Deep Learning Anomaly Detectors
Creation and workflow for deep learning anomaly detectors
Deep learning anomaly detectors are based on the multilayer deep learning networks provided in Deep Learning Toolbox™, such as autoencoders.
Deep learning algorithms provide an alternative approach to detecting anomalies. They are generally more computationally intensive, and therefore, slower to train.
Apps
| Time Series Anomaly Detector | Interactively create, train, test, and tune detectors for detecting anomalous behavior in time series (Since R2026a) |
Functions
Topics
- Detecting Anomalies in Time Series
Examine the general workflow for developing anomaly detectors that detect anomalous subsequences in time series.
- Train and Test TCN Anomaly Detector
Load the file
sineWaveAnomalyData.mat, which contains two sets of synthetic three-channel sinusoidal signals. - Interpret Evaluation Metrics for Time Series Anomaly Detectors
Interpret evaluation metrics that are returned by the
evaluationMetricsfunction and the app.