Re:Sktime: A Unified Python Toolbox for Machine Learning wit...
by kmundnic on Monday September 21, 2020 @12:44PM
(#24545521)
I knew about work in anomaly detection for state of health and state of charge in batteries, where you can somewhat model the physical effects (in a data driven manner). However, this description of the problem made me think that meta-learning might be suitable for the problem you’re describing. I’ve only seen it applied in computer vision though (and more recently in speech).
It can be a time series classification if and only if you have labels that say a given sample is an anomaly. But then what if it runs for a while and a new kind of anomaly starts happening that it’s never seen before and isn’t in the training set: will it detect it based on your labels?
Ideally anomaly detection should be a more unsupervised learning scenario where it can automatically determine what’s normal and what’s not.