Anomaly Detection on Time-Series Data
The detection of anomalies in time series data is a use case that occurs in a variety of scenarios, e.g. in automotive production, in the manufacture of chemical products such as battery cells through to cosmetic products as well as in measurement and monitoring technology. Wherever there is sensor data with a time reference, there are anomalies.
Anomalies are everywhere
Whereas in the past it was difficult to detect anomalies in series of measured values or patterns, modern methods such as autoencoder or self-attention networks can now be used to detect anomalies.
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