In the learning phase, how does the system recognize that there is an anomaly, so that it is not being trained?

The method used in this specific situation is called "unsupervised learning". Ingoing data is assumed to be correct and patterns are searched which deviate from the "common". Therefore, if a peak does not occur again in the course of time, it is recognized as anomaly and corrected automatically.

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