is a data of temperature of air at the exit of air heater. Fluctuations in data are either due to switching on and off around setpoint or partial switching of (1 of 2 coils) heater. Sustained low temperatures observed are due to heater being switched-off for considerable amount of time.
Sample data of temperature is as represented in following images:-
As per my understanding, prediction of any such data at particular time instant depends on past observations as well as on other factors (e.g. power to heater, mass flow rate of air etc.).
Most of timeseries analysis examples that I read on web were for data with higher timeperiod per observations (week, month, year). These gives to my mind some questions:-
- Is timeseries analysis a suitable method for prediction of my Temperature data? If not then why?
- If no to first question, then what predictive method can be used for such data (apart from first principle modeling)?
- How can the effect of certain background parameters (e.g. Mass flow rate of air, heater power) that are fixed during one observed series be included in overall analysis. For e.g. a certain Temperature profile is observed for 10,000 seconds for 1,000 W heater and 200 CFM and another profile for 10,000 seconds for 1,200 W heater and 220 CFM. Now each series can be analyzed on it own (maybe?). But how to include effect of start parameter (e.g. heater power) on from scratch prediction of new series?