기타 이상 감지기

기타 이상 감지기의 생성 및 워크플로

통계적 공정관리 감지기는 Statistics and Machine Learning Toolbox™에서 제공하는 산업용 제어 기술을 적용하고 있습니다. 이 감지 방식은 모델을 사용하는 대신 통계 데이터를 활용하여 이상값을 식별합니다.

머신러닝 모델과 마찬가지로, 통계적 공정관리 감지기는 훈련 시간이 짧으며, 감지기를 개발하는 데 있어 훌륭한 출발점이 됩니다.

앱

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

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

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

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

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.