Published April 10, 2024
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Anomaly detection using 1D-CNN
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Description
Implementation of a two-step method for anomaly detection using deep 1D-CNN architectures: Implementation of a two-step method for anomaly detection using deep 1D-CNN architectures:
1) Learning step : use pretext tasks to learn a representation of the data in a self-supervised way
2) Anomaly detection step : raise an anomaly each time the data reconstruction score is greater than a given threshold
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- Functional maturity
- Technological maturity
Trustworthy Attributes
Reliability
Engineering roles
Data Engineer
Use cases
Time series
Functional Set
Data Life cycle