We present a system for supervision of technical processes, called COMETH, which involves an active learning approach. The system is able to identify anomalies with very little training data, through an efficient feedback process. COMETH has been successfully applied in the context of heating ventilation and air conditioning systems and in industrial machinery. Here, we describe the idea of combining the time series analysis COMETH with large language models to integrate further context information and thus provide the user with specific recommendations
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