After I managed to build a project doing data classification, I would like to ask for advise on how to build a project doing "unsupervised anomaly detection"
I would appreciate a "pointer" to the right model to use, or tutorial on this topic - as a hint.
My problem... (with some simplifications):
I have a temperature sensor, reporting the data (temperature) every minute, for a length of 30 days - my "training data".
I have no idea whether in the history I view, there was any anomaly ("issue") related to the temperature, or when - just the data itself. So, the classification models aren't relevant, at least to my newbie level of understanding...
Then, I have a data for the temperature of the last one hour, reported by a minute.
My goal is to apply a reasonable heuristics, telling me the probability of that "hour" to represent an "anomaly"
, compared to the training data. Right now, I have some freedom
to define "anomaly", but it should reflect real world scenarios
like "too high", "too low", "too volatile", "too steady".
At the 2nd stage, I will need to analyze the information based on the days of week (assuming the temperature changes reflect some weekly "trends").
Thanks for any hint,