Additional Anomaly Detectors

Creation and workflow for additional anomaly detectors

The Statistical Process Control Detector incorporates industrial control techniques provided in Statistics and Machine Learning Toolbox™. Rather than using models, this detection approach uses statistical data to identify outliers.

Like machine learning models, Statistical process control detectors are quick to train, and provide a good starting point for developing a detector.

Apps

Time Series Anomaly Detector

Interactively create, train, test, and tune detectors for detecting anomalous behavior in time series (Since R2026a)

Functions

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Create Statistical Process Control Detector

`timeSeriesSpcAD`	Create an anomaly detector that applies statistical process control techniques to time series data (Since R2026a)
`TimeSeriesSPCDetector`	Detect subsequence anomalies in time series using statistical process control (Since R2026a)

Train and Test Sequence for Statistical Process Control Detector

`train`	Train statistical process control (SPC) anomaly detector and obtain detection threshold (Since R2025a)
`detect`	Detect anomalies in time series using a trained time statistical process control (SPC) detector model (Since R2026a)
`updateDetector`	Update settings of a trained statistical process control anomaly detector (Since R2026a)

Visualize and Evaluate Detector Performance

`plotHistogram`	Plot histogram of anomaly scores and detection threshold for statistical process control (SPC) anomaly detector (Since R2025a)
`plot`	Plot detected anomalies and anomaly scores generated from time series anomaly detectors that are based on statistical process control (Since R2026a)
`timeSeriesAnomalyMetrics`	Compute specialized evaluation metrics for time series anomaly detection (Since R2026a)

Topics

Train and Test Time Series Statistical Process Control Anomaly Detector
This example shows the development cycle for a Statistical Process Control anomaly detector.
Detecting Anomalies in Time Series
Examine the general workflow for developing anomaly detectors that detect anomalous subsequences in time series.
Interpret Evaluation Metrics for Time Series Anomaly Detectors
Interpret evaluation metrics that are returned by the evaluationMetrics function and the app.