Functional Anomaly Detection: a Benchmark Study. (arXiv:2201.05115v1 [stat.ML])

The increasing automation in many areas of the Industry expressly demands to
design efficient machine-learning solutions for the detection of abnormal
events. With the ubiquitous deployment of sensors monitoring nearly
continuously the health of complex infrastructures, anomaly detection can now
rely on measurements sampled at a very high frequency, providing a very rich
representation of the phenomenon under surveillance. In order to exploit fully
the information thus collected, the observations cannot be treated as
multivariate data anymore and a functional …

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