I have a signal which measures the power of a machine. I have been asked to fit an ARIMA model for this signal in order to find anomalies.
However as far as I know, the power of the machine is controlled by several factors, like: human control (i.e. an operator can increase, decrease, turn off, ... the power as he needs of as he is requested), automatic control (i.e. when for instance the internal temperature of the machine is too high it limits itself power, or if the power is not used for a given time the machine automatically turns off, ...), external control (i.e. some day it rains and the machine performs badly, ...), and of course some randomness.
My question is: from a methodological point of view, does it even make sense to fit an ARIMA model of this signal (and of this signal ONLY) without considering the context i.e. what the operator does, what the machine does, external factors? Maybe the power of the machine is "10" in the last six months, now it becomes "100" just because someone has decided so, how can I know if this is an anomaly or an intended behavior? How can the ARIMA model fit without these information? Or is the ARIMA model "strong enough" to handle all these "hidden" contexts?