"Newbie: help with unsupervised anomaly detection with RapidMiner"

max001max001 Member Posts: 2 Contributor I
edited June 2019 in Help

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,


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