The answer seems to be 'statistics' but I assume that's about as useful as saying a person interested in gravity should study 'physics'.
I anticipate a large number of data points to be delivered to me from a sensor. For purposes of discussion, say it's the temperature at the top of a tower, every second. That is, I'll have one sensor returning values every second. I know the possible range of the values, and I'll know when they were taken.
I want to know if a particular value is unusual. I want to know with some certainty that since time X, the values are trending up or down. I want to know if the sensor is misbehaving. For example, if our temperature sensor developed a bad ground and Murphy paid a visit, it could be the temperature would seem very stable. The average could be about right, and nope, not trending at all. Different analysis could point out that the nature of the data has changed, or that it is indeed just noise.
So of course this is statistics but is there a specific type I should care about?
EDIT - another reasonable use case is to notice cyclic behavior. It would probably be pretty normal for network traffic or memory usage to increase starting at about 8am 5 days a week. We would not be concerned by this. But what if one Monday, it didn't? What if another cycle was noticed, say, one that peaked on the 3rd Saturday of each month? Analysis of cyclic behavior says FFT to me, but after that I am lost.
(This may sound like a climate change question. It isn't. The fact is I am looking for a general solution. I am not even sure what the data will be. I'll ultimately have hundreds of sensors reporting to me... everything from memory usage on computers to the timestamps in which a security door is opened...)